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Publisher: Elsevier   (Total: 3157 journals)

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Showing 1 - 200 of 3161 Journals sorted alphabetically
A Practical Logic of Cognitive Systems     Full-text available via subscription   (Followers: 9)
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 35, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 24, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 96, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 27, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 37, SJR: 1.771, CiteScore: 3)
Achievements in the Life Sciences     Open Access   (Followers: 5)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 7)
Acta Astronautica     Hybrid Journal   (Followers: 415, SJR: 0.758, CiteScore: 2)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 2)
Acta Biomaterialia     Hybrid Journal   (Followers: 27, SJR: 1.967, CiteScore: 7)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription   (Followers: 2)
Acta de Investigación Psicológica     Open Access   (Followers: 3)
Acta Ecologica Sinica     Open Access   (Followers: 10, SJR: 0.18, CiteScore: 1)
Acta Haematologica Polonica     Free   (Followers: 1, SJR: 0.128, CiteScore: 0)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.661, CiteScore: 2)
Acta Materialia     Hybrid Journal   (Followers: 260, SJR: 3.263, CiteScore: 6)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.504, CiteScore: 1)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.542, CiteScore: 1)
Acta Oecologica     Hybrid Journal   (Followers: 12, SJR: 0.834, CiteScore: 2)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription  
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 3, SJR: 0.307, CiteScore: 0)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 1, SJR: 1.793, CiteScore: 6)
Acta Poética     Open Access   (Followers: 4, SJR: 0.101, CiteScore: 0)
Acta Psychologica     Hybrid Journal   (Followers: 27, SJR: 1.331, CiteScore: 2)
Acta Sociológica     Open Access   (Followers: 1)
Acta Tropica     Hybrid Journal   (Followers: 6, SJR: 1.052, CiteScore: 2)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 3, SJR: 0.374, CiteScore: 1)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 2)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.344, CiteScore: 1)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 1)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 6, SJR: 0.19, CiteScore: 0)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 3)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 7)
Acute Pain     Full-text available via subscription   (Followers: 14, SJR: 2.671, CiteScore: 5)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.53, CiteScore: 4)
Addictive Behaviors     Hybrid Journal   (Followers: 17, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 11, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 23)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 159, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 12, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 17, SJR: 0.694, CiteScore: 3)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.277, CiteScore: 1)
Advances in Agronomy     Full-text available via subscription   (Followers: 15, SJR: 2.384, CiteScore: 5)
Advances in Anesthesia     Full-text available via subscription   (Followers: 28, SJR: 0.126, CiteScore: 0)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 2)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 10, SJR: 0.992, CiteScore: 1)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 11, SJR: 1.551, CiteScore: 4)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 24, SJR: 2.089, CiteScore: 5)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 14, SJR: 0.572, CiteScore: 2)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.61, CiteScore: 7)
Advances in Botanical Research     Full-text available via subscription   (Followers: 2, SJR: 0.686, CiteScore: 2)
Advances in Cancer Research     Full-text available via subscription   (Followers: 33, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 9, SJR: 1.453, CiteScore: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 1.992, CiteScore: 5)
Advances in Cell Aging and Gerontology     Full-text available via subscription   (Followers: 4)
Advances in Cellular and Molecular Biology of Membranes and Organelles     Full-text available via subscription   (Followers: 12)
Advances in Chemical Engineering     Full-text available via subscription   (Followers: 27, SJR: 0.156, CiteScore: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.713, CiteScore: 1)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 10, SJR: 1.316, CiteScore: 2)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 26, SJR: 1.562, CiteScore: 3)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 19, SJR: 1.977, CiteScore: 8)
Advances in Computers     Full-text available via subscription   (Followers: 14, SJR: 0.205, CiteScore: 1)
Advances in Dermatology     Full-text available via subscription   (Followers: 15)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 12)
Advances in Digestive Medicine     Open Access   (Followers: 9)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 25)
Advances in Ecological Research     Full-text available via subscription   (Followers: 44, SJR: 2.524, CiteScore: 4)
Advances in Engineering Software     Hybrid Journal   (Followers: 28, SJR: 1.159, CiteScore: 4)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 8)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 46, SJR: 5.39, CiteScore: 8)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 1)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 9)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 59, SJR: 0.591, CiteScore: 2)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
Advances in Genetics     Full-text available via subscription   (Followers: 16, SJR: 1.354, CiteScore: 4)
Advances in Genome Biology     Full-text available via subscription   (Followers: 8, SJR: 12.74, CiteScore: 13)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 1.193, CiteScore: 3)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 24, SJR: 0.368, CiteScore: 1)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 12, SJR: 0.749, CiteScore: 3)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.193, CiteScore: 0)
Advances in Immunology     Full-text available via subscription   (Followers: 36, SJR: 4.433, CiteScore: 6)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 1.163, CiteScore: 2)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 2, SJR: 1.938, CiteScore: 3)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 6, SJR: 0.176, CiteScore: 0)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 3)
Advances in Life Course Research     Hybrid Journal   (Followers: 8, SJR: 0.682, CiteScore: 2)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Marine Biology     Full-text available via subscription   (Followers: 18, SJR: 0.88, CiteScore: 2)
Advances in Mathematics     Full-text available via subscription   (Followers: 11, SJR: 3.027, CiteScore: 2)
Advances in Medical Sciences     Hybrid Journal   (Followers: 7, SJR: 0.694, CiteScore: 2)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.158, CiteScore: 3)
Advances in Molecular and Cell Biology     Full-text available via subscription   (Followers: 23)
Advances in Molecular and Cellular Endocrinology     Full-text available via subscription   (Followers: 8)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 7, SJR: 0.182, CiteScore: 0)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 2)
Advances in Organ Biology     Full-text available via subscription   (Followers: 1)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 17, SJR: 1.875, CiteScore: 4)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.174, CiteScore: 0)
Advances in Parasitology     Full-text available via subscription   (Followers: 5, SJR: 1.579, CiteScore: 4)
Advances in Pediatrics     Full-text available via subscription   (Followers: 24, SJR: 0.461, CiteScore: 1)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 12)
Advances in Pharmacology     Full-text available via subscription   (Followers: 16, SJR: 1.536, CiteScore: 3)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.574, CiteScore: 1)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.109, CiteScore: 1)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 10)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 19)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 20, SJR: 0.791, CiteScore: 2)
Advances in Psychology     Full-text available via subscription   (Followers: 64)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 6, SJR: 0.371, CiteScore: 1)
Advances in Radiation Oncology     Open Access   (SJR: 0.263, CiteScore: 1)
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 3, SJR: 0.101, CiteScore: 0)
Advances in Space Biology and Medicine     Full-text available via subscription   (Followers: 6)
Advances in Space Research     Full-text available via subscription   (Followers: 404, SJR: 0.569, CiteScore: 2)
Advances in Structural Biology     Full-text available via subscription   (Followers: 5)
Advances in Surgery     Full-text available via subscription   (Followers: 11, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 34, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 18)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 14)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 2.262, CiteScore: 5)
Advances in Water Resources     Hybrid Journal   (Followers: 47, SJR: 1.551, CiteScore: 3)
Aeolian Research     Hybrid Journal   (Followers: 6, SJR: 1.117, CiteScore: 3)
Aerospace Science and Technology     Hybrid Journal   (Followers: 350, SJR: 0.796, CiteScore: 3)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.42, CiteScore: 2)
African J. of Emergency Medicine     Open Access   (Followers: 6, SJR: 0.296, CiteScore: 0)
Ageing Research Reviews     Hybrid Journal   (Followers: 11, SJR: 3.671, CiteScore: 9)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 457, SJR: 1.238, CiteScore: 3)
Agri Gene     Hybrid Journal   (Followers: 1, SJR: 0.13, CiteScore: 0)
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 17, SJR: 1.818, CiteScore: 5)
Agricultural Systems     Hybrid Journal   (Followers: 31, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 42, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 4)
Agriculture and Natural Resources     Open Access   (Followers: 3)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 57, SJR: 1.747, CiteScore: 4)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.589, CiteScore: 3)
Air Medical J.     Hybrid Journal   (Followers: 6, SJR: 0.26, CiteScore: 0)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.19, CiteScore: 0)
Alcohol     Hybrid Journal   (Followers: 12, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 11)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.604, CiteScore: 3)
Alexandria J. of Medicine     Open Access   (Followers: 1, SJR: 0.191, CiteScore: 1)
Algal Research     Partially Free   (Followers: 10, SJR: 1.142, CiteScore: 4)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 2)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.504, CiteScore: 1)
Allergology Intl.     Open Access   (Followers: 5, SJR: 1.148, CiteScore: 2)
Alpha Omegan     Full-text available via subscription   (SJR: 3.521, CiteScore: 6)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 10, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 51, SJR: 4.66, CiteScore: 10)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 4, SJR: 1.796, CiteScore: 4)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 4, SJR: 1.108, CiteScore: 3)
Ambulatory Pediatrics     Hybrid Journal   (Followers: 6)
American Heart J.     Hybrid Journal   (Followers: 53, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 56, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 44, SJR: 0.604, CiteScore: 1)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 10)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.524, CiteScore: 3)
American J. of Human Genetics     Hybrid Journal   (Followers: 34, SJR: 7.45, CiteScore: 8)
American J. of Infection Control     Hybrid Journal   (Followers: 29, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 35, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 47)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3, SJR: 1.967, CiteScore: 2)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 222, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 66, SJR: 3.184, CiteScore: 4)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 5, SJR: 0.265, CiteScore: 0)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.289, CiteScore: 1)
American J. of Otolaryngology     Hybrid Journal   (Followers: 25, SJR: 0.59, CiteScore: 1)
American J. of Pathology     Hybrid Journal   (Followers: 28, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 29, SJR: 2.164, CiteScore: 4)
American J. of Surgery     Hybrid Journal   (Followers: 38, SJR: 1.141, CiteScore: 2)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 12, SJR: 0.767, CiteScore: 1)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 7)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 63, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 18, SJR: 0.411, CiteScore: 1)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 3, SJR: 0.277, CiteScore: 0)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription  
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 5, SJR: 4.849, CiteScore: 10)
Analytica Chimica Acta     Hybrid Journal   (Followers: 42, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 184, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 12, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 12)
Anesthésie & Réanimation     Full-text available via subscription   (Followers: 2)
Anesthesiology Clinics     Full-text available via subscription   (Followers: 23, SJR: 0.683, CiteScore: 2)
Angiología     Full-text available via subscription   (SJR: 0.121, CiteScore: 0)
Angiologia e Cirurgia Vascular     Open Access   (Followers: 1, SJR: 0.111, CiteScore: 0)
Animal Behaviour     Hybrid Journal   (Followers: 203, SJR: 1.58, CiteScore: 3)

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Journal Cover
Accident Analysis & Prevention
Journal Prestige (SJR): 1.462
Citation Impact (citeScore): 3
Number of Followers: 96  
  Partially Free Journal Partially Free Journal
ISSN (Print) 0001-4575
Published by Elsevier Homepage  [3157 journals]
  • Effects of scheduled manual driving on drowsiness and response to take
           over request: A simulator study towards understanding drivers in automated
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Yanbin Wu, Ken Kihara, Yuji Takeda, Toshihisa Sato, Motoyuki Akamatsu, Satoshi Kitazaki Because current automated vehicles have operational limitations, it is important to ensure that the fallback-ready driver is able to perform appropriately when required to take over control of the vehicle. However, time-related increase in driver drowsiness is well-known, and drowsy driving can affect response to take-over request (TOR). It was previously reported that a scheduled period of manual driving during automated driving was beneficial in maintaining driver arousal level. The present driving simulator study investigates the effects of scheduled manual driving on driver drowsiness and performance, as well as age differences therein. A total of 115 participants, whose gender was balanced and age was distributed uniformly from 20 to 70 years, drove an automated vehicle for 31 min, and a TOR was prompted before a collision event. A between-subjects design comprised two conditions: with versus without a scheduled 10-min interval of manual driving that ended 10 min before TOR. The Karolinska Sleepiness Scale and eyeblink durations estimated from electrooculograms (EOG) were used to subjectively and objectively measure participant’s drowsiness. Reaction time, standard deviation of steering wheel angle, and minimum Time-to-Collison (TTC) were extracted to measure driver performance in response to TOR. The alleviating effect on drowsiness of 10-min scheduled manual driving became non-significant after another 10-min period of automated driving. Although the scheduled manual driving had no significant effect for younger drivers, older drivers reacted significantly more slowly in both steering and braking at the critical event. These findings provide essential insights for human-vehicle interactions: Scheduled manual driving cannot maintain drivers’ arousal level for 10 min afterwards, and for older drivers, it would be better to avoid unnecessary task-switching between manual and automated driving.
  • Functional forms of the negative binomial models in safety performance
           functions for rural two-lane intersections
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Kai Wang, Shanshan Zhao, Eric Jackson Safety Performance Functions (SPFs) play a prominent role in estimating intersection crashes, and identifying the sites with the highest potential for safety improvement. To maximize the crash prediction accuracy, this paper describes the application of different functional forms of the Negative Binomial (NB) models (i.e. NB-1, NB-2 and NB-P) in estimating safety performance functions by crash type for three types of rural two-lane intersections, including three-leg stop-controlled (3ST) intersections, four-leg stop-controlled (4ST) intersections and four-leg signalized (4SG) intersections. Crash types were aggregated into same-direction, opposite-direction, intersecting-direction and single-vehicle crashes. Major and minor road Annual Average Daily Traffic (AADT) were used as predictors in the SPF estimation. In addition, major and minor road AADT were also used as predictors in the estimation of the over-dispersion parameter of the NB models to account for the crash data heterogeneity. In the end, all NB models were compared based on both the model estimation goodness-of-fit and the prediction performance.The model goodness-of-fit indicates that the NB-P model outperforms the NB-1 and NB-2 models for most crash types and intersection types, by providing a flexible variance structure to the NB approaches. The parameterization of the over-dispersion factor verifies that the over-dispersion parameter of the NB models highly depends on how the variance structure is defined in the model, and the over-dispersion parameter is shown to vary among different intersections for each crash type and can be estimated using both the major and minor road AADT at rural two-lane intersections. The NB-P model is found to more effectively capture the variation of over-dispersion among intersections in NB models, which benefits the accommodation of data heterogeneity in intersection SPF development. The prediction performance comparison illustrates that the NB-P model slightly improves the crash prediction accuracy compared with the other two models, especially for the 3ST and 4SG intersections. In conclusion, the NB-P model with parameterized over-dispersion factor is recommended to provide more unbiased parameter estimates when estimating SPFs by crash type for rural two-lane intersections.
  • Expressway crash risk prediction using back propagation neural network: A
           brief investigation on safety resilience
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Junhua Wang, Yumeng Kong, Ting Fu This study presents the work in predicting crash risk on expressways with consideration of both the impact of safety critical events and traffic conditions. The traffic resilience theory is introduced to learn safety problems from the standpoint of 1) considering safety critical events, such as traffic violations, as the safety disturbances, and 2) considering safety resilience as the ability of the traffic, greatly associated with traffic conditions, to resist critical events turning into crashes. The concept of safety resilience was illustrated qualitatively through simulation experiments. Aimsun microsimulation software was used to simulate traffic conditions with safety critical events (vehicle violations, in this paper) involved based on the geometric design of the G15 Expressway in Shanghai. Based on data from the simulation experiment, a two-staged model was developed which classifies crash risk status into three types including no-risk, low-risk and high-risk status. Modeling approach that relies on the back propagation neural network method was applied. The performance of the model in prediction was validated through the Receiver Operating Characteristic (ROC) curve test. Results indicated that the model performed well in predicting crash risks in the simulated environment. After training the model, an extra simulation experiment involving six additional tests was conducted. Results show that the traffic resilience theory may work in explaining the relationship between traffic conditions, safety critical events and crash risk, which are the key elements in road safety field. The introduction of safety resilience may inspire further exploration on this topic in both research and practice. Meanwhile, the model can be used to predict and monitor risks on expressways in a potentially more precise way.
  • Sex differences in the association between impulsivity and driving under
           the influence of alcohol in young adults: The specific role of sensation
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Juan F. Navas, Cristina Martín-Pérez, Dafina Petrova, Antonio Verdejo-García, Marta Cano, Omar Sagripanti-Mazuquín, Ana Perandrés-Gómez, Ángela López-Martín, Sergio Cordovilla-Guardia, Alberto Megías, José C. Perales, Raquel Vilar-López IntroductionThere is an outstanding need to identify predictors of driving under the influence of alcohol (DUI) among young adults, particularly women. Impulsivity, or the tendency to act without thinking, is a predictor of DUI, but the specific facets of impulsivity that predict DUI and their interaction with sex differences remain unclear. We aimed to investigate sex differences in the link between impulsivity facets and DUI. Moreover, we sought to replicate previous findings regarding sex differences on impulsivity, and associations between impulsivity facets and DUI.MethodA total of 506 university students participated in the study (males, n = 128; females, n = 378). Participants completed measures of impulsivity (UPPS-P short version), alcohol use (AUDIT-C), frequency of DUI episodes and related perception of risk. The UPPS-P assesses five facets of impulsivity: sensation seeking, (lack of) premeditation and perseverance and positive and negative urgency.ResultsMen showed higher sensation seeking and lack of perseverance, alcohol use and DUI frequency and lower risk perception than women. DUI frequency was negatively associated with perception of risk and positively associated with alcohol use and the five impulsivity facets. After controlling for alcohol use and risk perception, only lack of premeditation was associated with DUI frequency in the whole sample. Sensation seeking was positively associated with DUI frequency only in women.DiscussionThe link between lack of premeditation and DUI suggest that pre-drinking planning strategies can contribute to prevent risky driving. In women, specific links between sensation seeking and DUI suggest the need for personality-tailored prevention strategies.
  • Pedestrian's risk-based negotiation model for self-driving vehicles to get
           the right of way
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Surabhi Gupta, Maria Vasardani, Bharat Lohani, Stephan Winter Negotiations among drivers and pedestrians are common on roads, but it is still challenging for a self-driving vehicle to negotiate for its right of way with other human road users, especially pedestrians. Currently, the self-driving vehicles are programmed for conservative behavior, yielding to approaching pedestrians. Consequently, the future urban traffic will slow down significantly. In this paper, a conceptual model of vehicle–pedestrian negotiation is proposed. This model allows individual decision making of multiple vehicles and pedestrians, extending a prior negotiation model for a single vehicle and a single pedestrian. The possible negotiation opportunities for vehicles are modeled considering different risk-taking behaviors of pedestrians. Simulation results show an overall improvement in the waiting time of vehicles and thus in the intersection throughput, compared to conservative vehicle behavior. The simulation results show also that the benefit of reduced waiting times for vehicles comes at the cost of some waiting time for pedestrians. However, the observed pedestrian waiting times in this model are not more than the generally accepted waiting times reported in empirical studies.
  • The effects of warning characteristics on driver behavior in connected
           vehicles systems with missed warnings
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Yiqi Zhang, Changxu Wu, Chunming Qiao, Yunfei Hou With emerging new technologies, the vehicles in the future with connected vehicle systems (CVS) will be equipped with the ability to communicate with each other and aim to provide drivers with information in a timely and reliable way to improve driver safety. This study was designed to investigate the interaction effects of warning lead time (2.5 s vs. 4.5 s), warning reliability (73% vs. 89%), and speech warning style (command vs. notification) on driver performance and subjective evaluation of warnings in CVS. A driving simulator study with thirty-two participants was designed to simulate a connected vehicle environment with missed warnings due to failures in the communication network of the CVS. With regard to the response types, the results showed that notification warnings led to a lower probability of braking response and a higher probability of braking and steering response compared with command warnings. The results showed command warnings led to a smaller collision rate compared to notification warnings with the warning lead time of 2.5 s, whereas no significant difference of collision rates was found between two warning styles when the warning lead time is 4.5 s. These results suggest notification warnings should be selected when the warning lead time is longer and the warning systems are highly reliable, which resulted in higher safety benefits and higher subjective rating. Command warnings could be selected when the warning lead time is shorter since they led to more safety benefits, but such selection has to be made with caution since command warnings may limit drivers’ response type and were perceived as less helpful than notification warnings.
  • Analysis of cut-in behavior based on naturalistic driving data
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Xuesong Wang, Minming Yang, David Hurwitz Cut-in maneuvers, when vehicles change lane and move closely in front of a vehicle in the adjacent lane, are very common but adversely affect roadway capacity and traffic safety. Yet little research has comprehensively explored cut-in behavior, particularly in China, which has a challenging driving environment and is often used for connected and autonomous vehicle testing. This study developed an extraction algorithm to retrieve 5608 cut-in events from the Shanghai Naturalistic Driving Study. The data were used to identify cut-in characteristics, including motivation, turn signal usage, duration, urgency, and impact. Results showed that almost half of drivers did not use a turn signal when cutting in, and that cut-ins had a shorter time to collision (TTC) than other lane changing. A lognormal distribution was found to produce the best fit for cut-in duration, which varied from 0.7 s to 12.4 s. As characteristics were found to vary by roadway type and motivation, multilevel mixed-effects linear models were developed to examine the influencing factors of cut-in gap acceptance. Acceptance of lead and lag gaps was significantly affected by environmental variables, vehicle type, and kinematic parameters, which has important implications for microsimulation, as does the large variance in duration that makes specifying duration essential when setting scenarios. Improvement in safety education is warranted by the high degrees of risk and aggression shown by TTC and turn signal usage; but the ability of drivers, who needed to yield to the cut-in, to predict danger and adopt safe, suitable, and timely strategies suggests that advanced driver assistance systems and connected and autonomous vehicles can learn similar responses.
  • Crash severity effects of adaptive signal control technology: An empirical
           assessment with insights from Pennsylvania and Virginia
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Zulqarnain H. Khattak, Michael D. Fontaine, Brian L. Smith, Jiaqi Ma Adaptive signal control technology (ASCT) is an intelligent transportation systems (ITS) technology that optimizes signal timings in real time to improve corridor flow. While a few past studies have examined the impact of ASCT on crash frequency, little is known about its effect on injury severity outcomes. Similarly, the impact of different types of ASCTs deployed across different states is also uncertain. This paper therefore, used ordered probit models with random parameters to estimate the injury severity outcomes resulting from ASCT deployment across Pennsylvania and Virginia. Two disparate systems deployed across the two different states were analyzed to assess whether they had similar impacts on injury severity, although signal timings are optimized using different algorithms by both systems. The estimation results revealed that both ASCT systems were associated with reductions in injury severity levels. Marginal effects showed that Type A ASCT systems reduced the propensity of severe plus moderate and minor injury crashes by 11.70% and 10.36% while type B ASCT reduced the propensity of severe plus moderate and minor injury crashes by 4.39% and 6.92%. Similarly, the ASCTs deployed across the two states were also observed to reduce injury severities. The combined best fit model also revealed a similar trend towards reductions in severe plus moderate and minor injury crashes by 5.24% and 9.91%. This model performed well on validation data with a low forecast error of 0.301 and was also observed to be spatially transferable. These results encourage the consideration of ASCT deployments at intersections with high crash severities and have practical implications for aiding agencies in making future deployment decisions about ASCT.
  • Patterns of motorcycle helmet use – A naturalistic observation study
           in Myanmar
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Felix Wilhelm Siebert, Deike Albers, U Aung Naing, Paolo Perego, Chamaiparn Santikarn Developing countries are subject to increased motorization, particularly in the number of motorcycles. As helmet use is critical to the safety of motorcycle riders, the goal of this study was to identify observable patterns of helmet use, which allow a more accurate assessment of helmet use in developing countries. In a video based observation study, 124,784 motorcycle riders were observed at seven observation sites throughout Myanmar. Recorded videos were coded for helmet use, number of riders on the motorcycle, rider position, gender, and time of day. Generally, motorcycle helmet use in Myanmar was found to be low with only 51.5% percent of riders wearing a helmet. Helmet use was highest for drivers (68.1%) and decreased for every additional passenger. It was lowest for children standing on the floorboard of the motorcycle (11.3%). During the day, helmet use followed a unimodal distribution, with the highest use observed during the late morning and lowest use observed in the early morning and late afternoon. Helmet use varied significantly between observation sites, ranging from 74.8% in Mandalay to 26.9% in Pakokku. In Mandalay, female riders had a higher helmet use than male riders, and helmet use decreased drastically on a national holiday in the city. Helmet use of motorcycle riders in Myanmar follows distinct patterns. Knowledge of these patterns can be used to design more precise helmet use evaluations and guide traffic law policy and police enforcement measures. Video based observation proved to be an efficient tool to collect helmet use data.
  • A methodology to estimate the number of unsafe vehicle-cyclist passing
           events on urban arterials
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Kushal Mehta, Babak Mehran, Bruce Hellinga In this study interactions between motorized vehicles and bicycles were studied by analyzing the overtaking behavior of motorized vehicles when passing bicycles on urban arterials. A methodology is presented to estimate the number of ‘unsafe’ passing events on 4-lane urban arterials with no on-street bike lanes. A ‘critical passing distance’ is defined to classify expected passing maneuvers i.e. when a motorized vehicle overtakes a bicycle, into ‘safe’ and ‘unsafe’ passing events. The proposed method enables calculation of the expected number of ‘unsafe passing’ events based on the expected bicycle demand, road segment’s length, AADT, speed limit, and traffic signal timing parameters. The ‘critical passing distance’ is an input parameter and can be set by the planner. Given the number of expected ‘unsafe passing’ events, and institutional safety objectives and standards in terms of acceptable risk levels for cyclists, transportation planning departments can use the proposed methodology to decide whether provision of a specific cycling facility is necessary for a given road segment.
  • Dimensions of driving-related emotions and behaviors: An exploratory
           factor analysis of common self-report measures
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): John P.K. Bernstein, Matthew Calamia ObjectiveA wide variety of driving self-report measures are purported to assess drivers’ behaviors and emotions. However, little is known about the underlying factor structure of these measures. This study examined the factor structure of several self-report measures frequently utilized in the assessment of driving-related behaviors and emotions.DesignCohort survey in a large sample (n = 287) of young adults (mean age = 19.91 years, SD = 1.65).ResultsExploratory factor analysis revealed a four-factor structure that included reckless driving behaviors, negative driving-related emotions, aggressive driving behaviors in response to perceived transgressions from other drivers, and perceived aggressive driving behaviors from other drivers. Aggressive driving behaviors not performed in response to other drivers loaded onto both aggressive driving-related factors.ConclusionsThe factor structure derived in the present study suggests considerable overlap in the content across commonly administered driving self-reports, while also suggesting four distinct dimensions of self-reported driving emotions and behaviors. Whereas some of these dimensions have been explored considerably in the literature (e.g., negative emotions), others deserve further exploration (e.g., perceived aggressive driving behaviors from other drivers). Implications for clinical practice and future investigations are discussed.
  • Evaluation of a novel bicycle helmet concept in oblique impact testing
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Emily Bliven, Alexandra Rouhier, Stanley Tsai, Rémy Willinger, Nicolas Bourdet, Caroline Deck, Steven M. Madey, Michael Bottlang BackgroundA novel bicycle helmet concept has been developed to mitigate rotational head acceleration, which is a predominant mechanism of traumatic brain injury (TBI). This WAVECEL concept employs a collapsible cellular structure that is recessed within the helmet to provide a rotational suspension. This cellular concept differs from other bicycle helmet technologies for mitigation of rotational head acceleration, such as the commercially available Multi-Directional Impact Protection System (MIPS) technology which employs a slip liner to permit sliding between the helmet and the head during impact. This study quantified the efficacy of both, the WAVECEL cellular concept, and a MIPS helmet, in direct comparison to a traditional bicycle helmet made of rigid expanded polystyrene (EPS).MethodsThree bicycle helmet types were subjected to oblique impacts in guided vertical drop tests onto an angled anvil: traditional EPS helmets (CONTROL group); helmets with a MIPS slip liner (SLIP group); and helmets with a WAVECEL cellular structure (CELL group). Helmet performance was evaluated using 4.8 m/s impacts onto anvils angled at 30°, 45°, and 60° from the horizontal plane. In addition, helmet performance was tested at a faster speed of 6.2 m/s onto the 45° anvil. Five helmets were tested under each of the four impact conditions for each of the three groups, requiring a total of 60 helmets. Headform kinematics were acquired and used to calculate an injury risk criterion for Abbreviated Injury Score (AIS) 2 brain injury.ResultsLinear acceleration of the headform remained below 90 g and was not associated with the risk of skull fracture in any impact scenario and helmet type. Headform rotational acceleration in the CONTROL group was highest for 6.2 m/s impacts onto the 45° anvil (7.2 ± 0.6 krad/s2). In this impact scenario, SLIP helmets and CELL helmets reduced rotational acceleration by 22% (p = 0003) and 73% (p 
  • FAST DASH: Program overview and key findings from initial technology
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Andrew J. Krum, Richard J. Hanowski, Andrew Miller, Andy Schaudt, Susan Soccolich One focus of the U.S. Federal Motor Carrier Safety Administration (FMCSA) is to provide leadership in the testing and evaluation of promising safety technologies developed for use in commercial motor vehicles (CMVs). To this end, a program was developed by FMCSA to conduct independent, short-turnaround evaluations of promising safety technologies. Vendors who had promising safety technologies, focused in the commercial vehicle domain, were solicited to participate and submit an application. One technology was selected by FMCSA for each evaluation cycle (lasting approximately 18 months). The technology was tested in both static and dynamic conditions, after which a trucking fleet, and its drivers, were brought in to test the technology in a field operational test (FOT) lasting approximately 6 weeks. During the FOT, 15–20 trucks were instrumented with the technology and other data collection equipment, including sensors and video cameras. A study was then conducted during which drivers used the technology in their revenue-producing operations. Initially, often for the first 2 months, the technology collected data but did but not actively present alerts to the driver. Following this baseline period, a four-month intervention period was conducted. Each evaluation has resulted in more than 1,000,000 km of driving data including continuous video data. Data analyses focused on understanding the efficacy of the technology in terms of (i) safety improvements, (ii) challenges to implementation (e.g., unintended consequences), and (iii) user acceptance (including driver, fleet manager, and other fleet personnel as appropriate). The technology vendors who applied for the first three evaluations can be classified into the following general categories: fatigue/drowsiness, fleet management, visibility safety systems, cell phone policy/enforcement, and other systems. Three technology evaluations were completed in the first 5 years of (i) a blind spot detection and warning system, (ii) an onboard monitoring system, and (iii) a novel mirror technology. High-level results of each of these three evaluations are highlighted in the paper.
  • Factors affecting the injury severity of out-of-control single-vehicle
           crashes in Singapore
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Mo Zhou, Hoong Chor Chin Single-vehicle (SV) crashes are of major concerns because of their high fatality rates. To understand the proneness of high injury severity for vehicle operators brought about by SV crashes without the confounding influence of other road users, this study focuses on those SV crashes without colliding with pedestrians, which are defined as out-of-control SV crashes given the general consequence of involved vehicles. Moreover, to compare the influence of contributory factors (including driver-vehicle/rider-vehicle, roadway, and environmental characteristics) by vehicle types, the injury severity for riders of motorized two-wheelers and drivers of other motorized vehicles are investigated separately using two disaggregated ordered probit models. The results show that for both riders and drivers, variables such as age (65 and above), drink driving, error type of failing to have proper control, driving maneuvers of left and right turns as well as driving after midnight are associated with more severe injuries whereas factors such as wet, oily or sandy surfaces are related to less severe injury. Four other variables, i.e., foreign vehicle registration, probation or expired license, high speed-limit roads, and type of median lane, have different influences on riders and drivers on injury severity. Additionally, factors such as road traffic type and nationality are only found to significantly influence only riders and drivers respectively. The results shed light on both the similar and different causes of high injury severity for riders and drivers involved in out-of-control SV crashes. Based on the findings, targeted countermeasures may be introduced from multiple perspectives such as driver education and policy development to improve non-traffic-interactive safety.
  • Real-time crash prediction models: State-of-the-art, design pathways and
           ubiquitous requirements
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Moinul Hossain, Mohamed Abdel-Aty, Mohammed A. Quddus, Yasunori Muromachi, Soumik Nafis Sadeek Proactive traffic safety management systems can monitor traffic conditions in real-time, identify the formation of unsafe traffic dynamics, and implement suitable interventions to bring unsafe conditions back to normal traffic situations. Recent advancements in artificial intelligence, sensor fusion and algorithms have brought about the introduction of a proactive safety management system closer to reality. The basic prerequisite for developing such a system is to have a reliable crash prediction model that takes real-time traffic data as input and evaluates their association with crash risk. Since the early 21st century, several studies have focused on developing such models. Although the idea has considerably matured over time, the endeavours have been quite discrete and fragmented at best because the fundamental aspects of the overall modelling approach substantially vary. Therefore, a number of transitional challenges have to be identified and subsequently addressed before a ubiquitous proactive safety management system can be formulated, designed and implemented in real-world scenarios. This manuscript conducts a comprehensive review of existing real-time crash prediction models with the aim of illustrating the state-of-the-art and systematically synthesizing the thoughts presented in existing studies in order to facilitate its translation from an idea into a ready to use technology. Towards that journey, it conducts a systematic review by applying various text mining methods and topic modelling. Based on the findings, this paper ascertains the development pathways followed in various studies, formulates the ubiquitous design requirements of such models from existing studies and knowledge of similar systems. Finally, this study evaluates the universality and design compatibility of existing models. This paper is, therefore, expected to serve as a one stop knowledge source for facilitating a faster transition from the idea of real-time crash prediction models to a real-world operational proactive traffic safety management system.
  • The long road home from distraction: Investigating the time-course of
           distraction recovery in driving
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Vanessa K. Bowden, Shayne Loft, Michael D. Wilson, James Howard, Troy A.W. Visser Driver distraction is a leading cause of accidents. While there has been significant research examining driver performance during a distraction, there has been less focus on how much time is required to recover performance following a distraction. To address this issue, participants in the current study completed a simulated 40-min drive while being presented with distractions. Distractions were followed by a visual Detection Response Task (DRT) to assess participants’ resource availability and potential capacity to respond to hazards, as well as continuous measures of driving performance including their ability to maintain a consistent speed and lane position. We examined recovery for a 40 s period following three types of distraction: cognitive only, cognitive + visual, and cognitive + visual + manual. Since safe driving requires cognitive, visual, and manual resources, we expected recovery to take longer when the distraction involved more of these resources. Consistent with this, each additional level of distraction further slowed DRT response times and increased speed variability during 0–10 s post-distraction. However, DRT accuracy was equally impaired for all conditions during 0–20 s post-distraction, while lane position maintenance from 0 to 10 s post-distraction was only impaired when the distraction included a manual component. In addition, while participants in all three conditions exhibited some degree of post-distraction impairment, only those in the cognitive + visual + manual condition reduced their speed during the time when distracted, suggesting drivers show limited awareness of the potential persistent consequences of distraction.
  • Evaluating the safety impact of connected and autonomous vehicles on
    • Abstract: Publication date: March 2019Source: Accident Analysis & Prevention, Volume 124Author(s): Alkis Papadoulis, Mohammed Quddus, Marianna Imprialou Recent technological advancements bring the Connected and Autonomous Vehicles (CAVs) era closer to reality. CAVs have the potential to vastly improve road safety by taking the human driver out of the driving task. However, the evaluation of their safety impacts has been a major challenge due to the lack of real-world CAV exposure data. Studies that attempt to simulate CAVs by using either a single or integrating multiple simulation platforms have limitations, and in most cases, consider a small element of a network (e.g. a junction) and do not perform safety evaluations due to inherent complexity. This paper addresses this problem by developing a decision-making CAV control algorithm in the simulation software VISSIM, using its External Driver Model Application Programming Interface. More specifically, the developed CAV control algorithm allows a CAV, for the first time, to have longitudinal control, search adjacent vehicles, identify nearby CAVs and make lateral decisions based on a ruleset associated with motorway traffic operations. A motorway corridor within M1 in England is designed in VISSIM and employed to implement the CAV control algorithm. Five simulation models are created, one for each weekday. The baseline models (i.e. CAV market penetration: 0%) are calibrated and validated using real-world minute-level inductive loop detector data and also data collected from a radar-equipped vehicle. The safety evaluation of the proposed algorithm is conducted using the Surrogate Safety Assessment Model (SSAM). The results show that CAVs bring about compelling benefit to road safety as traffic conflicts significantly reduce even at relatively low market penetration rates. Specifically, estimated traffic conflicts were reduced by 12–47%, 50–80%, 82–92% and 90–94% for 25%, 50%, 75% and 100% CAV penetration rates respectively. Finally, the results indicate that the presence of CAVs ensured efficient traffic flow.
  • Prioritizing highway safety improvement projects: A Monte-Carlo based Data
           Envelopment Analysis approach
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Ali Dadashi, Babak Mirbaha Road authorities have to prioritize safety improvement projects due to budget limitations. This process needs to estimate expected benefits (reduction in average crash frequency) and costs of projects. Due to variances of crash modification factor (CMF), crash frequency and cost of projects, prediction of costs and benefits would be accompanied by uncertainty and it can subsequently lead to a wrong decision making. To deal with the inherent uncertainty in the decision making process, this paper presents a ranking approach based on integration of Data Envelopment Analysis and Monte-Carlo simulation. A Monte-Carlo simulation is applied to generate stochastic values as input and outputs of the problem instead of running DEA model just for deterministic case. Data from an existing case study is used to evaluate the performance of the proposed methodology. Numerical results indicate that DEA results are very sensitive to data uncertainty and uncertainties can have great influence in ranking results of road safety improvement projects especially when both input and output data are uncertain. It also indicates that how the proposed methodology can be useful for detecting sensitive decision-making units and providing a more comprehensive view for decision makers to allocate a limit budget to the most efficient safety improvement projects.
  • The relation between working conditions, aberrant driving behaviour and
           crash propensity among taxi drivers in China
    • Abstract: Publication date: Available online 4 April 2018Source: Accident Analysis & PreventionAuthor(s): Yonggang Wang, Linchao Li, Carlo G. Prato Although the taxi industry is playing an important role in Chinese everyday life, little attention has been posed towards occupational health issues concerning the taxi drivers’ working conditions, driving behaviour and road safety. A cross-sectional survey was administered to 1021 taxi drivers from 21 companies in four Chinese cities and collected information about (i) sociodemographic characteristics, (ii) working conditions, (iii) frequency of daily aberrant driving behaviour, and (iv) involvement in property-damage-only (PDO) and personal injury (PI) crashes over the past two years. A hybrid bivariate model of crash involvement was specified: (i) the hybrid part concerned a latent variable model capturing unobserved traits of the taxi drivers; (ii) the bivariate part modelled jointly both types of crashes while capturing unobserved correlation between error terms. The survey answers paint a gloomy picture in terms of workload, as taxi drivers reported averages of 9.4 working hours per day and 6.7 working days per week that amount on average to about 63.0 working hours per week. Moreover, the estimates of the hybrid bivariate model reveal that increasing levels of fatigue, reckless behaviour and aggressive behaviour are positively related to a higher propensity of crash involvement. Lastly, the heavy workload is also positively correlated with the higher propensity of crashing, not only directly as a predictor of crash involvement, but also indirectly as a covariate of fatigue and aberrant driving behaviour. The findings from this study provide insights into potential strategies for preventive education and taxi industry management to improve the working conditions and hence reduce fatigue and road risk for the taxi drivers.
  • Predicting performance and safety based on driver fatigue
    • Abstract: Publication date: Available online 3 April 2018Source: Accident Analysis & PreventionAuthor(s): Daniel Mollicone, Kevin Kan, Chris Mott, Rachel Bartels, Steve Bruneau, Matthew van Wollen, Amy R. Sparrow, Hans P.A. Van Dongen Fatigue causes decrements in vigilant attention and reaction time and is a major safety hazard in the trucking industry. There is a need to quantify the relationship between driver fatigue and safety in terms of operationally relevant measures. Hard-braking events are a suitable measure for this purpose as they are relatively easily observed and are correlated with collisions and near-crashes. We developed an analytic approach that predicts driver fatigue based on a biomathematical model and then estimates hard-braking events as a function of predicted fatigue, controlling for time of day to account for systematic variations in exposure (traffic density). The analysis used de-identified data from a previously published, naturalistic field study of 106 U.S. commercial motor vehicle (CMV) drivers. Data analyzed included drivers’ official duty logs, sleep patterns measured around the clock using wrist actigraphy, and continuous recording of vehicle data to capture hard-braking events. The curve relating predicted fatigue to hard-braking events showed that the frequency of hard-braking events increased as predicted fatigue levels worsened. For each increment on the fatigue scale, the frequency of hard-braking events increased by 7.8%. The results provide proof of concept for a novel approach that predicts fatigue based on drivers’ sleep patterns and estimates driving performance in terms of an operational metric related to safety. The approach can be translated to practice by CMV operators to achieve a fatigue risk profile specific to their own settings, in order to support data-driven decisions about fatigue countermeasures that cost-effectively deliver quantifiable operational benefits.
  • Cognitive flexibility: A distinct element of performance impairment due to
           sleep deprivation
    • Abstract: Publication date: Available online 15 March 2018Source: Accident Analysis & PreventionAuthor(s): K.A. Honn, J.M. Hinson, P. Whitney, H.P.A. Van Dongen In around-the-clock operations, reduced alertness due to circadian misalignment and sleep loss causes performance impairment, which can lead to catastrophic errors and accidents. There is mounting evidence that performance on different tasks is differentially affected, but the general principles underlying this differentiation are not well understood. One factor that may be particularly relevant is the degree to which tasks require executive control, that is, control over the initiation, monitoring, and termination of actions in order to achieve goals. A key aspect of this is cognitive flexibility, i.e., the deployment of cognitive control resources to adapt to changes in events. Loss of cognitive flexibility due to sleep deprivation has been attributed to “feedback blunting,” meaning that feedback on behavioral outcomes has reduced salience - and that feedback is therefore less effective at driving behavior modification under changing circumstances. The cognitive mechanisms underlying feedback blunting are as yet unknown. Here we present data from an experiment that investigated the effects of sleep deprivation on performance after an unexpected reversal of stimulus-response mappings, requiring cognitive flexibility to maintain good performance. Nineteen healthy young adults completed a 4-day in-laboratory study. Subjects were randomized to either a total sleep deprivation condition (n = 11) or a control condition (n = 8). Athree-phase reversal learning decision task was administered at baseline, and again after 30.5 h of sleep deprivation, or matching well-rested control. The task was based on a go/no go task paradigm, in which stimuli were assigned to either a go (response) set or a no go (no response) set. Each phase of the task included four stimuli (two in the go set and two in the no go set). After each stimulus presentation, subjects could make a response within 750 ms or withhold their response. They were then shown feedback on the accuracy of their response. In phase 1 of the task, subjects were explicitly told which stimuli were assigned to the go and no go sets. In phases 2 and 3, new stimuli were used that were different from those used in phase 1. Subjects were not explicitly told the go/no go mappings and were instead required to use accuracy feedback to learn which stimuli were in the go and nogo sets. Phase 3 continued directly from phase 2 and retained the same stimuli as in phase 2, but there was an unannounced reversal of the stimulus-response mappings. Task results confirmed that sleep deprivation resulted in loss of cognitive flexibility through feedback blunting, and that this effect was not produced solely by (1) general performance impairment because of overwhelming sleep drive; (2) reduced working memory resources available to perform the task; (3) incomplete learning of stimulus-response mappings before the unannounced reversal; or (4) interference with stimulus identification through lapses in vigilant attention. Overall, the results suggest that sleep deprivation causes a fundamental problem with dynamic attentional control. This element of performance impairment due to sleep deprivation appears to be distinct from vigilant attention deficits, and represents a particularly significant challenge for fatigue risk management.
  • Assessing crash risk considering vehicle interactions with trucks using
           point detector data
    • Abstract: Publication date: Available online 12 March 2018Source: Accident Analysis & PreventionAuthor(s): Kyung (Kate) Hyun, Kyungsoo Jeong, Andre Tok, Stephen G. Ritchie Trucks have distinct driving characteristics in general traffic streams such as lower speeds and limitations in acceleration and deceleration. As a consequence, vehicles keep longer headways or frequently change lane when they follow a truck, which is expected to increase crash risk. This study introduces several traffic measures at the individual vehicle level to capture vehicle interactions between trucks and non-trucks and analyzed how the measures affect crash risk under different traffic conditions. The traffic measures were developed using headways obtained from Inductive Loop Detectors (ILDs). In addition, a truck detection algorithm using a Gaussian Mixture (GM) model was developed to identify trucks and to estimate truck exposure from ILD data. Using the identified vehicle types from the GM model, vehicle interaction metrics were categorized into three groups based on the combination of leading and following vehicle types. The effects of the proposed traffic measures on crash risk were modeled in two different cases of prior- and non-crash using a case-control approach utilizing a conditional logistic regression. Results showed that the vehicle interactions between the leading and following vehicle types were highly associated with crash risk, and further showed different impacts on crash risk by traffic conditions. Specifically, crashes were more likely to occur when a truck following a non-truck had shorter average headway but greater headway variance in heavy traffic while a non-truck following a truck had greater headway variance in light traffic. This study obtained meaningful conclusions that vehicle interactions involved with trucks were significantly related to the crash likelihood rather than the measures that estimate average traffic condition such as total volume or average headway of the traffic stream.
  • Data and methods for studying commercial motor vehicle driver fatigue,
           highway safety and long-term driver health
    • Abstract: Publication date: Available online 9 March 2018Source: Accident Analysis & PreventionAuthor(s): Hal S. Stern, Daniel Blower, Michael L. Cohen, Charles A. Czeisler, David F. Dinges, Joel B. Greenhouse, Feng Guo, Richard J. Hanowski, Natalie P. Hartenbaum, Gerald P. Krueger, Melissa M. Mallis, Richard F. Pain, Matthew Rizzo, Esha Sinha, Dylan S. Small, Elizabeth A. Stuart, David H. Wegman This article summarizes the recommendations on data and methodology issues for studying commercial motor vehicle driver fatigue of a National Academies of Sciences, Engineering, and Medicine study. A framework is provided that identifies the various factors affecting driver fatigue and relating driver fatigue to crash risk and long-term driver health. The relevant factors include characteristics of the driver, vehicle, carrier and environment. Limitations of existing data are considered and potential sources of additional data described. Statistical methods that can be used to improve understanding of the relevant relationships from observational data are also described. The recommendations for enhanced data collection and the use of modern statistical methods for causal inference have the potential to enhance our understanding of the relationship of fatigue to highway safety and to long-term driver health.
  • Exploring the temporal stability of global road safety statistics
    • Abstract: Publication date: Available online 21 February 2018Source: Accident Analysis & PreventionAuthor(s): Loukas Dimitriou, Paraskevas Nikolaou, Constantinos Antoniou Given the importance of rigorous quantitative reasoning in supporting national, regional or global road safety policies, data quality, reliability, and stability are of the upmost importance. This study focuses on macroscopic properties of road safety statistics and the temporal stability of these statistics at a global level. A thorough investigation of two years of measurements was conducted to identify any unexpected gaps that could highlight the existence of inconsistent measurements. The database used in this research includes 121 member countries of the United Nation (UN-121) with a population of at least one million (smaller country data shows higher instability) and includes road safety and socioeconomic variables collected from a number of international databases (e.g. WHO and World Bank) for the years 2010 and 2013. For the fulfillment of the earlier stated goal, a number of data visualization and exploratory analyses (Hierarchical Clustering and Principal Component Analysis) were conducted. Furthermore, in order to provide a richer analysis of the data, we developed and compared the specification of a number of Structural Equation Models for the years 2010 and 2013. Different scenarios have been developed, with different endogenous variables (indicators of mortality rate and fatality risk) and structural forms. The findings of the current research indicate inconsistency phenomena in global statistics of different instances/years. Finally, the results of this research provide evidence on the importance of careful and systematic data collection for developing advanced statistical and econometric techniques and furthermore for developing road safety policies.
  • Fatigue as a mediator of the relationship between quality of life and
           mental health problems in hospital nurses
    • Abstract: Publication date: Available online 14 February 2018Source: Accident Analysis & PreventionAuthor(s): Ahmad Bazazan, Iman Dianat, Zohreh Mombeini, Aydin Aynehchi, Mohammad Asghari Jafarabadi The aims of this study were to investigate the relationships among quality of life (QoL), mental health problems and fatigue among hospital nurses, and to test whether fatigue and its multiple dimensions would mediate the effect of QoL on mental health problems. Data were collected using questionnaires (including the World Health Organization Quality of Life-BREF [WHOQOL-BREF], General Health Questionnaire [GHQ-12] and Multidimensional Fatigue Inventory [MFI-20] for evaluation of QoL, mental health problems and fatigue, respectively) from 990 Iranian hospital nurses, and analysed by generalized structural equation modelling (GSEM). The results indicated that QoL, mental health problems and fatigue were interrelated, and supported the direct and indirect (through fatigue) effects of QoL on mental health problems. All domains of the WHOQOL-BREF, and particularly physical (sleep problems), psychological (negative feelings) and environmental health (leisure activities) domains, were strongly related to the mental health status of the studied nurses. Fatigue and its multiple dimensions partially mediated the relationship between QoL and mental health problems. The results highlighted the importance of physical, psychological and environmental aspects of QoL and suggested the need for potential interventions to improve fatigue (particularly physical fatigue along with mental fatigue) and consequently mental health status of this working population. The findings have possible implications for nurses' health and patient safety outcomes.
  • Prevalence of operator fatigue in winter maintenance operations
    • Abstract: Publication date: Available online 3 February 2018Source: Accident Analysis & PreventionAuthor(s): Matthew C. Camden, Alejandra Medina-Flintsch, Jeffrey S. Hickman, James Bryce, Gerardo Flintsch, Richard J. Hanowski Similar to commercial motor vehicle drivers, winter maintenance operators are likely to be at an increased risk of becoming fatigued while driving due to long, inconsistent shifts, environmental stressors, and limited opportunities for sleep. Despite this risk, there is little research concerning the prevalence of winter maintenance operator fatigue during winter emergencies. The purpose of this research was to investigate the prevalence, sources, and countermeasures of fatigue in winter maintenance operations. Questionnaires from 1043 winter maintenance operators and 453 managers were received from 29 Clear Road member states. Results confirmed that fatigue was prevalent in winter maintenance operations. Over 70% of the operators and managers believed that fatigue has a moderate to significant impact on winter maintenance operations. Approximately 75% of winter maintenance operators reported to at least sometimes drive while fatigued, and 96% of managers believed their winter maintenance operators drove while fatigued at least some of the time. Furthermore, winter maintenance operators and managers identified fatigue countermeasures and sources of fatigue related to winter maintenance equipment. However, the countermeasures believed to be the most effective at reducing fatigue during winter emergencies (i.e., naps) were underutilized. For example, winter maintenance operators reported to never use naps to eliminate fatigue. These results indicated winter maintenance operations are impacted by operator fatigue. These results support the increased need for research and effective countermeasures targeting winter maintenance operator fatigue.
  • Modeling when and where a secondary accident occurs
    • Abstract: Publication date: Available online 2 February 2018Source: Accident Analysis & PreventionAuthor(s): Junhua Wang, Boya Liu, Ting Fu, Shuo Liu, Joshua Stipancic The occurrence of secondary accidents leads to traffic congestion and road safety issues. Secondary accident prevention has become a major consideration in traffic incident management. This paper investigates the location and time of a potential secondary accident after the occurrence of an initial traffic accident. With accident data and traffic loop data collected over three years from California interstate freeways, a shock wave-based method was introduced to identify secondary accidents. A linear regression model and two machine learning algorithms, including a back-propagation neural network (BPNN) and a least squares support vector machine (LSSVM), were implemented to explore the distance and time gap between the initial and secondary accidents using inputs of crash severity, violation category, weather condition, tow away, road surface condition, lighting, parties involved, traffic volume, duration, and shock wave speed generated by the primary accident. From the results, the linear regression model was inadequate in describing the effect of most variables and its goodness-of-fit and accuracy in prediction was relatively poor. In the training programs, the BPNN and LSSVM demonstrated adequate goodness-of-fit, though the BPNN was superior with a higher CORR and lower MSE. The BPNN model also outperformed the LSSVM in time prediction, while both failed to provide adequate distance prediction. Therefore, the BPNN model could be used to forecast the time gap between initial and secondary accidents, which could be used by decision makers and incident management agencies to prevent or reduce secondary collisions.
  • Impact of real-time traffic characteristics on crash occurrence:
           Preliminary results of the case of rare events
    • Abstract: Publication date: Available online 5 January 2018Source: Accident Analysis & PreventionAuthor(s): Athanasios Theofilatos, George Yannis, Pantelis Kopelias, Fanis Papadimitriou Considerable efforts have been made from researchers and policy makers in order to explain road crash occurrence and improve road safety performance of highways. However, there are cases when crashes are so few that they could be considered as rare events. In such cases, the binary dependent variable is characterized by dozens to thousands of times fewer events (crashes) than non-events (non-crashes). This paper attempts to add to the current knowledge by investigating crash likelihood by utilizing real-time traffic data and by proposing a framework driven by appropriate statistical models (Bias Correction and Firth method) in order to overcome the problems that arise when the number of crashes is very low. Under this approach instead of using traditional logistic regression methods, crashes are considered as rare events In order to demonstrate this approach, traffic data were collected from three random loop detectors in the Attica Tollway (“Attiki Odos”) located in Greater Athens Area in Greece for the 2008–2011 period. The traffic dataset consists of hourly aggregated traffic data such as flow, occupancy, mean time speed and percentage of trucks in traffic. This study demonstrates the application and findings of our approach and revealed a negative relationship between crash occurrence and speed in crash locations. The method and findings of the study attempt to provide insights on the mechanism of crash occurrence and also to overcome data considerations for the first time in safety evaluation of motorways.
  • A crash prediction method based on bivariate extreme value theory and
           video-based vehicle trajectory data
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Chen Wang, Chengcheng Xu, Yulu Dai Traditional statistical crash prediction models oftentimes suffer from poor data quality and require large amount of historical data. In this paper, we propose a crash prediction method based on a bivariate extreme value theory (EVT) framework, considering both drivers’ perception-reaction failure and failure to proper evasive actions. An unmanned aerial vehicle (UAV) was utilized to collect videos of ten intersections in Fengxian, China, at representative time periods. High-resolution vehicle trajectory data were extracted by a Kanade-Lucas-Tomasi (KLT) technique, based on four detailed metrics were derived including Time-to-accident (TA), Post-encroachment Time (PET), minimum Time-to-collision (mTTC), and Maximum Deceleration Rate (MaxD). TA was expected to capture the chance of perception-reaction failure, while other three metrics were used to measure the probability of failure to proper evasive actions. Univariate EVT models were applied to obtain marginal crash probability based on each metric. Bivariate EVT models were developed to obtain joint crash probability based on three pairs: TA and mTTC, TA and PET, and TA and MaxD. Thus, union crash probability within observation periods can be derived and the annual crash frequency of each intersection was predicted. The predictions were compared to actual annual crash frequencies, using multiple tests. The findings are three-folds: 1. The best conflict metrics for angle and rear-end crash predictions were different; 2. Bivariate EVT models were found to be superior to univariate models, regarding both angle and rear-end crash predictions; 3. TA appeared to be an important conflict metric that should be considered in a bivariate EVT model framework. In general, the proposed method can be considered as a promising tool for safety evaluation, when crash data are limited.
  • Coherence assessment of accident database kinematic data
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Dario Vangi, Michelangelo-Santo Gulino, Carlo Cialdai The analysis and research of accidents aimed at improving the safety of vehicles and infrastructures are typically based on the retrospective investigation of data that are collected in in-depth accident databases. In particular, kinematic data related to accidents (impact velocity, velocity change of the vehicles, etc.) make possible the identification of correlations between impact severity and injury risk (IR), as well as assessing the effectiveness of vehicle protection systems. The necessary condition to conduct suitable and significant analyses is to utilise data which are correct and representative of national statistics, i.e., congruent with physical laws governing the accident phenomena. Whereas representativeness can generally be retrospectively verified, the checks on kinematic data coherence during codification are rarely performed.The present work describes a procedure to verify the internal coherence of kinematic data collected in in-depth accident databases. The introduced checks allow the identification of parameters, which are not internally coherent because the accident reconstruction model employed is inappropriate or improperly used. These checks pertain to physical laws on which road accident reconstruction is based, i.e., momentum conservation, compatibility of velocity triangles, and energy conservation. Moreover, they can be modified and expanded to consider other parameters, making the methodology virtually applicable to any database.In the case of vehicle-to-vehicle collisions, the application of the procedure to detect incongruent data inside two real databases demonstrates how their number is often not negligible. Furthermore, consequences can be substantial for both direct and secondary analysis, i.e., determining IR curves (for example, logistic regression on input data) and identifying IR associated to an accident. Accordingly, the application of checks is particularly recommended during both analysis and collection phases to confirm the congruence of collected data; consequently, the quality of investigation is enhanced.
  • Real-world effects of rear cross-traffic alert on police-reported backing
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Jessica B. Cicchino ObjectiveSeveral parking assistance systems are available to help drivers back up. Rear cross-traffic alert warns drivers when they are reversing and cross-traffic approaches the rear of their vehicle, as might happen when backing out of a perpendicular parking space in a lot. The goal of this study was to examine the effect of rear cross-traffic alert on backing crashes.MethodsNegative binomial regression was used to compare police-reported backing crash rates in 25 U.S. states per insured vehicle year between General Motors and Mazda vehicles with rear cross-traffic alert and the same vehicle models without the optional system, controlling for the presence of other parking assistance systems and additional factors that may affect crash risk.ResultsCrash involvement rates in backing crashes overall were 22% lower among vehicles with rear cross-traffic alert than among vehicles without the system when averaged between the two manufacturers. Rates were 32% lower among vehicles with the system than without in two-vehicle backing crashes where the vehicles were traveling in perpendicular directions. Both reductions were statistically significant.ConclusionsRearview cameras and rear automatic braking, which are designed to help drivers detect and brake for obstacles directly behind the vehicle, have been shown in previous research to be effective in reducing backing crashes reported to the police. Rear cross-traffic alert can complement these systems by preventing potential backing crashes that other parking assistance systems are not designed to detect. Manufacturers should equip vehicles with rear cross-traffic alert, in addition to other parking assistance systems like rear automatic braking, to maximize the number of backing crashes prevented.
  • Safety impact of right-turn waiting area at signalised junctions
           conditioned on driver’s decision-making based on fuzzy cellular automata
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Yidan Gao, Qingji Zhou, Chen Chai, Yiik Diew Wong Right-turn waiting area (RWA) is a short demarcated queueing area ahead of the stop line that allows the right-turn vehicles at signalised junctions under the permissive filtering signal operation to proceed into the junction-box at the onset of full green signal phase. The RWA layout gives guidance to vehicle placement of turning vehicles which improves safety and mitigates vehicle queue overflow of the right-turn vehicles. RWA enhances the capacity of right-turn lanes while alleviating conflict severity in some cases. This study analysed the safety impact of the conflict between opposing straight-through vehicles and right-turn vehicles at RWA junctions in Singapore. A microscopic simulation model based on Fuzzy Cellular Automata (FCA) was developed. Field surveys were carried out to obtain the inputs for calibrating the fuzzy inference system. Different scenarios were analysed and discussed including different types of junctions with and without RWAs. The proposed model is found to be able to simulate decision-making of individual drivers at RWA or before stop line, such as exiting the queueing area or crossing the stop line when faced with different gaps and velocity of opposing straight-through vehicles.
  • Shorten pedestrians’ perceived waiting time: The effect of tempo and
           pitch in audible pedestrian signals at red phase
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Yi Cao, Xiangling Zhuang, Guojie Ma Long waiting time at red light leads to negative experiences and red-light running behaviors. To shorten pedestrians’ experienced waiting time, this study explores how the tempo and pitch in audible pedestrian signals influence time estimation. In a simulated task of waiting at the red light, we compared pedestrians’ estimation of waiting time for three durations (30 s, 45 s, 60 s) while the tempo (40 bpm, 60 bpm, 120 bpm, bpm as the number of beats per minute) and pitch (175 Hz, 350 Hz, 700 Hz) of the tone were manipulated. The results show that pedestrians’ estimations of waiting time decreased with decreasing tempo in the audible signal, but did not differ significantly across different pitches. To verify the effect of tempo on time estimation in real crossing scenario, we interviewed 217 pedestrians randomly selected at six sites on their waiting time at different tempos. The tempo can still predict pedestrians’ time estimation. The findings have implications for auditory signal design of traffic lights.
  • Trajectory-based identification of critical instantaneous decision events
           at mixed-flow signalized intersections
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Yanning Wei, Keping Li, Keshuang Tang Mixed-flow intersections are prevailing in many developing countries such as China and India. At mixed-flow intersections, there is no clear lane discipline or regular trajectories within the intersection, especially for the non-motorized traffic. This leads to more interactions and encounters between the motorized traffic and the non-motorized traffic. Hence, critical instantaneous decision events such as abrupt accelerating, decelerating, jerking, swerving, and swinging, may occur more frequently, which result in potential traffic conflicts and crashes. This study presents a methodology to identify critical instantaneous decision events at the mixed-flow signalized intersections, based on the entropy theory and high-resolution vehicle trajectory data. A three-dimensional cube searching algorithm is firstly proposed to extract general traffic events by examining the proximity between trajectories. A novel model incorporating vehicle kinematics and Permutation Entropy is then developed to identify critical events, by quantifying driving volatility based on the time-serial trajectory data. Next, 3, 349 vehicle trajectories and 805 bicycle trajectories with a resolution of 0.12 s collected at a signalized intersection in Shanghai are used to demonstrate the proposed method. Results show that the proposed method is capable of identifying critical instantaneous decision events, and tends to produce a higher identification ratio comparing with the conventional method solely based on kinematic thresholds. A sensitivity analysis is also conducted to investigate the effects of model parameters on the performance of the proposed method. The presented work could be applied for traffic safety assessment, real-time driving alert systems, and early diagnosis of risk-prone road users at mixed-flow intersections.
  • Effects of transit signal priority on traffic safety: Interrupted time
           series analysis of Portland, Oregon, implementations
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Yu Song, David Noyce Transit signal priority (TSP) has been implemented to transit systems in many cities of the United States. In evaluating TSP systems, more attention has been given to its operational effects than to its safety effects. Existing studies assessing TSP’s safety effects reported mixed results, indicating that the safety effects of TSP vary in different contexts. In this study, TSP implementations in Portland, Oregon, were assessed using interrupted time series analysis (ITSA) on month-to-month changes in number of crashes from January 1995 to December 2010. Single-group and controlled ITSA were conducted for all crashes, property-damage-only crashes, fatal and injury crashes, pedestrian-involved crashes, and bike-involved crashes. Evaluation of the post-intervention period (2003–2010) showed a reduction in all crashes on street sections with TSP (-4.5%), comparing with the counterfactual estimations based on the control group data. The reduction in property-damage-only crashes (-10.0%) contributed the most to the overall reduction. Fatal and injury crashes leveled out after TSP implementation but did not change significantly comparing with the control group. Pedestrian and bike-involved crashes were found to increase in the post-intervention period with TSP, comparing with the control group. Potential reasons to these TSP effects on traffic safety were discussed.
  • Validating the bivariate extreme value modeling approach for road safety
           estimation with different traffic conflict indicators
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Lai Zheng, Tarek Sayed, Mohamed Essa A range of conflict indicators have been developed for traffic conflict observation. The various conflict indicators have been shown in earlier studies to be of different and sometimes independent nature. Therefore, there is a need to combine different indicators to gain better understanding of the underlying severity of traffic events and for more reliable safety analysis. This study proposes a bivariate extreme value model to integrate different traffic conflict indicators for road safety estimation, and the model is validated with actual crash data. Based on video data collected from four signalized intersections in two Canadian cities, computer vision techniques were utilized to identify rear-end traffic conflicts using several indicators. The conflict indicators included: time to collision (TTC), modified time to collision (MTTC), post encroachment time (PET), and deceleration to avoid crash (DRAC). Then bivariate extreme value models were developed for combinations of each two indicators, and the numbers of crashes were estimated from the models and compared to the observed crashes. The results show that most of the estimated crashes are in the range of 95% Poisson confidence interval of observed crashes, which indicates that the bivariate extreme value model is a promising tool for road safety estimation. Moreover, the accuracy of estimated crashes are different for different indicator combinations. The results show that the estimates of TTC&PET are the most accurate, followed by TTC&MTTC, TTC&DRAC, PET&MTTC, PET&DRAC and MTTC&DRAC. A further correlation analysis suggests that a combination of two independent conflict indicators leads to better crash estimation performance.
  • Quantitatively mining and distinguishing situational discomfort grading
           patterns of drivers from car-following data
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Geqi Qi, Wei Guan Situational discomfort awareness plays an important role in decision making among drivers and has rarely been discussed in detail in previous research. An instrumented vehicle was used to collect car-following data from multiple drivers, thereby quantitatively examining situational discomfort grading patterns using a new discomfort grading method and the latent Dirichlet allocation model. In this process, the gas pedal data and speed difference data are particularly involved in the computation for providing broader meaning to discomfort and building more comprehensive situations. The results show that individual discomfort awareness varies between drivers. More importantly, the potential patterns of situational discomfort grading are extracted, which provides knowledge for characterizing drivers in the context of discomfort awareness. The knowledge achieved can be further applied to distinguish drivers and identify the typical comfort and discomfort zones. This study has great value for promoting investigations on traffic psychology and developing more effective and customized driver assistant systems.
  • Predictive factors associated with driving under the influence among
           Brazilian drug-using drivers
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Juliana N. Scherer, Daiane Silvello, Vanessa L. Volpato, Vinícius S. Roglio, Letícia Fara, Felipe Ornell, Lisia von Diemen, Felix Paim Kessler, Flavio Pechansky The incidence of driving under the influence of psychoactive substances (DUI) and its recidivism can be curtailed by the proper identification of specific and predictive characteristics among drug users. In this sense, interpersonal violence (IV), psychiatric comorbidity and impulsivity seem to play an important role in DUI engagement according to previous studies. There are, however, limited data originated from low and middle income countries. In the present study, drug-using Brazilian drivers reporting DUI (n = 75) presented a higher prevalence of bipolar disorders (BD; DUI: 8% vs. non-DUI: 0%, p 
  • Evaluation of pedestrian crossing behavior and safety at uncontrolled
           mid-block crosswalks with different numbers of lanes in China
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Cunbao Zhang, Feng Chen, Yuanyuan Wei This study aims to investigate pedestrian crossing behavior and safety at uncontrolled mid-block crosswalks with different numbers of vehicle lanes. For this purpose, twelve uncontrolled mid-block crosswalks in Wuhan, China were selected to collect data via field investigation. Descriptive statistics were used to analyze pedestrian crossing behavior, and the distribution of pedestrian-vehicle conflicts on different vehicle lanes was given. Three ordered probit (OP) models for pedestrian-vehicle conflicts analysis (PVCA) were established to measure the effects of various factors on pedestrian safety. Descriptive statistical results showed that crosswalks with different numbers of lanes have diverse impacts on pedestrian crossing behavior and safety. As the number of vehicle lanes increases, the proportion of pedestrians adopting the rolling gap crossing mode, crossing the street with others, and changing the speed or path increase accordingly. Moreover, the number of pedestrian-vehicle conflicts at two-way six-lane crosswalks is 5.96 times higher than that of two-lane crosswalks, and 2.04 times higher than that of four-lane crosswalks. From the results of OP models, it was found that pedestrian behavioral characteristics such as rolling gap crossing mode, crossing with others significantly increased the possibility of pedestrian-vehicle conflicts.Graphical abstractGraphical abstract for this article
  • Influence of drivers’ psychological risk profiles on the effectiveness
           of traffic calming measures
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Lorenzo Domenichini, Valentina Branzi, Martina Smorti Road traffic injuries represent a serious public health problem and are one of leading cause of death, injury and disability around the world. Road accidents are often caused by an accumulation of factors; however, drivers appear to be by far the most decisive one. The driver’s behaviour is complex and depends on reflex (or involuntary) and voluntary driving actions. The first class of actions (reflex actions) are typical human reactions that remain inaccessible to awareness and refer to the direct interaction between the road user and the characteristics of the road and its surrounding environment. Conversely, voluntary actions are conscious behaviours adopted on the basis of planned decisions. Both types of driving actions act simultaneously and the interaction between them and their relative effects on road safety are an aspect not yet well examined. The main objective of this study was to provide, by means of a driving simulation experiment, an insight on this interaction by evaluating the influence of some psychological characteristics on the effectiveness of different types of traffic calming measures at pedestrian crossings, designed according to the Human Factors principles. Fifty-eight participants drove a virtual urban route while data on their performance, as they approached five configurations of pedestrian crossings equipped with different physical and perceptual treatments, were collected. The participants were preliminarily characterized by means of two psychological questionnaires, which allowed the identification of three distinct groups of drivers belonging to three risk profiles (careful, worried, and at risk). The three groups of drivers reacted differently to the proposed engineering treatments, confirming the clustering identified by the preliminary analysis. The results showed that the proposed traffic calming measures are effective on all psychological sub-groups of drivers, with different effectiveness. These first results support that, in the considered driving environment (pedestrian crossings), the Human Factors approach, with which traffic calming measures can be arranged, could be effective, even if different psychological sub-groups are differently affected.
  • The factors shaping car drivers’ attitudes towards cyclist and their
           impact on behaviour
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Laura S. Fruhen, Isabel Rossen, Mark A. Griffin Cycling for transportation has multiple benefits to both individuals and societies. However, in many countries, cycling rates are very low. One major deterrent is hostile or aggressive behaviours directed towards cyclists. Past research has established that negative attitudes towards cyclist are a major driver of aggressive behaviour. However, the attitudinal roots that motivate these negative attitudes are currently not well understood. This study investigates to what extent negative attitudes towards cyclists are rooted in a sense of attachment to cars, and environmental attitudes. Furthermore, the study examines whether the distinctiveness of group-membership of cyclists, as signalled by cycling attire, influences the link between attitudes and aggressive behaviours directed at cyclists. An online survey of 308 car drivers measured automobility and environmental attitudes, attitudes towards cyclist, and aggressive behaviour addressed at two groups of cyclists (lycra-clad or casually dressed cyclists). Hierarchical regression analyses showed that automobility attitudes, but not environmental attitudes, were associated with negative car driver attitudes towards cyclists. A significant link between negative attitudes towards cyclists and aggressive behaviour addressed at cyclists was not moderated by the type of cyclist shown. These findings provide a more refined understanding of the basis in which negative attitudes towards cyclists are rooted and how they affect driver behaviour. This research may inform campaigns and initiatives aimed at changing attitudes towards cyclists.
  • The association between sensation seeking and driving outcomes: A
           systematic review and meta-analysis
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Xiaoyan Zhang, Xingda Qu, Da Tao, Hongjun Xue The purpose of this study was to evaluate the association between sensation seeking (SS) and driving outcomes (including four aberrant driving behaviors, accident involvement and tickets received) through a systematic review and meta-analysis. Forty-four eligible studies, representing 48 individual trials, were identified from a systematic literature search of four electronic databases, and included in the meta-analysis. Overall, the meta-analysis results showed that SS yielded significant positive correlations with risky driving (pooled r = 0.24, p 
  • Observing the observation of (vulnerable) road user behaviour and traffic
           safety: A scoping review
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Wouter van Haperen, Malik Sarmad Riaz, Stijn Daniels, Nicolas Saunier, Tom Brijs, Geert Wets Behavioural observation studies in road safety research collect naturalistic data of road users that are not informed (beforehand) of their participation in a research project. It enables the observation of behavioural and situational processes that contribute to unsafe traffic events, while possible behavioural adaptations due to the road users’ recognition of being observed are minimized. The literature in this field is vast and diverse, with studies dating back to the 1930s. The aim of this paper is to summarize the research efforts in the domain of road user behavioural observation research to examine trends and developments of this type of research, using a scoping review. After the definition of certain selection criteria, 600 journal articles found in three major online databases were retrieved and included in this review.The number of publications regarding road user behavioural observation studies has increased rapidly during recent years, indicating the importance of behavioural observation studies to study traffic safety. Most studies collected data on car drivers (81%), while vulnerable road users have been observed in 32% of all studies, with pedestrians and (motor)cyclists as the most common road user types. The results showed that the main goal of behavioural observation is to monitor (51%), followed by the evaluation of a specific safety improving measure (38%) and the development of behavioural models (10%). Most topics relate to traffic events where interactions with other road users are necessary, indicating that the examination of behavioural processes underlying single-vehicle crashes has received little attention. The ongoing developments of automated video analysis software tools can be the next methodological step forward in video-based behavioural observation studies, since it enables a more objective data collection and data analysis process.
  • Using horizontal curve speed reduction extracted from the naturalistic
           driving study to predict curve collision frequency
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Bashar Dhahir, Yasser Hassan Many models have been developed to predict collision frequency and evaluate safety performance on horizontal curves. The approach used in data collection or some assumptions made in the analysis methodology might lead to inaccurate results. For example, manual data collection, equipment limitations, and field experiments involving monitoring driving behavior for a specific region for a short-term are potential sources of errors in data collection. This paper aims at overcoming some of these issues in developing models to evaluate safety performance of horizontal curves and predict the curve collision frequency. The developed models relate expected collision frequency on horizontal curves to the speed reduction from the approach tangent to the curve, which is commonly used as a major geometric design consistency measure. The methodology to achieve this objective included three tasks; data collection, evaluating and modeling the viable speed reduction parameters, and developing safety performance models to estimate collision frequency on horizontal curves. Individual drivers’ trips on 49 horizontal curves on rural two-lane highways in rolling and mountainous terrains in Washington State were extracted from the Naturalistic Driving Study (NDS) database. Models were developed to relate different speed reduction parameters to curve characteristics. These models were then applied to 1430 horizontal curves in Washington State to estimate the speed reduction parameters and relate them to collision frequency. Several safety performance models were developed which show that speed reduction, as a design consistency measure, is directly related to collision frequency on horizontal curves. Furthermore, the speed reduction parameters are more significant variables in predicting collision frequency than all curve geometric parameters.
  • Probabilistic, safety-explicit design of horizontal curves on two-lane
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Bashar Dhahir, Yasser Hassan The high collision rates on horizontal curves compared to other roadway elements make them one of the most critical elements in a transportation network. In this regard, it is important to develop models to predict the safety performance of the horizontal curves. A considerable number of studies have been conducted to develop safety performance functions based on several concepts such as geometric characteristics, design consistency, reliability analysis, and comfort threshold. However, these models do not account for all horizontal curve design criteria or consider several cases such as driving in adverse weather conditions or on pavement of low available friction. This paper develops a probabilistic, safety explicit approach of horizontal curve design using reliability analysis of four design criteria: vehicle stability, driver comfort, sight distance, and vehicle rollover. Two situations were considered in the analysis: driving in clear weather (dry pavement) and raining weather (wet pavement) to develop safety performance functions for annual and five-year collision frequency. Four types of regression models, Poisson, negative binomial, zero-inflated Poisson, and zero-inflated negative binomial, were used in the analysis. The AIC, BIC, and Vuong test were used in evaluating the developed models.
  • Private and public willingness to pay for safety: A validity test
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Henrik Andersson, Elodie Levivier, Gunnar Lindberg Stated preference (SP) methods are often used to elicit an affected population’s preferences for, e.g., increased safety or better environmental quality. SP methods are based on hypothetical market scenarios which have advantages, since decision alternatives are known to the analysis, but also necessitate thorough validity tests of the results, since decisions are hypothetical. This study suggests a validity test based on theoretical predictions and empirical findings for private and public safety measures. According to the test, willingness to pay (WTP) for a public safety measure should exceed or be equal to the private one. Based on a rich data set eliciting both private and public WTP the results show that private WTP exceeds public WTP. Hence, the findings in this study highlight the importance of validity tests of preference estimates for safety, and suggest that WTP also for a private safety measure should be elicited in studies eliciting WTP for public safety measures, to allow for the validity test.
  • Utilizing UAV video data for in-depth analysis of drivers’ crash risk at
           interchange merging areas
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Xin Gu, Mohamed Abdel-Aty, Qiaojun Xiang, Qing Cai, Jinghui Yuan The interchange merging area suffers a high crash risk in the freeway system, which is greatly related to the intense mandatory merging maneuvers. Ignoring such correlation may result in limited and biased conclusions and inefficient countermeasures. Recently, the availability of unmanned aerial vehicle (UAV) provides us an opportunity to collect individual vehicle’s data to conduct traffic analysis at the microscopic level. Hence, this paper contributes to the literature by proposing a new framework to analyze crash risk at freeway interchange merging areas considering drivers’ merging behavior. The analysis framework is conducted based on individual vehicle data from UAV videos. A multilevel random parameters logistic regression model is proposed to investigate each driver’s merging behavior in the acceleration lane. The model could identify the impact of different factors related to traffic and drivers on the merging behavior. Then, the crash risk between the merging vehicle and surrounding vehicles is calculated by incorporating the time-to-collision (TTC) and the output of the estimated merging behavior’s model. The results suggest that the proposed method provides more valuable insights about the crash risk at interchange merging areas by simultaneously considering the merging behavior and the safety measure. It is concluded that the merging speed, driving ability (e.g., lane change confidence, lane-keeping instability), and the merging location can affect the crash risk. These results can help traffic engineers propose efficient countermeasures to enhance the safety of the interchange merging area. The results also have implications to the design of merging areas and the advent of connected vehicles’ technology.
  • The effects of takeover request modalities on highly automated car control
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Sol Hee Yoon, Young Woo Kim, Yong Gu Ji This study investigated the influences of takeover request (TOR) modalities on a drivers’ takeover performance after they engaged in non-driving related (NDR) tasks in highly automated driving (HAD). Visual, vibrotactile, and auditory modalities were varied in the design of the experiment under four conditions: no-task, phone conversation, smartphone interaction, and video watching tasks. Driving simulator experiments were conducted to analyze the drivers’ take-over performance by collecting data during the transition time of re-engaging control of the vehicle, the time taken to be on the loop, and time taken to be physically ready to drive. Data were gathered on the perceived usefulness, safety, satisfaction, and effectiveness for each TOR based on a self-reported questionnaire. Takeover and hands-on times varied considerably, as shown by high standard deviation values between modalities, especially for phone conversations and smartphone interaction tasks. Moreover, it was found that participants failed to take over control of the vehicle when they were given visual TORs for phone conversation and smartphone interaction tasks. The perceived safety and satisfaction varied for the NDR task. Results from the statistical analysis showed that the NDR task significantly influenced the takeover time, but there was no significant interaction effect between the TOR modalities and the NDR task. The results could potentially be applied to the design of safe and efficient transitions of highly controlled, automated driving, where drivers are enabled to engage in NDR tasks.
  • Evaluating individual risk proneness with vehicle dynamics and self-report
           data ˗ toward the efficient detection of At-risk drivers
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Blazej Palat, Guillaume Saint Pierre, Patricia Delhomme Vehicle-dynamics data, now more readily available thanks to moderate-cost, embedded data logging solutions, have been used to study drivers' behavior (acceleration, braking, and yaw rate) through naturalistic driving research aimed at detecting critical safety events. In addition, self-reported measures have been developed to describe these events and to assess various individual risk factors such as sensation seeking, lack of experience, anger expression while driving, and sensitivity to distraction. In the present study, we apply both of these methods of gathering driving data in order to assess risk proneness as accurately as possible. Data were obtained from 131 drivers, who filled in an introductory questionnaire pertaining to their driving habits. Their vehicles were equipped with an external, automatic data-capture device for approximately two months. During that period, the participants reported critical safety events that occurred behind the wheel by (a) pressing a button connected to the device and (b) describing the events in logbooks. They also filled in weekly questionnaires, and at the end of the participation period, a final questionnaire with various self-reported measures pertaining to their driving activity. We processed the data by (a) performing a multiple correspondence analysis of the characteristics assessed via the automatic data capture and self-reports, and (b) categorizing the participants via hierarchical clustering of their coordinates on the dimensions obtained from the correspondence analysis. This allowed us to identify a group of drivers (n = 43) at risk, based on several self-reported measures, in particular, their recent crash involvement, and the frequency of critical acceleration/deceleration events as an objective measure. However, the at-risk drivers did not themselves report more critical safety events than the other two groups.
  • Self-reported violations, errors and lapses for older drivers: Measuring
           the change in frequency of aberrant driving behaviours across five
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Sjaan Koppel, Amanda N. Stephens, Michel Bédard, Judith L. Charlton, Peteris Darzins, Marilyn Di Stefano, Sylvain Gagnon, Isabelle Gélinas, Phuong Hua, Lynn MacLeay, Malcolm Man-Son-Hing, Barbara Mazer, Anita Myers, Gary Naglie, Morris Odell, Michelle M. Porter, Mark J. Rapoport, Arne Stinchcombe, Holly Tuokko, Brenda Vrkjlan The current study aimed to: 1. to confirm the 21-item, three-factor Driver Behaviour Questionnaire (DBQ) structure suggested by Koppel et al. (2018) within an independent sample of Canadian older drivers; 2. to examine whether the structure of the DBQ remained stable over a four-year period; 3. to conduct a latent growth analysis to determine whether older drivers’ DBQ scores changed across time. Five hundred and sixty Canadian older drivers (males = 61.3%) from the Candrive/Ozcandrive longitudinal study completed the DBQ yearly for four years across five time-points that were approximately 12 months apart. In Year 1, the average age of the older drivers was 76.0 years (SD = 4.5 years; Range = 70–92 years). Findings from the study support the 21-item, three-factor DBQ structure suggested by Koppel and colleagues for an Australian sample of older drivers as being acceptable in an independent sample of Canadian older drivers. In addition, Canadian older drivers’ responses to this version of the DBQ were stable across the five time-points. More specifically, there was very little change in older drivers’ self-reported violations, and no significant change for self-reported errors or lapses. The findings from the current study add further support for this version of the DBQ as being a suitable tool for examining self-reported aberrant driving behaviours in older drivers. Future research should investigate the relationship between older drivers’ self-reported aberrant driving behaviours and their performance on functional measures, their responses to other driving-related abilities and practice scales and/or questionnaires, as well their usual (or naturalistic) driving practices and/or performance on on-road driving tasks.
  • Updated estimates of the relationship between speed and road safety at the
           aggregate and individual levels
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Rune Elvik, Anna Vadeby, Tove Hels, Ingrid van Schagen Recent studies of the relationship between the speed of traffic and road safety, stated as the number of fatalities and the number of injury accidents, are reviewed and their results synthesised by means of meta-analysis. All studies were based on data fully or partly for years after 2000. Previously proposed models of the relationship between the speed of traffic and road safety, including the Power Model and an Exponential Model, are supported. Summary estimates of coefficients show that the relationship between speed and road safety remains strong. The Power Model and the Exponential Model both fit the data very well. The relationship between speed and road safety is the same at the individual driver level as at the aggregate level referring to the mean speed of traffic.
  • Semi-autonomous vehicles: Usage-based data evidences of what could be
           expected from eliminating speed limit violations
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Ana M. Pérez-Marín, Montserrat Guillen The use of advanced driver assistance systems and the transition towards semi-autonomous vehicles are expected to contribute to a lower frequency of motor accidents and to have a significant impact for the automobile insurance industry, as rating methods must be revised to ensure that risks are correctly measured. Telematics information and usage-based insurance research are analyzed to identify the effect of driving patterns on the risk of accident. This is used as a starting point for addressing risk quantification and safety for vehicles that can control speed. The effect of excess speed on the risk of accidents is estimated with a real telematics data set. Scenarios for a reduction of speed limit violations and the consequent decrease in the expected number of accident claims are shown. If excess speed could be eliminated, then the expected number of accident claims could be reduced to half of its initial value, applying the average conditions of the data used in this study. As a consequence, insurance premiums also diminish.
  • Modeling and mitigating fatigue-related accident risk of taxi drivers
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Musen Kingsley Li, Jiayi Joey Yu, Liang Ma, Wei Zhang Taxi drivers worldwide often have very long driving hours and experience frequent fatigue. These conditions are associated with a high prevalence of fatigue and accidents. However, the key factors that distinguish high/low fatigue-related accident risk (FRAR) taxi drivers are uncertain. By examining a series of potential factors related with fatigue or accident risk as discussed in previous research, the objective was to find out the most important factors that relate to taxi driver’s FRAR, and to investigate the association of these factors and taxi driver’s FRAR. Modeling methods were applied to questionnaire data collected from Beijing taxi drivers. A 269-sample dataset was analyzed to identify key factors related to FRAR and to fit FRAR prediction models. The model’s performance on high-risk driver prediction was then tested using another independently collected 100-sample dataset. High-risk taxi drivers had significantly longer driving hours per working day, lower rest ratios, less driving experience, and were more confident about their fatigue resistance. The FRAR model with only four major measurable predictors achieved a sensitivity of 91.9% and a specificity of 94.6% on predicting labeled data. Adjusting drive-rest habits and self-evaluation pertaining to these predictors is good for high-risk drivers to mitigate their accident risk. It was concluded that taxi drivers’ drive-rest habits, experience, and intention for fatigue driving are crucial, and to a large degree determine their FRAR, and the prediction model can satisfactorily identify high-risk taxi drivers.
  • Bicycle helmet wearing is associated with closer overtaking by drivers: A
           response to Olivier and Walter, 2013
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Ian Walker, Dorothy L. Robinson There is a body of research on how driver behaviour might change in response to bicyclists’ appearance. In 2007, Walker published a study suggesting motorists drove closer on average when passing a bicyclist if the rider wore a helmet, potentially increasing the risk of a collision. Olivier and Walter re-analysed the same data in 2013 and claimed helmet wearing was not associated with close vehicle passing. Here we show how Olivier and Walter’s analysis addressed a subtly, but importantly, different question than Walker’s. Their conclusion was based on omitting information about variability in driver behaviour and instead dividing overtakes into two binary categories of ‘close’ and ‘not close’; we demonstrate that they did not justify or address the implications of this choice, did not have sufficient statistical power for their approach, and moreover show that slightly adjusting their definition of ‘close’ would reverse their conclusions. We then present a new analysis of the original dataset, measuring directly the extent to which drivers changed their behaviour in response to helmet wearing. This analysis confirms that drivers did, overall, get closer when the rider wore a helmet. The distribution of overtaking events shifted just over one-fifth of a standard deviation closer to the rider – a potentially important behaviour if, as theoretical frameworks suggest, near-misses and collisions lie on a continuum. The paper ends by considering wider issues surrounding this topic and suggests public health research might be best served by shifting focus to risk elimination rather than harm mitigation.
  • A meta-analysis of the crash risk of cannabis-positive drivers in
           culpability studies—Avoiding interpretational bias
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Ole Rogeberg Background: Culpability studies, a common study design in the cannabis crash risk literature, typically report odds-ratios (OR) indicating the raised risks of a culpable accident. This parameter is of unclear policy relevance, and is frequently misinterpreted as an estimate of the increased crash risk, a practice that introduces a substantial “interpretational bias”.Methods: A Bayesian statistical model for culpability study counts is developed to provide inference for both culpable and total crash risks, with a hierarchical effect specification to allow for meta-analysis across studies with potentially heterogeneous risk parameter values. The model is assessed in a bootstrap study and applied to data from 13 published culpability studies.Results: The model outperforms the culpability OR in bootstrap analyses. Used on actual study data, the average increase in crash risk is estimated at 1.28 (1.16–1.40). The pooled increased risk of a culpable crash is estimated as 1.42 (95% credibility interval 1.11–1.75), which is similar to pooled estimates using traditional ORs (1.46, 95% CI: 1.24–1.72). The attributable risk fraction of cannabis impaired driving is estimated to lie below 2% for all but two of the included studies.Conclusions: Culpability ORs exaggerate risk increases and parameter uncertainty when misinterpreted as total crash ORs. The increased crash risk associated with THC-positive drivers in culpability studies is low.
  • Assessment of the disaster medical response system through an
           investigation of a 43-vehicle mass collision on Jung-ang expressway
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Hee Young Lee, Jeong IL Lee, Oh Hyun Kim, Kang Hyun Lee, Hyeong Tae Kim, Hyun Youk PurposeIt was considered the challenges of the actual response and the potential for improvement, including the activities of the disaster response system, national emergency medical center, and the regional base hospital for the treatment of multiple traffic accident victims. The purpose of this study was to analyze the accident management system through real investigating the multiple collision over 10 vehicles with mass casualty events as a disaster situation.MethodsThis study was retrospective study to analyze the disaster event with multiple collision traffic accident on the expressway in Korea. We visited five medical centers for eight days since the accident occurred and interviewed the injured patients in this accident to examine the health status and medical records. After that, we visited the sixteen car-repair shops in four cities for real investigate about damaged vehicles. According to the arrangement of the accident situation for the accident vehicles through real-world investigation, we reproduced all parts of the accident scene, which were real-world investigated, by the accident situation sketch program. The collected data were summarized by Collision Deformation Classification (CDC) codes, and the medical records of the occupants were assessed using the Injury Severity Score (ISS).ResultsThe cause of the accident was snow freezing of the road. The information about 72 injured patients on 31 damaged vehicles was collected by phone, visit, and actual accident investigation. Of the 72 patients who were examined, 4 were severely injured and 68 were mildly injured. The accident occurred in the order of Sedan 13 (41.9%), SUV 11 (35.5%), Truck 4 (12.9%), Van 2 (6.5%) and Bus 1 (3.2%). The median value of the age [lower quartile and upper quartile] was 43 [34.5–52] years old and the patients included 25 drivers, 11 passengers, 7 back seat passengers, and 29 bus passengers.ConclusionThe primary cause of this mass collision accident was road surface freezing, but the more serious secondary cause was a driver’s inability to avoid the accident scene after the first collision. The severely injured occupants were occurred on the roads outside and inside the vehicle. In the event of a disaster, various teams from the police team, firefighting team, DMAT, EMS, road management team are gathered, and communication and command system between each team is important in order to identify and solve the disaster situation. To do this, it is important to develop manuals and prepare for training through repeated simulations.
  • Safety sensitivity to roadway characteristics: A comparison across highway
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Sikai Chen, Tariq Usman Saeed, Majed Alinizzi, Steven Lavrenz, Samuel Labi This paper examined the accident risk factors associated with highway traffic and roadway design, for each of three highway classes in the United States using a bivariate modeling framework involving two levels of accident severity. With regard to the highest class (Interstates), the results suggest that, compared to no-casualty accidents, casualty accidents are more sensitive to traffic volume and average vertical grade, but less sensitive to the inside shoulder width and the median width. For US Roads, it was determined that, compared to no-casualty accidents, casualty accidents are more sensitive to traffic volume, outside shoulder width, pavement condition, and median width but less sensitive to the average vertical grade. For the relatively lowest-class roads (State Roads), it was determined that, compared to no-casualty accidents, casualty accidents are more sensitive to the traffic volume, lane width, outside shoulder width, and pavement condition. Compared to the relatively lower-class highways, accidents at higher-class highways are more sensitive to: changes in traffic volume, average vertical grade, median width, inside shoulder width, and the pavement condition (no-casualty accidents only); but less sensitive to changes in lane width, pavement condition (casualty accidents only), and the outside shoulder width. This variation in sensitivity across the different road classes could be attributed to the differences in road geometry standards across the road classes, as the results seem to support the hypothesis that these standards strongly influence accident occurrence. It is hoped that the developed bivariate negative binomial models can help highway engineers to evaluate their current design standards and policy, and to assess the safety consequences of changes in these standards in each road class.
  • Exploring the impacts of speed variances on safety performance of urban
           elevated expressways using GPS data
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Chuan Xu, Xuesong Wang, Hong Yang, Kun Xie, Xiaohong Chen Speed variation on urban expressways has been frequently noted as a key factor associated with high crash risk. However, it was often difficult to capture the safety impact of speed variance with spaced sensor measurements. As an alternative, this paper aims to leverage the use of the floating car data (FCD) to capture the speed variance in a morning rush hour on urban elevated expressways and examine its effect on safety. A semi-automatic filtering process was introduced to distinguish taxi GPS data points on the elevated expressways from the ones on the surface roads under the expressways. Subsequently, the standard deviation of the cross-sectional speed mean (SDCSM) and the cross-section speed standard deviation (MCSSD) were derived to capture the spatial and temporal speed variances, respectively. Together with other explanatory variables, both hierarchical and non-hierarchical Poisson-gamma measurement error models were developed to model the crash frequencies of the expressways. The modeling results showed that the hierarchical model performed better and both SDCSM and MCSSD were found to be positively related to the crash occurrence. This secures the need for addressing the impact of speed variation when modeling crashes occurred on the elevated expressways.
  • The association of self-regulation, habit, and mindfulness with texting
           while driving
    • Abstract: Publication date: February 2019Source: Accident Analysis & Prevention, Volume 123Author(s): Melanie M. Moore, Patricia M. Brown The saturation of mobile phones throughout Australia has led to some individuals being unable to regulate their use within situations that are inappropriate or risky. One of the most prevalent risky mobile phone use behaviours is texting while driving. Attempts to explain texting while driving suggest cognitive variables and personality characteristics are key factors. This study explored relationships between trait self-regulation, habitual text messaging, trait mindfulness, and texting while driving. One hundred and seventy participants comprising Australian undergraduate psychology students and members of the public completed an online survey measuring trait self-regulation, habitual text messaging behaviour, trait mindfulness, and frequency of texting while driving. It was found that habitual texting behaviour mediated the relationship between trait self-regulation and frequency of texting while driving. Additionally, trait mindfulness moderated the relationship between habit and texting while driving, such that habitual texting was significantly, positively related to texting while driving, but only for individuals with low to moderate trait mindfulness. These results suggest personality constructs related to attention, awareness, and control of behaviour play a significant role in counteracting the association that habitual texting behaviour has with the frequency of texting while driving. As these traits are considered malleable, this association may be applicable in future development of intervention programs aimed at increasing control over mobile phone use and reducing the frequency with which people text while driving.
  • Full Bayesian conflict-based models for real time safety evaluation of
           signalized intersections
    • Abstract: Publication date: Available online 5 October 2018Source: Accident Analysis & PreventionAuthor(s): Mohamed Essa, Tarek Sayed Existing advanced traffic management and emerging connected vehicles (CVs) technology can generate considerable amount of data on vehicle positions and trajectories. This data can be used for real-time safety optimization of intersections. To achieve this, it is essential to first understand how changes in signal control affect safety in real-time. This paper develops conflict-based safety performance functions (SPFs) of signalized intersections at the cycle level using multiple traffic conflict indicators. The developed SPFs relate various dynamic traffic parameters to the number of rear-end conflicts at the signal cycle. The traffic parameters included: queue length, shock wave speed and area, and the platoon ratio. The Time-to-Collision, the Modified-Time-to-Collision, and the Deceleration Rate to Avoid the Crash were used as traffic conflict indicators. Traffic video-data collected from six signalized intersections was used in the analysis. The SPFs were developed using the Full Bayesian approach to address the unobserved heterogeneity and the variation among different sites. Overall, the results showed that all the developed SPFs have good fit with all explanatory variables being statistically significant. Also, the highest conflict frequency was noticed at the beginning of the green time, while the highest conflict severity was noticed at the beginning of the red time. Lastly, the results can be used most beneficially in real-time safety optimization of signalized intersection.
  • Comparison of univariate and two-stage approaches for estimating crash
           frequency by severity—Case study for horizontal curves on two-lane rural
    • Abstract: Publication date: Available online 1 September 2018Source: Accident Analysis & PreventionAuthor(s): Alireza Jafari Anarkooli, Bhagwant Persaud, Mehdi Hosseinpour, Taha Saleem The Highway Safety Manual (HSM) procedures apply specific safety performance functions (SPFs) and crash modification factors (CMFs) appropriate for estimating the safety effects of design and operational changes to a roadway. Although the applicability of the SPFs and CMFs may significantly vary by crash severity, they are mainly based on total crash counts, with different approaches for estimation of crashes by crash severity. The variety of approaches in the HSM and in the literature in general suggests that there may be no one best approach for all situations, and that there is a need to develop and compare alternative approaches for each potential application. This paper addresses this need by demonstrating the development and comparison of alternative approaches using horizontal curves on two-lane roads as a case study. This site type was chosen because of the high propensity for severe crashes and the potential for exploring a wide range of variables that affect this propensity. To facilitate this investigation, a two-stage modeling approach is developed whereby the proportion of crashes for each severity level is estimated as a function of roadway-specific factors and traffic attributes and then applied to an estimate of total crashes from an SPF. Using Highway Safety Information System (HSIS) curve data for Washington state, a heterogeneous negative binomial (HTNB) regression model is estimated for total crash counts and then applied with severity distribution functions (SDFs) developed by a generalized ordered probit model (GOP). To evaluate the performance of this two-stage approach, a comparison is made with predictions directly obtained from estimated univariate SPFs for crash frequency by severity and also from a fixed proportion method that has been suggested in the HSM. The results revealed that, while the two-stage SDF approach and univariate approach adopt different procedures for model estimation, their prediction accuracies are similar, and both are superior to the fixed proportion method. In short, this study highlights the potential of the two-stage SDF approach in accounting for crash frequency variations by severity levels, at least for curved two-lane road sections, and especially for the all too frequent cases where samples are too small to estimate viable univariate crash severity models.
  • Forecasting German crash numbers: The effect of meteorological variables
    • Abstract: Publication date: Available online 18 August 2018Source: Accident Analysis & PreventionAuthor(s): Kevin Diependaele, Heike Martensen, Markus Lerner, Andreas Schepers, Frits Bijleveld, Jacques J.F. Commandeur At the end of each year, the German Federal Highway Research Institute (BASt) publishes the road safety balance of the closing year. They describe the development of accident and casualty numbers disaggregated by road user types, age groups, type of road and the consequences of the accidents. However, at the time of publishing, these series are only available for the first eight or nine months of the year. To make the balance for the whole year, the last three or four months are forecasted. The objective of this study was to improve the accuracy of these forecasts through structural time-series models that include effects of meteorological conditions. The results show that, compared to the earlier heuristic approach, root mean squared errors are reduced by up to 55% and only two out of the 27 different data series yield a modest rise of prediction errors. With the exception of four data series, prediction accuracies also clearly improve incorporating meteorological data in the analysis. We conclude that our approach provides a valid alternative to provide input to policy makers in Germany.
  • The European road safety decision support system on risks and measures
    • Abstract: Publication date: Available online 18 August 2018Source: Accident Analysis & PreventionAuthor(s): Heike Martensen, Kevin Diependaele, Stijn Daniels, Wouter Van den Berghe, Eleonora Papadimitriou, George Yannis, Ingrid Van Schagen, Wendy Weijermars, Wim Wijnen, Ashleigh Filtness, Rachel Talbot, Pete Thomas, Klaus Machata, Eva Aigner Breuss, Susanne Kaiser, Thierry Hermitte, Rob Thomson, Rune Elvik The European Road Safety Decision Support System ( is an innovative system providing the available evidence on a broad range of road risks and possible countermeasures. This paper describes the scientific basis of the DSS. The structure underlying the DSS consists of (1) a taxonomy identifying risk factors and measures and linking them to each other, (2) a repository of studies, and (3) synopses summarizing the effects estimated in the literature for each risk factor and measure, and (4) an economic efficiency evaluation instrument (E3-calculator). The DSS is implemented in a modern web-based tool with a highly ergonomic interface, allowing users to get a quick overview or go deeper into the results of single studies according to their own needs.
  • Safety assessment of control design parameters through vehicle dynamics
    • Abstract: Publication date: Available online 20 July 2018Source: Accident Analysis & PreventionAuthor(s): Stergios Mavromatis, Alexandra Laiou, George Yannis An existing vehicle dynamics model was utilized to define design parameters up to which steady state cornering conditions apply and consequently lift the restrictions of the point mass model. Aiming to assess critical safety concerns in terms of vehicle skidding, the motion of a passenger car was examined over a range of design speed values paired with control design elements from AASHTO 2011 Design Guidelines as well as certain values of poor pavement friction coefficients.Two distinct cases were investigated; the determination of the maximum attainable constant speed (termed as safe speed) at impending skid conditions as well as the case of comfortable curve negotiation where lower constant speed values were utilized. The overall objective was to define the safety margins for each examined case.From the interaction between road geometry, pavement friction and vehicle characteristics, many interesting findings are reported, where some of them are beyond the confined field of road geometry parameters; such as demanded longitudinal and lateral friction values and horse-power utilization rates. From the road geometry point of view, it was found that control alignments on steep upgrades consisting of low design speed values and combined with poor friction pavements are critical in terms of safety. Such cases should be treated very cautiously through certain actions. These actions include the adoption of acceptable arrangements for the above values regarding new alignments, posted speed management for existing but also scheduling friction improvement programmes more accurately for both cases.
  • A novel method for imminent crash prediction and prevention
    • Abstract: Publication date: Available online 12 July 2018Source: Accident Analysis & PreventionAuthor(s): Zhi Chen, Xiao Qin A crash prediction and prevention method was proposed to detect imminent crash risk and help recommend traffic control strategies to prevent crashes. The method consists of two modules, the crash prediction module and the crash prevention module. The crash prediction module detects crash-prone conditions when the predicted crash probability exceeds a specified threshold. Then the crash prevention module would simulate the safety effect of traffic control alternatives and recommend the optimal one. The proposed method was demonstrated in a case study with variable speed limit (VSL). Results showed that the proposed crash prediction and prevention method could effectively detect crash-prone conditions and evaluate the safety and mobility impacts of various safety countermeasures.
  • Road safety data considerations
    • Abstract: Publication date: Available online 11 July 2018Source: Accident Analysis & PreventionAuthor(s): Loukas Dimitriou, Constantinos Antoniou
  • Use of real-world connected vehicle data in identifying high-risk
           locations based on a new surrogate safety measure
    • Abstract: Publication date: Available online 6 July 2018Source: Accident Analysis & PreventionAuthor(s): Kun Xie, Di Yang, Kaan Ozbay, Hong Yang Traditional methods for the identification of high-risk locations rely heavily on historical crash data. Rich information generated from connected vehicles could be used to obtain surrogate safety measures (SSMs) for risk identification. Conventional SSMs such as time to collision (TTC) neglect the potential risk of car-following scenarios in which the following vehicle’s speed is slightly less than or equal to the leading vehicle’s but the spacing between two vehicles is relatively small that a slight disturbance would yield collision risk. To address this limitation, this study proposes time to collision with disturbance (TTCD) for risk identification. By imposing a hypothetical disturbance, TTCD can capture rear-end conflict risks in various car following scenarios, even when the leading vehicle has a higher speed. Real-world connected vehicle pilot test data collected in Ann Arbor, Michigan is used in this study. A detailed procedure of cleaning and processing the connected vehicle data is presented. Results show that risk rate identified by TTCD can achieve a higher Pearson’s correlation coefficient with rear-end crash rate than other traditional SSMs. We show that high-risk locations identified by connected vehicle data from a relatively shorter time period are similar to the ones identified by using the historical crash data. The proposed method can substantially reduce the data collection time, compared with traditional safety analysis that generally requires more than three years to get sufficient crash data. The connected vehicle data has thus shown the potential to be used to develop proactive safety solutions and the risk factors can be eliminated in a timely manner.
  • Corrigendum to “A farewell to brake reaction times'
           Kinematics-dependent brake response in naturalistic rear-end
           emergencies” [Accid. Anal. Prev. 95 (2016) 209–226]
    • Abstract: Publication date: Available online 15 June 2018Source: Accident Analysis & PreventionAuthor(s): Gustav Markkula, Johan Engström, Johan Lodin, Jonas Bärgman, Trent Victor
  • Evaluation of safety effect of turbo-roundabout lane dividers using
           floating car data and video observation
    • Abstract: Publication date: Available online 1 June 2018Source: Accident Analysis & PreventionAuthor(s): Mariusz Kieć, Jiří Ambros, Radosław Bąk, Ondřej Gogolín Roundabouts are one of the safest types of intersections. However, the needs to meet the requirements of operation, capacity, traffic organization and surrounding development lead to a variety of design solutions. One of such alternatives are turbo-roundabouts, which simplify drivers’ decision making, limit lane changing in the roundabout, and induce low driving speed thanks to raised lane dividers. However, in spite of their generally positive reception, the safety impact of turbo-roundabouts has not been sufficiently studied. Given the low number of existing turbo-roundabouts and the statistical rarity of accident occurrence, the prevalent previously conducted studies applied only simple before-after designs or relied on traffic conflicts in micro-simulations. Nevertheless, the presence of raised lane dividers is acknowledged as an important feature of well performing and safe turbo-roundabouts.Following the previous Polish studies, the primary objective of the present study was assessment of influence of presence of lane dividers on road safety and developing a reliable and valid surrogate safety measure based on field data, which will circumvent the limitations of accident data or micro-simulations. The secondary objective was using the developed surrogate safety measure to assess and compare the safety levels of Polish turbo-roundabout samples with and without raised lane dividers.The surrogate safety measure was based on speed and lane behaviour. Speed was obtained from video observations and floating car data, which enabled the construction of representative speed profiles. Lane behaviour data was gathered from video observations.The collection of the data allowed for a relative validation of the method by comparing the safety performance of turbo-roundabouts with and without raised lane dividers. In the end, the surrogate measure was applied for evaluation of safety levels and enhancement of the existing safety performance functions, which combine traffic volumes, and speeds as a function of radii). The final models may help quantify the safety impact of different turbo-roundabout solutions.
  • Evaluation of surrogate measures for pedestrian trips at intersections and
           crash modeling
    • Abstract: Publication date: Available online 31 May 2018Source: Accident Analysis & PreventionAuthor(s): Jaeyoung Lee, Mohamed Abdel-Aty, Imran Shah Pedestrians are considered the most vulnerable road users who are directly exposed to traffic crashes. With a view to addressing the growing concern of pedestrian safety, Federal and local governments aim at reducing pedestrian-involved crashes. Nevertheless, pedestrian volume data are rarely available even though they among the most important factors to identify pedestrian safety. Thus, this study aims at identifying surrogate measures for pedestrian exposure at intersections. A two-step process is implemented: the first step is the development of Tobit and generalized linear models for predicting pedestrian trips (i.e., exposure models). In the second step, negative binomial and zero inflated negative binomial models were developed for pedestrian crashes using the predicted pedestrian trips. The results indicate that among various exposure models the Tobit model performs the best in describing pedestrian exposure. The identified exposure-relevant factors are the presence of schools, car-ownership, pavement condition, sidewalk width, bus ridership, intersection control type and presence of sidewalk barrier. It was also found that the negative binomial model with the predicted pedestrian trips and that with the observed pedestrian trips perform equally well for estimating pedestrian crashes. Also, the difference between the observed and the predicted pedestrian trips does not appear as statistically significant, according to the results of the t-test and Wilcoxon signed-rank test. It is expected that the methodologies using predicted pedestrian trips or directly including pedestrian surrogate exposure variables can estimate safety performance functions for pedestrian crashes even though when pedestrian trip data is not available.
  • 10th International Conference on managing fatigue: Managing fatigue to
           improve safety, wellness, and effectiveness
    • Abstract: Publication date: Available online 19 May 2018Source: Accident Analysis & PreventionAuthor(s): Jeffrey S. Hickman, Richard J. Hanowski, Jana Price, J. Erin Mabry
  • How much is left in your “sleep tank”' Proof of concept for a
           simple model for sleep history feedback
    • Abstract: Publication date: Available online 7 May 2018Source: Accident Analysis & PreventionAuthor(s): Jillian Dorrian, Steven Hursh, Lauren Waggoner, Crystal Grant, Maja Pajcin, Charlotte Gupta, Alison Coates, David Kennaway, Gary Wittert, Leonie Heilbronn, Chris Della Vedova, Siobhan Banks Technology-supported methods for sleep recording are becoming increasingly affordable. Sleep history feedback may help with fatigue-related decision making – Should I drive' Am I fit for work' This study examines a “sleep tank” model (SleepTank™), which is analogous to the fuel tank in a car, refilled by sleep, and depleted during wake. Required inputs are sleep period time and sleep efficiency (provided by many consumer-grade actigraphs). Outputs include suggested hours remaining to “get sleep” and percentage remaining in tank (Tank%). Initial proof of concept analyses were conducted using data from a laboratory-based simulated nightshift study. Ten, healthy males (18–35y) undertook an 8h baseline sleep opportunity and daytime performance testing (BL), followed by four simulated nightshifts (2000 h–0600 h), with daytime sleep opportunities (1000 h–1600 h), then an 8 h night-time sleep opportunity to return to daytime schedule (RTDS), followed by daytime performance testing. Psychomotor Vigilance Task (PVT) and Karolinska Sleepiness Scale were performed at 1200 h on BL and RTDS, and at 1830 h, 2130 h 0000 h and 0400 h each nightshift. A 40-minute York Driving Simulation was performed at 1730 h, 2030 h and 0300 h on each nightshift. Model outputs were calculated using sleep period timing and sleep efficiency (from polysomnography) for each participant. Tank% was a significant predictor of PVT lapses (p 
  • Effects of strategic early-morning caffeine gum administration on
           association between salivary alpha-amylase and neurobehavioural
           performance during 50 h of sleep deprivation
    • Abstract: Publication date: Available online 7 May 2018Source: Accident Analysis & PreventionAuthor(s): Maja Pajcin, Jason M White, Siobhan Banks, Jill Dorrian, Gemma M Paech, Crystal L Grant, Kayla Johnson, Katie Tooley, Eugene Aidman, Justin Fidock, Gary H Kamimori, Chris B Della Vedova Self-assessment is the most common method for monitoring performance and safety in the workplace. However, discrepancies between subjective and objective measures have increased interest in physiological assessment of performance. In a double-blind placebo-controlled study, 23 healthy adults were randomly assigned to either a placebo (n = 11; 5 F, 6 M) or caffeine condition (n = 12; 4 F, 8 M) while undergoing 50 h (i.e. two days) of total sleep deprivation. In previous work, higher salivary alpha-amylase (sAA) levels were associated with improved psychomotor vigilance and simulated driving performance in the placebo condition. In this follow-up article, the effects of strategic caffeine administration on the previously reported diurnal profiles of sAA and performance, and the association between sAA and neurobehavioural performance were investigated. Participants were given a 10 h baseline sleep opportunity (monitored via standard polysomnography techniques) prior to undergoing sleep deprivation (total sleep time: placebo = 8.83 ± 0.48 h; caffeine = 9.01 ± 0.48 h). During sleep deprivation, caffeine gum (200 mg) was administered at 01:00 h, 03:00 h, 05:00 h, and 07:00 h to participants in the caffeine condition (n = 12). This strategic administration of caffeine gum (200 mg) has been shown to be effective at maintaining cognitive performance during extended wakefulness. Saliva samples were collected, and psychomotor vigilance and simulated driving performance assessed at three-hour intervals throughout wakefulness. Caffeine effects on diurnal variability were compared with previously reported findings in the placebo condition (n = 11). The impact of caffeine on the circadian profile of sAA coincided with changes in neurobehavioural performance. Higher sAA levels were associated with improved performance on the psychomotor vigilance test during the first 24 h of wakefulness in the caffeine condition. However, only the association between sAA and response speed (i.e. reciprocal-transform of mean reaction time) was consistent across both days of sleep deprivation. The association between sAA and driving performance was not consistent across both days of sleep deprivation. Results show that the relationship between sAA and reciprocal-transform of mean reaction time on the psychomotor vigilance test persisted in the presence of caffeine, however the association was relatively weaker as compared with the placebo condition.
  • School start times and teenage driver motor vehicle crashes
    • Abstract: Publication date: Available online 26 April 2018Source: Accident Analysis & PreventionAuthor(s): Robert D. Foss, Richard L. Smith, Natalie P. O'Brien IntroductionShifting school start times to 8:30 am or later has been found to improve academic performance and reduce behavior problems. Limited research suggests this may also reduce adolescent driver motor vehicle crashes. A change in the school start time from 7:30 am to 8:45 am for all public high schools in one North Carolina county presented the opportunity to address this question with greater methodologic rigor.MethodWe conducted ARIMA interrupted time-series analyses to examine motor vehicle crash rates of high school age drivers in the intervention county and 3 similar comparison counties with comparable urban-rural population distribution. To focus on crashes most likely to be affected, we limited analysis to crashes involving 16- & 17-year-old drivers occurring on days when school was in session.ResultsIn the intervention county, there was a 14% downward shift in the time-series following the 75 min delay in school start times (p = .076). There was no change approaching statistical significance in any of the other three counties. Further analysis indicated marked, statistically significant shifts in hourly crash rates in the intervention county, reflecting effects of the change in school start time on young driver exposure. Crashes from 7 to 7:59 am decreased sharply (−25%, p = .008), but increased similarly from 8 to 8:59 am (21%, p = .004). Crashes from 2 to 2:59 pm declined dramatically (−48%, p = .000), then increased to a lesser degree from 3 to 3:59 pm (32%, p = .024) and non-significantly from 4 to 4:59 (19%, p = .102). There was no meaningful change in early morning or nighttime crashes, when drowsiness-induced crashes might have been expected to be most common.DiscussionThe small decrease in crashes among high school age drivers following the shift in school start time is consistent with the findings of other studies of teen driver crashes and school start times. All these studies, including the present one, have limitations, but the similar findings suggest that crashes and school start times are indeed related, with earlier start times equating to more crashes.ConclusionLater high school start times (>8:30 am) appear to be associated with lower adolescent driver crash rates, but additional research is needed to confirm this and to identify the mechanism by which this occurs (reduced drowsiness or reduced exposure).
  • Drowsiness measures for commercial motor vehicle operations
    • Abstract: Publication date: Available online 26 April 2018Source: Accident Analysis & PreventionAuthor(s): Amy R. Sparrow, Cynthia M. LaJambe, Hans P.A. Van Dongen Timely detection of drowsiness in Commercial Motor Vehicle (C MV) operations is necessary to reduce drowsiness-related CMV crashes. This is relevant for manual driving and, paradoxically, even more so with increasing levels of driving automation. Measures available for drowsiness detection vary in reliability, validity, usability, and effectiveness. Passively recorded physiologic measures such as electroencephalography (EEG) and a variety of ocular parameters tend to accurately identify states of considerable drowsiness, but are limited in their potential to detect lower levels of drowsiness. They also do not correlate well with measures of driver performance. Objective measures of vigilant attention performance capture drowsiness reliably, but they require active driver involvement in a performance task and are prone to confounds from distraction and (lack of) motivation. Embedded performance measures of actual driving, such as lane deviation, have been found to correlate with physiologic and vigilance performance measures, yet to what extent drowsiness levels can be derived from them reliably remains a topic of investigation. Transient effects from external circumstances and behaviors – such as task load, light exposure, physical activity, and caffeine intake – may mask a driver’s underlying state of drowsiness. Also, drivers differ in the degree to which drowsiness affects their driving performance, based on trait vulnerability as well as age. This paper provides a broad overview of the current science pertinent to a range of drowsiness measures, with an emphasis on those that may be most relevant for CMV operations. There is a need for smart technologies that in a transparent manner combine different measurement modalities with mathematical representations of the neurobiological processes driving drowsiness, that account for various mediators and confounds, and that are appropriately adapted to the individual driver. The research for and development of such technologies requires a multi-disciplinary approach and significant resources, but is technically within reach.
  • Effects of methodological decisions on rainfall-related crash relative
           risk estimates
    • Abstract: Publication date: Available online 23 April 2018Source: Accident Analysis & PreventionAuthor(s): Alan W. Black, Gabriele Villarini Numerous studies have examined the influence of rainfall on the relative risk of crash, and they all agree that rainfall leads to an increase in relative risk as compared to dry conditions; what they do not agree on is the magnitude of these increases. Here we consider three methodological decisions made in computing the relative risk and examine their impacts: the inclusion or exclusion of zero total events (where no crashes occur during event or control periods), the temporal scale of analysis, and the use of information on pavement and weather conditions contained with the crash reports to determine relative risk. Our analyses are based on several years of data from six U.S. states (Arkansas, Georgia, Illinois, Maryland, Minnesota and Ohio). Zero total events in the context of weather related crash studies typically provide no information on the actual crash odds and greatly alter the distribution of relative risk estimates and should be removed from the analysis. While the use of a daily time step provides an estimate of relative risk that is not significantly different from an hourly time step for the majority of rural counties in our study area, the same is true of only 39% of the urban counties. Finally, the use of pavement and weather condition information from the crash reports results in relative risk estimates that are lower than the standard approach, however this difference decreases as rainfall totals increase. By highlighting the influence of methodological choices, we hope to pave the way towards the potential reduction in uncertainties in weather-related relative risk estimates.
  • Implications of estimating road traffic serious injuries from hospital
    • Abstract: Publication date: Available online 19 April 2018Source: Accident Analysis & PreventionAuthor(s): K. Pérez, W. Weijermars, N. Bos, A.J. Filtness, R. Bauer, H. Johannsen, N. Nuyttens, L. Pascal, P. Thomas, M. Olabarria, The Working group of WP7 project To determine accurately the number of serious injuries at EU level and to compare serious injury rates between different countries it is essential to use a common definition. In January 2013, the High Level Group on Road Safety established the definition of serious injuries as patients with an injury level of MAIS3+(Maximum Abbreviated Injury Scale). Whatever the method used for estimating the number or serious injuries, at some point it is always necessary to use hospital records. The aim of this paper is to understand the implications for (1) in/exclusion criteria applied to case selection and (2) a methodological approach for converting ICD (International Classification of Diseases/Injuries) to MAIS codes, when estimating the number of road traffic serious injuries from hospital data. A descriptive analysis with hospital data from Spain and the Netherlands was carried out to examine the effect of certain choices concerning in- and exclusion criteria based on codes of the ICD9-CM and ICD10. The main parameters explored were: deaths before and after 30 days, readmissions, and external injury causes. Additionally, an analysis was done to explore the impact of using different conversion tools to derive MAIS3 + using data from Austria, Belgium, France, Germany, Netherlands, and Spain. Recommendations are given regarding the in/exclusion criteria and when there is incomplete data to ascertain a road injury, weighting factors could be used to correct data deviations and make more real estimations.
  • Risk management in port and maritime logistics
    • Abstract: Publication date: Available online 11 April 2018Source: Accident Analysis & PreventionAuthor(s): Jasmine Siu Lee Lam, Y.H. Venus Lun, Michael G.H. Bell
  • Dangerous intersections' A review of studies of fatigue and
           distraction in the automated vehicle
    • Abstract: Publication date: Available online 10 April 2018Source: Accident Analysis & PreventionAuthor(s): Gerald Matthews, Catherine Neubauer, Dyani J. Saxby, Ryan W. Wohleber, Jinchao Lin The impacts of fatigue on the vehicle driver may change with technological advancements including automation and the increasing prevalence of potentially distracting in-car systems. This article reviews the authors’ simulation studies of how fatigue, automation, and distraction may intersect as threats to safety. Distinguishing between states of active and passive fatigue supports understanding of fatigue and the development of countermeasures. Active fatigue is a stress-like state driven by overload of cognitive capabilities. Passive fatigue is produced by underload and monotony, and is associated with loss of task engagement and alertness. Our studies show that automated driving reliably elicits subjective symptoms of passive fatigue and also loss of alertness that persists following manual takeover. Passive fatigue also impairs attention and automation use in operators of Remotely Piloted Vehicles (RPVs). Use of in-vehicle media has been proposed as a countermeasure to fatigue, but such media may also be distracting. Studies tested whether various forms of phone-based media interacted with automation-induced fatigue, but effects were complex and dependent on task configuration. Selection of fatigue countermeasures should be guided by an understanding of the form of fatigue confronting the operator. System design, regulation of level of automation, managing distraction, and selection of fatigue-resilient personnel are all possible interventions for passive fatigue, but careful evaluation of interventions is necessary prior to deployment.
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Heriot-Watt University
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