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Publisher: Elsevier   (Total: 3161 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: 33, SJR: 1.655, CiteScore: 2)
Academic Radiology     Hybrid Journal   (Followers: 23, SJR: 1.015, CiteScore: 2)
Accident Analysis & Prevention     Partially Free   (Followers: 94, SJR: 1.462, CiteScore: 3)
Accounting Forum     Hybrid Journal   (Followers: 25, SJR: 0.932, CiteScore: 2)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 34, 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: 412, 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: 249, 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: 2, 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: 6)
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: 16, SJR: 1.29, CiteScore: 3)
Addictive Behaviors Reports     Open Access   (Followers: 8, SJR: 0.755, CiteScore: 2)
Additive Manufacturing     Hybrid Journal   (Followers: 9, SJR: 2.611, CiteScore: 8)
Additives for Polymers     Full-text available via subscription   (Followers: 22)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 147, SJR: 4.09, CiteScore: 13)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 11, SJR: 1.167, CiteScore: 4)
Advanced Powder Technology     Hybrid Journal   (Followers: 16, 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: 12, 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: 22, 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: 31, SJR: 3.043, CiteScore: 6)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 8, 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: 3)
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: 29, 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: 11)
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: 24)
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: 7)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 44, 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: 56, 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: 21, 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: 9)
Advances in Marine Biology     Full-text available via subscription   (Followers: 16, 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: 6, 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: 21)
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: 1)
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: 10)
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: 9)
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: 18)
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: 62)
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: 5)
Advances in Space Research     Full-text available via subscription   (Followers: 397, 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: 10, SJR: 0.555, CiteScore: 2)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 31, SJR: 2.208, CiteScore: 4)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 17)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 13)
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: 342, 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: 445, 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: 32, SJR: 1.156, CiteScore: 4)
Agricultural Water Management     Hybrid Journal   (Followers: 44, SJR: 1.272, CiteScore: 3)
Agriculture and Agricultural Science Procedia     Open Access   (Followers: 2)
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: 11, SJR: 1.153, CiteScore: 3)
Alcoholism and Drug Addiction     Open Access   (Followers: 9)
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: 11, 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: 9, SJR: 0.201, CiteScore: 1)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 50, 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: 50, SJR: 3.267, CiteScore: 4)
American J. of Cardiology     Hybrid Journal   (Followers: 54, SJR: 1.93, CiteScore: 3)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 45, 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: 28, SJR: 1.062, CiteScore: 2)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 34, SJR: 2.973, CiteScore: 4)
American J. of Medicine     Hybrid Journal   (Followers: 46)
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: 205, SJR: 2.7, CiteScore: 4)
American J. of Ophthalmology     Hybrid Journal   (Followers: 62, 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: 27, SJR: 2.139, CiteScore: 4)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 28, 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: 6)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.144, CiteScore: 3)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 62, SJR: 0.138, CiteScore: 0)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 17, 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: 43, SJR: 1.512, CiteScore: 5)
Analytical Biochemistry     Hybrid Journal   (Followers: 176, SJR: 0.633, CiteScore: 2)
Analytical Chemistry Research     Open Access   (Followers: 11, SJR: 0.411, CiteScore: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 11)
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: 189, 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: 94  
 
  Partially Free Journal Partially Free Journal
ISSN (Print) 0001-4575
Published by Elsevier Homepage  [3161 journals]
  • Employee safety single vs. dual priorities: When is the rate of
           work-related driving accidents lower'
    • Abstract: Publication date: December 2018Source: Accident Analysis & Prevention, Volume 121Author(s): Ron Aaron Malka, Shalhevet Leibovitz-Zur, Eitan NavehAbstractApplying both occupational safety and ambidexterity theories, we investigate which situation in organizations leads to a lower number of work-related road accidents: a single-priority situation focused on road safety, or a dual-priority situation in which both road safety and customer service are priorities. Occupational safety theory puts forward an ‘either-or’ approach in which employee safety must be the first priority, above and beyond all others. In contrast, the ambidexterity theory’s ‘both-and’ approach suggests a simultaneous coexistence of priorities. Results from forty-three units in three organizations that make intensive use of work-related driving and aim to deliver good customer service are described. The results suggest that when the level of customer service priority was low, an increase in the level of road safety priority significantly decreased the number of road accidents. However, when the level of customer service priority was high, an increase in the level of road safety priority was not associated with less road accidents. The results show that work-related road accidents would be lower in a safety-first, single-priority situation compared to a dual-priority situation encompassing both road safety and customer service. We discuss the theoretical and practical implications of these results.
       
  • The relationship between the demographic, personal, and social factors of
           Malaysian motorcyclists and risk taking behavior at signalized
           intersections
    • Abstract: Publication date: December 2018Source: Accident Analysis & Prevention, Volume 121Author(s): Muhamad Nazri Borhan, Ahmad Nazrul Hakimi Ibrahim, Affan Aziz, Muhamad Razuhanfi Mat YazidAbstractIn the context of road safety, risk-taking is undoubtedly one of the main contributory factors in road accidents. The actual forces which influence individuals to take such risks, nevertheless, are still not fully understood. To address this, this study was therefore conducted to investigate the relationship of the demographic, personal, and social factors of motorcyclists, with a specific focus on their risk-taking behavior at signalized intersections in Malaysia. This study adopted the quantitative method using cross-sectional questionnaire surveys and involved 251 respondents. The demographic factors were analyzed using the t-test and an ANOVA Scheffe Post-Hoc test, while the motorcyclists’ personal and social characteristics were analyzed with multiple linear regression. The findings indicate that the individuals who were greater risk takers at signalized intersections were teenage motorcyclists (16–25 years old) who had finished their education before taking their high school diploma, and who also received a lower than average monthly income from private sector firms. The actual experience of accidents was also shown to be positively related to this risk-taking behavior. In addition, in term of personal and social factors, results showed that, for these individuals, there was a significant difference between the strength of peer influence and that of parental and spouse guidance. However, there was no significant difference in the risk-taking behavior of Malaysian motorcyclists riding at signalized intersections for the following factors: between genders, in terms of accident involvement, in terms of enforcement of traffic regulations, and prevention steps and confidence level after being involved in an accident.
       
  • The effect of gender, occupation and experience on behavior while driving
           on a freeway deceleration lane based on field operational test data
    • Abstract: Publication date: December 2018Source: Accident Analysis & Prevention, Volume 121Author(s): Nengchao Lyu, Yue Cao, Chaozhong Wu, Jin Xu, Lian XieAbstractDeceleration lanes improve traffic flow by reducing interference, increasing capacity and enhancing safety. However, accident rates are higher on these interchange segments than on other freeway segments. It is important to attempt to reduce traffic accidents on these interchange segments by further exploring the behavior of different types of drivers on a highway deceleration lane. In this study, with field operational test (FOT) data from 89 driving instances (derived from 46 participants driving the test road twice) on a typical freeway deceleration lane, section speed profiles, vehicle trajectories, lane position and other key parameters were obtained. The lane-change characteristics and speed profiles of drivers with different genders, occupations and experiences were analyzed. The significant disparities between them reflects the risk associated with different groups of drivers. The study shows that male drivers changed to the outside lane earlier; professional drivers and experienced drivers made the last lane change as early as possible to enter the deceleration lane; and the speed of the vehicles entering the exit ramp was significantly higher than the speed limit. This research work provides ground truth data for deceleration lane design, driver ability training and off-ramp traffic safety management.
       
  • Reducing traffic violations in minority localities: Designing a traffic
           enforcement program through a public participation process
    • Abstract: Publication date: December 2018Source: Accident Analysis & Prevention, Volume 121Author(s): Roni FactorAbstractThe current study tests an innovative public participation process for designing and implementing a tailored traffic enforcement program in minority localities. The quasi-experiment used two matched pairs of randomly selected Israeli Arab localities, where one locality in each pair was randomly assigned to the experimental group and the other to the control group. The intervention’s main features were the public participation process and implementation by police of the traffic enforcement program designed during the process. Systematic field observations on 12,236 vehicles in the four localities found a meaningful and significant reduction in traffic violations in the experimental localities following the intervention, while a small increase in violations was observed in the control localities. The most meaningful decline, indicating improvement in drivers’ behavior, was in non-use of seatbelts and small children in the front seat. The study suggests that a public participation process which identifies local road traffic problems and “dark” hot spots (places where offenses and risky behavior recur but might not be known to the police), followed by implementing tailored solutions for these problems, can reduce traffic violations. Future research should aim to separate out the independent effects of the two phases (the public participation process and tailored enforcement).
       
  • The consumer-citizen duality: Ten reasons why citizens prefer safety and
           drivers desire speed
    • Abstract: Publication date: December 2018Source: Accident Analysis & Prevention, Volume 121Author(s): Niek Mouter, Sander van Cranenburgh, Bert van WeeAbstractCost-benefit analyses for transportation projects usually value impacts on safety and travel time through experiments in which consumers of mobility (‘drivers’) choose between routes which differ in safety and travel time. This approach has been criticized for failing to consider that private choices may not fully reflect citizens’ preferences over public goods and means, a concept known as the consumer-citizen duality. Recent empirical evidence has established that individuals do indeed assign comparatively more value to safety in their role as citizens than in their role as drivers. Our study aims to provide explanations for this finding by presenting four stated choice experiments in which respondents were asked to make choices, both as citizens and as drivers, between routes that differed in travel time and safety. Subsequently, respondents were asked to provide reasons for their choices. We identify five cognitive and five normative explanations. The cognitive explanations suggest that individuals make diverging choices because their perceptions of accident risk differ between the two roles. Drivers will assign a relatively low value to mitigating accident risk because they believe that: (1) such risks are trivial on an individual level; (2) their personal risk is lower than the average risk; (3) their personal risk is controllable; (4) they would not be able to distinguish relative safety levels in real life; and (5) their choices for others are more risk-averse than choices for themselves and, unlike citizens, they are not explicitly evaluating risky choices for others. The normative explanations involve that individuals believe that the government should assign more value to safety compared to individual drivers because: (6) as citizen they are more prone to base their choices on social norms which prescribe risk-averse behaviour in this context; (7) governments have a duty of care concerning the safety of the transportation network; (8) drivers have a relatively high degree of responsibility to reduce their own travel times; (9) governments should account for drivers’ tendencies to choose faster routes by building safer ones; and (10) governments should ensure the safety of the road network because this allows drivers to choose the fastest route without being concerned about the impact of their route choice on accident risk.
       
  • Safety and operational impacts of setting speed limits below engineering
           recommendations
    • Abstract: Publication date: December 2018Source: Accident Analysis & Prevention, Volume 121Author(s): Vikash V. Gayah, Eric T. Donnell, Zhengyao Yu, Lingyu LiAbstractThis study quantifies the operational and safety impacts of setting posted speed limits below engineering recommendations using field data from rural roads in Montana. Vehicle operating speeds and historical crash data were collected at multiple sites with posted speed limits set equal to engineering recommendations and sites with posted speed limits set lower than engineering recommendations. Linear, quantile and logistic regression models were estimated to predict mean operating speed, 85th percentile operating speed and speed limit compliance, respectively, as a function of various roadway characteristics and level of speed enforcement. The Empirical-Bayes before-after approach was also used to develop crash modification factors (CMFs) that describe the expected change in total and fatal + injury crash frequency when setting posted speed limits lower than engineering recommendations. Because safety data were collected over a long time period, temporal adjustments were incorporated to account for yearly changes in crash reporting, traffic characteristics and other variables. The results revealed that speed limit compliance worsened as the difference between the engineering recommended and posted speed limits increased. The presence of verified heavy police enforcement reduced both mean and 85th-percentile operating speeds by approximately 4 mph and increased speed limit compliance. The safety analysis found a statistically significant reduction in total, fatal + injury, and property damage only (PDO) crash frequency at locations with posted speed limits set 5 mph lower than engineering recommendations. Locations with posted speed limits set 10 mph lower than engineering recommendations experienced a decrease in total and PDO crash frequency, but an increase in fatal + injury crash frequency. The safety effects of setting speed limits 15 to 25 mph lower than engineering recommendations were less clear, as the results were not statistically significant, likely due to the small sample of sites included in the evaluation. Overall, the results suggest that setting posted speed limits 5 mph lower than the engineering recommended practice may result in operating speeds that are more consistent with the posted speed limits and overall safety benefits.
       
  • From partial and high automation to manual driving: Relationship between
           non-driving related tasks, drowsiness and take-over performance
    • Abstract: Publication date: December 2018Source: Accident Analysis & Prevention, Volume 121Author(s): Frederik Naujoks, Simon Höfling, Christian Purucker, Kathrin ZeebAbstractBackgroundUntil the level of full vehicle automation is reached, users of vehicle automation systems will be required to take over manual control of the vehicle occasionally and stay fallback-ready to some extent during the drive. Both, drowsiness caused by inactivity and the engagement in distracting non-driving related tasks (NDRTs) such as entertainment or office work have been suggested to impair the driver’s ability to safely handle these transitions of control. Thus, it is an open question whether engagement in NDRTs will impair or improve take-over performance.MethodIn a motion-based driving simulator, 64 participants completed an automated drive that lasted either one or two hours using either a partially or highly automated driving system. In the partially automated driving condition, a warning was issued after several seconds when drivers took both hands off the steering wheel, while the highly automated driving system allowed hands-off driving permanently. Drivers were allowed to bring along their smartphones and to use them during the drive. They engaged in a wide variety of NDRTs such as reading or using social media. At the end of the session, drivers had to react to a sudden lead vehicle braking event. In the partial automation condition, there was no take-over request (TOR) to notify the drivers of the braking vehicle, while in the highly automated condition, the situation happened right after the drivers had deactivated the automation in response to a TOR. The lead time of the TOR was set at 8 s. Driver’s level of drowsiness, workload (visual, mental and motoric) from carrying out the NDRT and motivational appeal of the NDRT right before the control transition were video-coded and used to predict the outcome of the braking event (i.e., reaction and system deactivation times, minimal Time-to-collision (TTC) and self-reported criticality) with a multiple regression approach.ResultsIn the partial automation condition, reaction times to the braking vehicle and situation criticality as measured by the minimum TTC could be well predicted. Main predictors for increased reaction time were drowsiness and motivational appeal of the NDRT. However, visual and mental demand associated with NDRTs did decrease reaction time, suggesting that the NDRT helped the drivers to maintain alertness during the partially automated drive. Accordingly, drowsiness and motivational appeal of the NDRT increased situation criticality, while cognitive load due to the NDRT decreased it. In the highly automated condition, however, it was not possible to predict system deactivation time (in reaction to the TOR), brake reaction time to the braking vehicle and situation criticality by observed drowsiness and NDRT engagement.DiscussionThe results suggest a relationship between the driver’s drowsiness and NDRT engagement in partial automation but not in highly automated driving. Several explanations for this finding are discussed. It could be possible that the lead time of 8 s might have given the drivers enough time to complete the driver state transition process from executing NDRTs to manual driving, putting them in a position to be able to cope with the driving event, while this was not possible in the partial automation condition. Methodological issues that might have led to a non-detection of an effect of drowsiness or NDRT engagement in the highly automated driving condition, such as the sample size and sensitivity of the observer ratings, are also discussed.
       
  • Multivariate random parameter Tobit modeling of crashes involving aging
           drivers, passengers, bicyclists, and pedestrians: Spatiotemporal
           variations
    • Abstract: Publication date: December 2018Source: Accident Analysis & Prevention, Volume 121Author(s): Mehmet Baran Ulak, Eren Erman Ozguven, Omer Arda Vanli, Maxim A. Dulebenets, Lisa SpainhourAbstractThe increase in 65 years and older population in the United States compels the investigation of the crashes involving all aging (65+) roadway users (drivers, passengers, bicyclists, and pedestrians) in order to ensure their safety. As such, the objective of this research is to provide a spatiotemporal comparative investigation of the crashes involving these aging roadway users in Florida via concurrently using the same set of predictors in order to obtain comparable findings among them. First, a new metric, namely Crash Rate Difference (CRD) approach is developed, which enables one to capture potential spatial and temporal (e.g., weekend and weekday) variations in crash rates of aging user-involved crashes. Second, a multivariate random parameter Tobit model is utilized to determine the factors that drive both the crash occurrence probability and the crash rate of 65+ roadway users, accounting for the unobserved heterogeneity. Findings show that there are statistically significant heterogeneous effects of predictors on the crash rates of different roadway users, which evidences the unobserved heterogeneity across observations. Results also indicate that the presence of facilities such as hospitals, religious facilities, or supermarkets is very influential on crash rates of 65+ roadway users, advocating that roadways around these facilities should be particularly scrutinized by road safety stakeholders. Interestingly, the effect of these facilities on crashes also differs significantly between weekdays and weekends. Moreover, the roadway segments with high crash rates vary temporally depending on whether it is a weekday or a weekend. These findings regarding the spatiotemporal variations clearly indicate the need to develop and design better traffic safety measures and plans addressing these specific roadway segments, which can be tailored to alleviate traffic safety problems for 65+ roadway users.
       
  • The effects of personality types on self-reported safety behavior: Focused
           on plant workers in Korea
    • Abstract: Publication date: December 2018Source: Accident Analysis & Prevention, Volume 121Author(s): Lee Jong-Hyun, So Soo-Hyun, Min Seung-Nam, Lee Kyung-SunAbstractWe sought to validate a safety behavior tool used in South Korean nuclear power plants, and to investigate the effects of HEXACO personality types on safety behaviors. The participants were 242 individuals employed in corporate safety management who answered the questionnaires on safety behaviors checklist, impulsiveness, affectivity, job burnout, and perfectionism. An exploratory factor analysis was conducted on the safety behavior items, and the convergent and discriminant validity were confirmed through correlational analyses with the existing related variables. To examine the individual effects of personality variables on the validated safety behavior questionnaire, we introduced control variables into a subsequent hierarchical regression analysis. The analyses revealed that the personality variables had significant effects on the subscales of the safety behavior scale. The present study is significant in that it revealed that personality, a broad construct, can predict human errors and safety behaviors, which have had previously been found to associate with only specific variables, such as stress, impulsiveness, and perfectionism.
       
  • Impact of data aggregation approaches on the relationships between
           operating speed and traffic safety
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Rongjie Yu, Mohammed Quddus, Xuesong Wang, Kui YangAbstractThe impact of operating speed on traffic crash occurrence has been a controversial topic in the traffic safety discipline as some studies reported a positive association whereas others indicated a negative relationship between speed and crashes. Two major issues thought to be accountable for such conflicting findings are the application of inappropriate statistical methods and the use of sample datasets with varying levels of aggregation. The main objective of this study is therefore to investigate the impacts of data aggregation schemes on the relationships between operating speed and traffic safety. A total of three aggregation approaches were examined: (1) a segment-based dataset in which crashes are grouped by roadway segment, (2) a scenario-based dataset where crashes are aggregated by traffic operating scenarios, and (3) a disaggregated crash-level dataset consisting of information from individual crashes. The first two aggregation approaches were used in examining the relationships between operating speed and crash frequency using Bayesian random-effects negative binomial models. The third disaggregated crash risk analysis was conducted utilizing Bayesian random-effects logistic regression models. From the modeling results, it has been concluded that the scenario-based approach shared similar findings with those of the disaggregated crash risk analysis approach in which a U-shaped relationship between operating speed and crash occurrence was identified. However, the commonly adopted segment-based aggregation approach revealed a monotonous negative relationship between speed and crash frequency. The implications of the different analyses results and the potential applications of the results on speed management systems have therefore been discussed.
       
  • Effects of modal shares on crash frequencies at aggregate level
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Mehdi Mohammadi, Gholamali Shafabakhsh, Ali NaderanAbstractIntroductionLong-range transportation plans often involve proposals for improvements/ changes in different modes of travel. This means that modal share of trips generated at each traffic analysis zone (TAZ) by mode of travel needs to be predicted/ forecasted for safety evaluation purposes. The objective of this research study is to develop a series of aggregate crash prediction models (ACPMs) that relate with the modal split step of the conventional four-step demand models.MethodThe models are developed utilizing network and vehicular, socio-economical, trip production/attraction and trip frequencies by mode at TAZ-level as explanatory variables in a generalized linear regression with the assumption of a negative binomial error structure. Crash frequencies are split into total crashes (TC) and severe crashes (SC).ResultsThe models prove promising in estimating crash frequencies upon changes in modal shares, which is essential in safety assessment of alternate transportation demand management (TDM) scenarios. Trips made in car, bus, and bus Service mode became significant in the estimated TC and trips made in car, taxi, school service, bus service and moped mode became significant in the estimated SC ACPMs.ConclusionsThe ACPMs may be used from two different points of view. First and most appropriate use is to consider these as tools to forecast future crash frequencies and develop long-term plans to counteract. In the second point of view, ACPMs act as the primary planning tool to identify how any increase in a specific mode-ridership will contribute to crash frequencies. This is of great interest in developing plans that involve increased use of a specific mode.Practical applicationAs modal shares are forecasted in certain years into the future by the modal split step of demand modeling, crash frequencies could also be forecasted and safety implications of mobility improvement scenarios (e.g. increased number of trips by bus, car, etc.) would be evaluated.
       
  • Comparison of univariate and two-stage approaches for estimating crash
           frequency by severity—Case study for horizontal curves on two-lane rural
           roads
    • Abstract: Publication date: Available online 1 September 2018Source: Accident Analysis & PreventionAuthor(s): Alireza Jafari Anarkooli, Bhagwant Persaud, Mehdi Hosseinpour, Taha SaleemAbstractThe 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.
       
  • Understanding the effects of trip patterns on spatially aggregated crashes
           with large-scale taxi GPS data
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Jie Bao, Pan Liu, Xiao Qin, Huaguo ZhouAbstractThe primary objective of this study was to investigate how trip pattern variables extracted from large-scale taxi GPS data contribute to the spatially aggregated crashes in urban areas. The following five types of data were collected: crash data, large-scale taxi GPS data, road network attributes, land use features and social-demographic data. A data-driven modeling approach based on Latent Dirichlet Allocation (LDA) was proposed for discovering hidden trip patterns from a taxi GPS dataset, and a total of fifty trip patterns were identified. The collected data and the identified trip patterns were further aggregated into167 ZIP Code Tabulation Areas (ZCTA). Random forest technique was used to identify the factors that contributed to total, PDO and fatal-plus-injury crashes in the selected ZCTAs during the study period. Geographically weighted Poisson regression (GWPR) models were then developed to establish a relationship between the crashes and the contributing factors selected by the random forest technique. Comparative analyses were conducted to compare the performance of the GWPR models that considered traditional traffic exposure variables only, trip pattern variables only, and both traditional exposure and trip pattern variables. The model specification results suggest that the trip pattern variables significantly affected the crash counts in the selected ZCTAs, and the models that considered both the traditional traffic exposure and the trip pattern variables had the best goodness-of-fit in terms of the lowest MAD and AICc values.
       
  • Cyclists’ eye movements and crossing judgments at uncontrolled
           intersections: An eye-tracking study using animated video clips
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): N. Kovácsová, C.D.D. Cabrall, S.J. Antonisse, T. de Haan, R. van Namen, J.L. Nooren, R. Schreurs, M.P. Hagenzieker, J.C.F. de WinterAbstractResearch indicates that crashes between a cyclist and a car often occur even when the cyclist must have seen the approaching car, suggesting the importance of hazard anticipation skills. This study aimed to analyze cyclists’ eye movements and crossing judgments while approaching an intersection at different speeds. Thirty-six participants watched animated video clips with a car approaching an uncontrolled four-way intersection and continuously indicated whether they would cross the intersection first. We varied (1) car approach scenario (passing, colliding, stopping), (2) traffic complexity (one or two approaching cars), and (3) cyclist’s approach speed (15, 25, or 35 km/h). Results showed that participants looked at the approaching car when it was relevant to the task of crossing the intersection and posed an imminent hazard, and they directed less attention to the car after it had stopped or passed the intersection. Traffic complexity resulted in divided attention between the two cars, but participants retained most visual attention to the car that came from the right and had right of way. Effects of cycling speed on cyclists’ gaze behavior and crossing judgments were small to moderate. In conclusion, cyclists’ visual focus and crossing judgments are governed by situational factors (i.e., objects with priority and future collision potential), whereas cycling speed does not have substantial effects on eye movements and crossing judgments.
       
  • Recommend or mandate' A systematic review and meta-analysis of the
           effects of mandatory bicycle helmet legislation
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Alena HoyeAbstractIf all cyclistswere wearing helmets, significant numbers of head injuries might theoretically be prevented. Mandatory bicycle helmet legislation increases helmet use but is a controversial measure. Results from 21 studies of the effects of mandatory bicycle helmet legislation on injuries among crash involved cyclists were investigated by means of meta-analysis and the effects of several potential biases were investigated. The summary effect of mandatory bicycle helmet legislation for all cyclists on head injuries is a statistically significant reduction by 20% (95% confidence interval [−27; −13]). Larger effects were found for serious head injury (−55%; 95% confidence interval; [−78; −8]). Among children, larger effects were found when legislation applies to all cyclists than when it applies to children only. There is no clear indication of the results being affected by publication bias. Publication bias may exist, but any existing biases seem to more or less outweigh each other. Results from meta-analysis do not indicate that the results are systematically affected by a lack of control for time trend bias, choice of comparison group or study design (before-after vs. case control). Summary effects may be somewhat overestimated because of a lack of control for potential confounding variables in some of the studies. However, such a bias, if it exists, is not likely to be large. Empirical evidence for the hypotheses that mandatory bicycle helmet legislation deters people from cycling and that helmet wearing leads to behavioral adaptation is mixed. In summary, mandatory bicycle helmet legislation can be expected to reduce head injury among crash involved cyclists. Some adverse effects may occur, but will not necessarily be large or long-lasting. People who may be deterred from cycling, are among those with the highest injury risk and the smallest health effects from cycling. If the overall goal is to improve safety for all cyclists and to increase cycling, mandatory bicycle helmet legislation should be supplemented by other measures, especially improved bicycle infrastructure.
       
  • Are low income and minority households more likely to die from
           traffic-related crashes'
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Robert B. Noland, Maria Luz LahamAbstractAn analysis of motor vehicle mortality is conducted using data from the Census Bureau’s National Longitudinal Mortality Study for 1980, 1990, and 2000. The likelihood of being a motor vehicle crash fatality is compared to all other causes of death and not dying within the six year follow up period of the data. Using a multinomial logistic regression, mortality associations with the socioeconomics and demographics of individuals is examined. No association is found with a greater likelihood of being a motor vehicle mortality, based on family income, ethnicity, or race. Those living in rural areas, are unemployed or disabled, and residents of southern states are more likely to be a motor-vehicle fatality. These results conflict with those of many ecological studies that assume lower income neighborhoods (and their residents) are more likely to die due to motor-vehicle crashes.
       
  • Red-light running behavior of cyclists in Italy: An observational study
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): F. Fraboni, V. Marín Puchades, M. De Angelis, L. Pietrantoni, G. PratiAbstractAccident analysis and studies on traffic revealed that cyclists’ violation of red-light regulation is a typical infringement committed by cyclists. Furthermore, an association between cyclists’ crash involvement and red-light violations has been found across different countries. The literature on red-light running cyclists’ behavior in relation to their characteristic is still scarce. The present study, adopted an eye-observational methodology to investigates differences in cyclists’ crossing behavior at intersections, with a particular attention to their demographical characteristics. The classification of cyclists’ red-light behavior in risk-taking, opportunistic and law-obeying, was adopted and re-adapted to reflect more objective behaviors, eliminating any inference or judgment. Two researchers at a time observed unobtrusively at four different intersections, during morning and late afternoon peak hours, 1381 cyclists approaching the traffic light during the red phase. More than 60% of the observed cyclists violated the traffic control. Results showed that the visual search strategy displayed by the cyclists and the presence of other cyclists at the intersection are important factors in predicting the probability of red-light running behavior.
       
  • Multiple imputation of missing marijuana data in the Fatality Analysis
           Reporting System using a Bayesian multilevel model
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Qixuan Chen, Sharifa Z. Williams, Yutao Liu, Stanford T. Chihuri, Guohua LiAbstractBackgroundThe Fatality Analysis Reporting System (FARS) provides important data for studying the role of marijuana in motor vehicle crashes. However, marijuana testing data are available for only 34% of drivers in the FARS, which represents a major barrier in the use of the data.MethodsWe developed a multiple imputation (MI) procedure for estimating marijuana positivity among drivers with missing marijuana test results, using a Bayesian multilevel model that allows a nonlinear association with blood alcohol concentrations (BACs), accounts for correlations among drivers in the same states, and includes both individual-level and state-level covariates. We generated 10 imputations for the missing marijuana-testing data using Markov chain Monte Carlo simulations and estimated positivity rates of marijuana in the nation and each state.ResultsDrivers who were at older age, female, using seatbelt at the time of crash, having valid license, or operating median/heavy trucks were less likely to test positive for marijuana. There was a reverse U-shaped association between BACs and positivity of marijuana, with lower positivity when BACs 
       
  • Predicting interstate motor carrier crash rate level using classification
           models
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Jundi Liu, Linda N. Boyle, Ashis G. BanerjeeAbstractEnsuring safe operations of large commercial vehicles (motor carriers) remains an important challenge, particularly in the United States. While the federal regulatory agency has instituted a compliance review-based rating method to encourage carriers to improve their safety levels, concerns have been expressed regarding the effectiveness of the current ratings. In this paper, we consider a crash rate level (high, medium, and low) rather than a compliance review-based rating (satisfactory, conditional satisfactory, and unsatisfactory). We demonstrate an automated way of predicting the crash rate levels for each carrier using three different classification models (Artificial Neural Network, Classification and Regression Tree (CART), and Support Vector Machine) and three separate variable selection methods (Empirical Evidence, Multiple Factor Analysis, Garson's algorithm). The predicted crash rate levels (high, low) are compared to the assigned levels based on the current safety rating method. The results indicate the feasibility of crash rate level as an effective measure of carrier safety, with CART having the best performance.
       
  • Spatial autocorrelation analysis of cargo trucks on highway crashes in
           Chile
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Carola A. Blazquez, Barbara Picarte, Juan Felipe Calderón, Fernando LosadaAbstractThe growing number of cargo trucks on highway crashes in recent years due to the increase in freight movement in Chile motivates this study to identify the formation of persistent crash clusters on highway Ruta 5 (R5). Two spatial statistical methods (Moran’s I and Getis-Ord Gi*) were used to determine whether crashes on this highway showed spatial clustering over time from a global and local perspective. Globally, recurrent crash clusters are spatially correlated on vertical curves and straight highway sections on northern R5 with different truck types and with the tractor-trailer units during rainy days on southern R5. The local spatial autocorrelation results suggest that the contributing causes related to the loss of control of the vehicle, the fatigue and imprudence of the driver, and crashes involving tractor units with trailer tend to cause persistent rollover crash clusters throughout R5. Overall, clustering of crash attributes with high values (i.e., hot spots) occurring on highway locations with vertical curves and on cloudy days predominated in the northern R5, and the largest number of recurrent hot spots occurred on sunny days along southern R5. A hot spot spatial co-occurrence analysis was further performed to identify the strong relationships between the studied crash attributes, and the crash and injury types as outcomes. The indication of high risk for the clustering of cargo trucks on highways crashes provides a basis for improving highway safety and reduce the associated social and economic costs.
       
  • Modular design of fatigue detection in naturalistic driving environments
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Hilal Al-libawy, Ali Al-Ataby, Waleed Al-Nuaimy, Majid A. Al-TaeeAbstractResearch in driver mental fatigue is motivated by the fact that errors made by drivers often have life-threatening consequences. This paper proposes a new modular design approach for the early detection of driver fatigue system taking into account optimisation of system performance using particle swarm optimisation (PSO). The proposed system is designed and implemented using an existing dataset that was simultaneously collected from participants and vehicles in a naturalistic environment. Four types of data are considered as fatigue-related metrics including: vehicle acceleration, vehicle rotation pattern, driver's head position and driver's head rotation. The driver's blink rate data is used in this work as a proxy for ground truth for the classification algorithm. The collected data elements are initially fed to input modules represented by ternary neural network classifiers that estimates alertness. A Bayesian algorithm with PSO is then used to combine and optimise detection performance based on the number of existing input modules as well as their output states. Performance of the developed fatigue-detection system is assessed experimentally with a small data samples of driver trips. The obtained results are found in agreement with the state-of-the-art in terms of accuracy (90.4%), sensitivity (92.6%) and specificity (90.7%). These results are achieved with significant design flexibility and robustness against partial loss of input data source(s). However, due to small sample size of dataset (N = 3), a larger dataset need to be tested with the same system framework to generalise the findings of this work.
       
  • Surrogate safety and network screening: Modelling crash frequency using
           GPS travel data and latent Gaussian Spatial Models
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Joshua Stipancic, Luis Miranda-Moreno, Nicolas Saunier, Aurelie LabbeAbstractImproving road safety requires accurate network screening methods to identify and prioritize sites in order to maximize the effectiveness of implemented countermeasures. In screening, hotspots are commonly identified using statistical models and ranking criteria derived from observed crash data. However, collision databases are subject to errors, omissions, and underreporting. More importantly, crash-based methods are reactive and require years of crash data. With the arrival of new technologies including Global Positioning System (GPS) trajectory data, proactive surrogate safety methods have gained popularity as an alternative approach for screening. GPS-enabled smartphones can collect reliable and spatio-temporally rich driving data from regular drivers using an inexpensive, simple, and user-friendly tool. However, few studies to date have analyzed large volumes of smartphone GPS data and considered surrogate-safety modelling techniques for network screening. The purpose of this paper is to propose a surrogate safety screening approach based on smartphone GPS data and a Full Bayesian modelling framework. After processing crash data and GPS data collected in Quebec City, Canada, several surrogate safety measures (SSMs), including vehicle manoeuvres (hard braking) and measures of traffic flow (congestion, average speed, and speed variation), were extracted. Then, spatial crash frequency models incorporating the extracted SSMs were proposed and validated. A Latent Gaussian Spatial Model was estimated using the Integrated Nested Laplace Approximation (INLA) technique. While the INLA Negative Binomial models outperformed alternative models, incorporating spatial correlations provided the greatest improvement in model fit. Relationships between SSMs and crash frequency established in previous studies were generally supported by the modelling results. For example, hard braking, congestion, and speed variation were all positively linked to crash counts at the intersection level. Network screening based on SSMs presents a substantial contribution to the field of road safety and works towards the elimination of crash data in evaluation and monitoring.
       
  • Design and experiment verification of a novel analysis framework for
           recognition of driver injury patterns: From a multi-class classification
           perspective
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Mengtao Zhu, Yunjie Li, Yinhai WangDetecting driver injury patterns is a typical classification problem. Crash data sets are highly skewed where fatalities and severe injuries are often less represented compared to other events. The severity prediction performance of the existing models is poor due to the highly imbalanced samples of different severity levels within a given dataset. This paper proposes a machine learning based analysis framework from a multi-class classification perspective for accurate recognition of the driver injury patterns. The proposed framework includes preprocessing, classification, evaluation and application of a given dataset. This framework is verified based on the three years single-vehicle ROR (run-off-road) crash records collected in Washington State from 2011 to 2013. At first, thirteen most important safety-related variables are recognized through random forests. Then, the four driver’s injury severity levels viz., fatal/serious injury, evident injury, possible injury, and no injury are predicted by integrating the decomposed binary neural network models to achieve better performance. Finally, a sensitivity analysis is carried out to interpret variables’ impacts on the decomposed injury severity levels. The study shows that lack of restraint, female drivers, truck usage, driver impairment, driver distraction, vehicle overturn (rollover), dawn/dusk, and overtaking are the leading factors contributing to the driver fatalities or severe injuries in a single-vehicle ROR crash. Most of the findings are consistent with the previous studies. The experimental results validate the effectiveness of the proposed framework which can be further applied for pattern recognition in traffic safety research.Graphical abstractGraphical abstract for this article
       
  • 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. CommandeurAbstractAt 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 ElvikAbstractThe European Road Safety Decision Support System (roadsafety-dss.eu) 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.
       
  • Examining driver injury severity in intersection-related crashes using
           cluster analysis and hierarchical Bayesian models
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Zhenning Li, Cong Chen, Yusheng Ci, Guohui Zhang, Qiong Wu, Cathy Liu, Zhen (Sean) QianAbstractTraffic crashes are more likely to occur at intersections where the traffic environment is complicated. In this study, a hybrid approach combining cluster analysis and hierarchical Bayesian models is developed to examine driver injury severity patterns in intersection-related crashes based on two-year crash data in New Mexico. Three clusters are defined by K-means cluster analysis based on weather and roadway environmental conditions in order to reveal drivers’ risk compensation instability under diverse external environment. Hierarchical Bayesian random intercept models are developed for each of the three clusters as well as the whole dataset to identify the contributing factors on multilevel driver injury outcomes: property damage only (Level I), complaint of injury and visible injury (Level II), and incapacitating injury and fatality (Level III). Model comparison with an ordinary multinomial logistic model omitting crash data hierarchical features and cross-level interactions verifies the suitability and effectiveness of the proposed hybrid approach. Results show that a number of crash-level variables (time period, weather, light condition, area, and road grade), vehicle/driver-level variables (traffic controls, vehicle action, vehicle type, seatbelt used, driver age, drug/alcohol impaired, and driver age) along with some cross-level interactions (i.e., left turn and night, drug and dark) impose significantly influence driver injury severity. This study provides insightful understandings of the effects of these variables on driver injury severity in intersection-related crashes and beneficial references for developing effective countermeasures for severe crash prevention.
       
  • The influence of impulsivity and the Dark Triad on self-reported
           aggressive driving behaviours
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Laura Ball, Ruth Tully, Vincent EganAbstractThe present study tested the role of Dark Triad traits (DT; narcissism, psychopathy, and Machiavellianism) as potential contributors to self-reported aggressive driving alongside driving anger, general aggression, impulsiveness, and attributions of malign driving intent. Members of the general community (N = 168) completed an online survey battery measuring these characteristics, and a proxy measure of aggressive driving. Regression analyses revealed that psychopathy, a history of physical aggression towards others, and the “progress impeded” aspect of driving anger, accounted for 50.8% of the variance in self-reported aggressive driving behaviours. The remaining variables were not significant. A structural equation model found all measures fitted into a single model in which impulsivity and the DT predicted general aggression, general aggression fully mediated the effect of the DT on driving anger, and general aggression and progress impedance predicted self-reported aggressive driving (GFI = 0.925). These results indicate tendencies toward expressing aggression physically, frustration at goals being impeded, and a callous, impulsive nature can predispose an individual to aggressive driving behaviours. Implications of these findings and recommendations for research are discussed.
       
  • Multivariate linear intervention models with random parameters to estimate
           the effectiveness of safety treatments: Case study of intersection device
           program
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Emanuele Sacchi, Karim El-BasyounyAbstractA novel intervention model that analyzes time-series crash data was recently introduced in the road safety statistical field. The model allows the computation of components related to direct and indirect treatment effects using a linearized time-series intervention model. The isolation of a component corresponding to the direct treatment effects, known as the crash modification function (CMFunction), enables the assessment of safety countermeasures over time. To gain new insights into how crash counts are influenced by covariates and to account for the fact that many components affecting crash occurrence are not easily available (unobserved heterogeneity), the linear intervention models with random parameters are implemented to evaluate the safety impacts of a specific treatment. Both matched-pair and full random parameter models were applied. In addition, the analysis was carried out in a multivariate context to account for possible correlation between dependent variables. The safety treatment selected for this study was the Intersection Safety Device (ISD) program implemented in the City of Edmonton (Alberta, Canada). The safety impacts were estimated by assessing the change in crash severity (property-damage-only vs. fatal-plus-injury) over time. Overall, the results showed a lower deviance information criterion (better goodness of fit) of the multivariate linear intervention model with random parameters compared to the univariate form with fixed parameters. The difference of the indexes of treatment effectiveness between the proposed modeling framework and the univariate model with fixed parameters was estimated up to 2.7%, which indicates the importance of accounting for unobserved heterogeneity.
       
  • The relationship between driving skill and driving behavior: Psychometric
           adaptation of the Driver Skill Inventory in China
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Jing Xu, Juan Liu, Xianghong Sun, Kan Zhang, Weina Qu, Yan GeAbstractMost road accidents are caused by human factors alone or in combination with other factors. Deficits in driving skill are a human factor that contributes to accidents. It is important to focus on driving skills to reduce traffic accidents and enhance safe driving. In this study, we adopted a Chinese version of the Driver Skill Inventory (DSI) and explored its correlation with driving behaviors, sociodemographic factors and personality. A total of 295 licensed drivers voluntarily completed a survey that covered the DSI, the Driver Behavior Questionnaire, the Positive Driver Behavior Scale, self-reported traffic accidents, penalty points and fines, the Big Five Inventory, and sociodemographic parameters. First, the results of principal axis analysis on the DSI yielded two clear factors: perceptual-motor skills and safety skills. Second, both perceptual-motor skills and safety skills were positively correlated with positive behaviors. Safety skills were negatively correlated with all aberrant driving behaviors (e.g., aggressive violations, ordinary violations, errors, and lapses), whereas perceptual-motor skills were negatively correlated with errors and lapses. Third, with regard to penalties, safety skills were negatively associated with penalty fines and points received within the past year, whereas perceptual-motor skills showed no such correlation. Fourth, with regard to sociodemographic parameters, perceptual-motor skills were positively correlated with years of holding a driving license, weekly driving distance and annual driving distance. Men reported higher perceptual-motor skills than women, whereas safety skills were unrelated to gender. Fifth, structural equation modeling was conducted to test the effects of personality traits on driving skill. The results showed that conscientiousness, neuroticism and openness to experience were significant predictors of perceptual-motor skills, whereas agreeableness and conscientiousness were significant predictors of safety skills. Overall, based on these results, the Chinese version of the DSI has acceptable internal consistency and a stable structure; thus, it represents a useful tool to measure driving skill. Moreover, the measurement of personality traits, which are important individual factors closely linked to driving skill, can aid in the education of professional drivers or to inform preventative and educational activities that focus on personality traits in addition to knowledge.
       
  • Validation of the influencing factors associated with traffic violations
           and crashes on freeways of developing countries: A case study of Iran
    • Abstract: Publication date: Available online 9 August 2018Source: Accident Analysis & PreventionAuthor(s): Mansour Hadji Hosseinlou, Alireza Mahdavi, Mehdi Jabbari NooghabiAbstractAmong the rural roads, freeways have the maximum allowable speed limit. This subject increases the tendency of drivers to use these kinds of roads, and despite its positive effects, it has caused numerous problems. One of them is the increase in the rate of traffic violations and crashes. The amount of crashes per kilometer in Iran’s freeways is almost twice the other rural roads. Hence, finding a solution to this problem is of particular importance. This research intends to validate some of the influencing factors which cause traffic violations and crashes in freeways and determine their amount of influence through statistical models. For this purpose, the authors considered violations and crashes for 36 road segments as dependent variables and other factors as independent ones. Since dependent variables were count, discrete, and non-zero, the proposed models were Poisson and Zero-truncated Poisson. The processing of the models indicated that the amounts of Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) indices for the Zero-truncated Poisson model are less than those of the Poisson model and the result of the Pseudo-R2 test for this model is within the acceptable range. Moreover, the result of Chi-square test which shows the proximity of expected and observed amounts was better for Zero-truncated Poisson model. Thus, this model has a considerable advantage against Poisson model. Final models indicated that the average speed has a positive correlation with the number of violations and crashes and as it increases, they increase too. Besides, peripheral landscapes, number of interchanges, number of passing lanes, and exemption from paying toll have an opposite relationship with violations and crashes.
       
  • Bivariate extreme value modeling for road safety estimation
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Lai Zheng, Karim Ismail, Tarek Sayed, Tazeen FatemaAbstractSurrogate safety measures have been advocated as a complementary approach to study safety from a broader perspective than relying on crash data alone. This study proposes an approach to incorporate different surrogate safety measures in a unified framework for road safety estimation within the bivariate extreme value theory framework. The model structure, model specification, threshold selection method, and parameter estimation method of the bivariate threshold excess model are introduced. Two surrogate safety measures, post encroachment time (PET) and length proportion of merging (LPM), are chosen to characterize the severity of merging events on freeway entrance merging areas. Based on the field data collected along Highway 417 in the City of Ottawa, Ontario, Canada, the bivariate modelling methods with seven distribution functions are applied and compared, and the model with logistic distribution function is selected as the best model. The best bivariate models’ estimation results are then evaluated by comparing them to their two marginal (univariate Generalized Pareto distribution) models. The results show that the bivariate models tend to generate crash estimates that are much closer to observed crashes than univariate models. A more important finding is that incorporating two surrogate safety measures into the bivariate models can significantly reduce the uncertainty of crash estimates. The efficiency of a bivariate model is not evidently better than either of its marginal models, but it is expected to be improved with data of a prolonged observation period. This study is also a step forward in the direction of developing multivariate safety hierarchy models, since models of the safety hierarchy have been predominantly univariate.
       
  • Bike lanes next to on-street parallel parking
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Paul SchimekAbstractFor decades it has been the conventional wisdom that crashes involving bicyclists and opening car doors are rare. This belief is based on motor vehicle crash reports, but these reports generally exclude this crash type by definition. More complete sources show that dooring crashes are one of the most common causes of urban bicycle-motor vehicle collisions, accounting for 12%–27% of the total.This paper reviews all available studies of bicyclist position in bike lanes adjacent to on-street parking. With bike lanes meeting current minimum standards, almost all bicyclists were observed riding within range of opening doors. However, when an additional three or four feet is provided between the bike lane and parked cars, hardly any bicyclists are observed in the door zone.All of the design guides recently developed in North America for separated bike lanes include a buffer to account for the door zone when the bike lane is placed between on-street parallel parking and the curb. However, only the Ontario design guide has a similar requirement for standard bike lanes. The buffer requirement for standard bike lanes adjacent to on-street parking should be incorporated into all design guidance.When there is not room for this necessary buffer, an alternative is to place a shared lane marking in the center of the travel lane, which encourages bicyclists to ride outside the door zone. Increasing the number of bicyclists who ride outside of the door zone may require lowering speed limits and repealing laws that create a presumption that bicyclists must always keep to the right of the travel lane.
       
  • Wildlife warning reflectors do not mitigate wildlife–vehicle
           collisions on roads
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Anke Benten, Torsten Hothorn, Torsten Vor, Christian AmmerAbstractWildlife–vehicle collisions cause human fatalities and enormous economic and ecological losses on roads worldwide. A variety of mitigation measures have been developed over the past decades to separate traffic and wildlife, warn humans, or prevent wildlife from entering a road while vehicles are passing by, but only few are economical enough to be applied comprehensively. One such measure, wildlife warning reflectors, has been implemented over the past five decades. However, their efficacy is questioned because of contradictory study results and the variety of applied study designs and reflector models. We used a prospective, randomized non-superiority cross-over study design to test our hypothesis of the inefficacy of modern wildlife warning reflectors. We analyzed wildlife–vehicle collisions on 151 testing sites of approximately 2 km in length each. During the 24-month study period, 1984 wildlife–vehicle collisions were recorded. Confirmatory primary and exploratory secondary analyses using a log-link Poisson mixed model with normal nested random intercepts of observation year in road segment, involved species, and variables of the road segment and the surrounding environment showed that reflectors did not lower the number of wildlife–vehicle collisions by a relevant amount. In addition, variables of the road segment and the surrounding environment did not indicate differential effects of wildlife warning reflectors. Based on our results, we conclude that wildlife warning reflectors are not an effective tool for mitigating wildlife–vehicle collisions on roads.
       
  • Modeling and comparing injury severity of at-fault and not at-fault
           drivers in crashes
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Venkata R. Duddu, Praveena Penmetsa, Srinivas S. PulugurthaAbstractThis paper examines and compares the effect of selected variables on driver injury severity of, both, at-fault and not at-fault drivers. Data from the Highway Safety Information System (HSIS) for the state of North Carolina was used for analysis and modeling. A partial proportional odds model was developed to examine the effect of each variable on injury severity of at-fault driver and not at-fault driver, and, to examine how each variable affects these two drivers’ injury severity differently. Road characteristics, weather condition, and geometric characteristics were observed to have a similar effect on injury severity in a crash to at-fault and not at-fault drivers. Age of the driver, physical condition, gender, vehicle type, and, the number and type of traffic rule violations were observed to play a significant role in the injury severity of not at-fault drivers when compared to at-fault drivers in the crash. Moreover, motorcyclists and drivers 70 years or older are observed to be the most vulnerable road users.
       
  • Young male drivers’ perceptions of and experiences with YouTube videos
           of risky driving behaviours
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Evelyn Vingilis, Zümrut Yildirim-Yenier, Larissa Vingilis-Jaremko, Jane Seeley, Christine M. Wickens, Daniel H. Grushka, Judy FleiterAbstractObjectiveYouTube features millions of videos of high risk driving behaviours and negative consequences of high risk driving (“fails”), such as injuries or deaths. Unfortunately, no information is available on YouTube viewership of these types of sites or on the effects of these videos on viewers. The purpose of this study was to examine young male drivers’ perceptions of and experiences with YouTube videos of risky driving behaviours.MethodsUsing an exploratory qualitative descriptive approach, three 2-hour focus groups were conducted with young men 18–30 years of age to determine: (i) if they watch and share YouTube videos, including high risk driving videos; (ii) what effects high risk driving videos have on them and others and whether YouTube videos of negative consequences discourage high risk driving.ResultsParticipants indicated three uses for YouTube; it has replaced television watching and provides entertainment and information. Motivations of both risky drivers in videos and viewers to engage in high risk driving activities included person characteristics (e.g., sensation seeking and responsivity to financial rewards for high view count videos) and socio-environmental factors (e.g., peer pressure). Most indicated that they would not try to imitate the risky behaviours exhibited in videos, although a few had tried to copy some risky driving moves from videos.ConclusionsSocial, not mass media is now the common information and entertainment source for young people. YouTube videos of high risk driving are common and ubiquitous. Findings from these focus groups suggest that viewers could influence subsequent content of social media videos and reciprocally, videos could influence behaviours of some viewers, particularly young male viewers.
       
  • Safety performance functions for horizontal curves and tangents on two
           lane, two way rural roads
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Jeffrey P. Gooch, Vikash V. Gayah, Eric T. DonnellAbstractHorizontal curves on two-way, two-lane rural roads pose critical safety concerns. Accurate prediction of safety performance at these locations is vital to properly allocate resources as a part of any safety management process. The current method of predicting safety performance on horizontal curves relies on the application of a safety performance function (SPF) developed using only tangent sections and adjusting this value using a crash modification factor (CMF). However, this process inherently assumes that safety performance on curves and tangent sections share the same general functional relationships with variables included in the SPF, notably traffic volumes and segment length, even though research suggests otherwise. In light of this, the goal of this paper is to systematically study the relationship between safety performance and traffic volumes on horizontal curves of two-lane, two-way rural roads and to compare this to the safety performance of tangent sections. The propensity scores-potential outcomes framework is used to help ensure similarity between tangent and curve sections considered in the study, while mixed-effects negative binomial regression is used to quantify safety performance. The results reveal that safety performance on horizontal curves differs significantly from that on tangent sections with respect to both traffic volumes and segment length. Significant differences were also found between the safety performance on tangents and curves relative to other roadway features. These results suggest that curve-specific SPFs should be considered in the next edition of the Highway Safety Manual.
       
  • Evaluating the safety and operational impacts of left-turn bay extension
           at signalized intersections using automated video analysis
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Ahmed Tageldin, Tarek Sayed, Karim IsmailAbstractLeft-turn lanes are commonly introduced to provide space to accommodate comfortable deceleration and adequate storage of turning vehicles. Operational shortcomings may arise due to inadequate length, including overflow and blockage of left-turn entrance by queues on an adjacent through lane. This study investigates the potential safety and operational benefits of treating left-turn lanes by extending the length further upstream a signalized intersection. Video data was collected at three treated left-turn lanes as well as three matched control lanes; all in both before and after treatment conditions. Safety parameters consisted of the counts and severities of traffic conflicts occurring on the left-turn lanes and inside the intersection. There was a marked reduction in traffic conflict counts in all treated sites. The overall treatment effect, which accounts for the simultaneous change in control sites, was 63.2% (p 
       
  • Investigating factors of crash frequency with random effects and random
           parameters models: New insights from Chinese freeway study
    • Abstract: Publication date: November 2018Source: Accident Analysis & Prevention, Volume 120Author(s): Qinzhong Hou, Andrew P. Tarko, Xianghai MengAbstractIn response to the rapid economic growth in China, its freeway system has become the longest in the world and likely will continue to expand. Unfortunately, the safety issues on freeways in China have grown as well and are of great concern to Chinese transportation authorities and drivers. While many proven safety countermeasures developed and implemented by other countries are available for reference, they may be not fully transferrable to China due to the differences in driving cultures and conditions. As a result, an investigation of China’s unique safety factors and effective relevant countermeasures are urgently needed.The study presented in this paper thoroughly investigated the factors contributing to freeway crashes in China based on detailed crash data, traffic characteristics, freeway geometry, pavement conditions, and weather conditions. To properly account for the over-dispersion of data and unobserved heterogeneity, a random effects negative binomial (RENB) model and a random parameters negative binomial (RPNB) model were applied, along with a negative binomial (NB) model. The analysis revealed a large number of crash frequency factors, including several interesting and important factors rarely studied in the past, such as the safety effects of climbing lanes. Moreover, the RENB and RPNB models were found to considerably outperform the NB model; however, although the RPNB exhibited better goodness-of-fit than the RENB model, the difference was rather small. The findings of this study shed more light on the factors influencing freeway crashes in China. The results will be useful to highway designers and engineers for creating, building, and operating safe freeways as well as to safety management departments for developing effective safety countermeasures. The study presented in this paper also provides additional guidance for choosing relevant methods to analyze safety and to identify safety factors.
       
  • Safety assessment of control design parameters through vehicle dynamics
           model
    • Abstract: Publication date: Available online 20 July 2018Source: Accident Analysis & PreventionAuthor(s): Stergios Mavromatis, Alexandra Laiou, George YannisAbstractAn 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 QinAbstractA 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 YangAbstractTraditional 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.
       
  • A computational model of pedestrian road safety: The long way round is the
           safe way home
    • Abstract: Publication date: Available online 28 June 2018Source: Accident Analysis & PreventionAuthor(s): Charlotte Hannah, Irena Spasić, Padraig CorcoranAbstractWe propose a novel linear model of pedestrian safety in urban areas with respect to road traffic crashes that considers a single independent variable of pedestrian path safety. This variable is estimated for a given urban area by sampling pedestrian paths from the population of such paths in that area and in turn estimating the mean safety of these paths. We argue that this independent variable directly models the factors contributing to pedestrian safety. This contrasts previous approaches, which, by considering multiple independent variables describing the environment, traffic and pedestrians themselves, indirectly model these factors. Using data about 15 UK cities, we demonstrate that the proposed model accurately estimates numbers of pedestrian casualties.
       
  • 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 GogolínAbstractRoundabouts 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 ShahAbstractPedestrians 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.
       
  • The measurement equivalence of a safety climate measure across five
           faultlines
    • Abstract: Publication date: Available online 21 May 2018Source: Accident Analysis & PreventionAuthor(s): Xiaohong Xu, Stephanie C. Payne, Mindy E. BergmanAbstractThis study examines the appropriateness of comparing safety climate survey responses across multiple faultlines—hypothetical dividing lines that split a group into subgroups based on one or more attributes. Using survey data from 8790 employees of a multinational chemical processing and manufacturing company from 76 work sites nested within 19 different countries, we examined the multilevel measurement equivalence of a safety climate measure across cultural dimensions, survey languages, organizational hierarchy, employment arrangements, and work environments. As simulation studies support the faultline at the individual-level requires measurement equivalence tests that are different from the faultline at the country-level, we used multi-group multilevel confirmatory factor analyses for the Level-3 faultline, and multilevel factor mixture models for known classes for the Level-1 faultlines. The results demonstrated that faultlines can prevent safety climate measurement equivalence, which prohibits the aggregation of individual-level scores to higher levels and making comparisons across faultlines. This first study on multilevel safety climate measurement equivalence serves as both a warning to safety climate researchers and practitioners regarding the importance of faultlines and reminds us to consider the level of the faultlines when testing measurement equivalence with multilevel data.
       
  • 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
       
  • Effects of alertness management training on sleepiness among long-haul
           truck drivers: A randomized controlled trial
    • Abstract: Publication date: Available online 18 May 2018Source: Accident Analysis & PreventionAuthor(s): M. Pylkkönen, A. Tolvanen, C. Hublin, J. Kaartinen, K. Karhula, S. Puttonen, M. Sihvola, M. SallinenAbstractEducation is a frequently recommended remedy for driver sleepiness in occupational settings, although not many studies have examined its usefulness. To date, there are no previous on-road randomized controlled trials investigating the benefits of training on sleepiness among employees working in road transport. To examine the effects of an educational intervention on long-haul truck drivers’ sleepiness at the wheel, amount of sleep between work shifts, and use of efficient sleepiness countermeasures (SCM) in association with night and non-night shift, a total of 53 truck drivers operating from southern Finland were allocated into an intervention and a control group using a stratified randomization method (allocation ratio for intervention and control groups 32:21, respectively). The intervention group received a 3.5-hour alertness management training followed by a two-month consultation period and motivational self-evaluation tasks two and 4–5 months after the training, while the control group had an opportunity to utilize their usual statutory occupational health care services. The outcomes were measured under drivers’ natural working and shift conditions over a period of two weeks before and after the intervention using unobtrusive data-collection methods including the Karolinska Sleepiness Scale measuring on-duty sleepiness, a combination of actigraphy and a sleep-log measuring sleep between duty hours, and self-report questionnaire items measuring the use of SCMs while on duty. The data analysis followed a per-protocol analysis. Results of the multilevel regression models showed no significant intervention-related improvements in driver sleepiness, prior sleep, or use of SCMs while working on night and early morning shifts compared to day and/or evening shifts. The current study failed to provide support for a feasible non-recurrent alertness-management training being effective remedy for driver sleepiness in occupational settings. These results cannot, however, be interpreted as evidence against alertness management training in general but propose that driver education is not a sufficient measure as such to alleviate driver sleepiness.
       
  • Prediction and perception of hazards in professional drivers: Does hazard
           perception skill differ between safe and less-safe fire-appliance
           drivers'
    • Abstract: Publication date: Available online 18 May 2018Source: Accident Analysis & PreventionAuthor(s): David Crundall, Victoria KrollAbstractCan hazard perception testing be useful for the emergency services' Previous research has found emergency response drivers’ (ERDs) to perform better than controls, however these studies used clips of normal driving. In contrast, the current study filmed footage from a fire-appliance on blue-light training runs through Nottinghamshire, and endeavoured to discriminate between different groups of EDRs based on experience and collision risk. Thirty clips were selected to create two variants of the hazard perception test: a traditional push-button test requiring speeded-responses to hazards, and a prediction test that occludes at hazard onset and provides four possible outcomes for participants to choose between. Three groups of fire-appliance drivers (novices, low-risk experienced and high-risk experienced), and age-matched controls undertook both tests. The hazard perception test only discriminated between controls and all FA drivers, whereas the hazard prediction test was more sensitive, discriminating between high and low-risk experienced fire appliance drivers. Eye movement analyses suggest that the low-risk drivers were better at prioritising the hazardous precursors, leading to better predictive accuracy. These results pave the way for future assessment and training tools to supplement emergency response driver training, while supporting the growing literature that identifies hazard prediction as a more robust measure of driver safety than traditional hazard perception tests.
       
  • 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 BanksAbstractTechnology-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 VedovaAbstractSelf-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.
       
  • Analysing truck harsh braking incidents to study roundabout accident risk
    • Abstract: Publication date: Available online 5 May 2018Source: Accident Analysis & PreventionAuthor(s): Jwan Kamla, Tony Parry, Andrew DawsonAbstractIn order to reduce accident risk, highway authorities prioritise maintenance budgets partly based upon previous accident history. However, as accident rates have continued to fall, this approach has become problematic as accident ‘black spots’ have been treated and the number of accidents at any individual site has fallen, making previous accident history a less reliable indicator of future accident risk. Another way of identifying sites of higher accident risk might be to identify near-miss accidents (where an accident nearly happened but was avoided). The principal aim of this paper is to analyze potentially unsafe truck driving conditions from counts of Harsh Braking Incidents (HBIs) at roundabouts and compare the results to similar, previous studies of accident numbers at the same sites, to explore if HBIs can be studied as a surrogate for accidents. This is achieved by processing truck telematics data with geo-referenced incidents of harsh braking. Models are then developed to characterise the relationships between truck HBIs and geometric and traffic variables. These HBIs are likely to occur more often than accidents and may, therefore, be useful in identifying sites with high accident risk. Based on the results of this study, it can be concluded that HBIs are influenced by traffic and geometric variables in a similar way to accidents; therefore they may be useful in considering accident risk at roundabouts. They are a source of higher volumes of data than accidents, which is important in considering changes or trends in accident risk over time. The results showed that random-parameters count data models provide better goodness of fit compared to fixed-parameters models and more variables were found to be significant, giving a better prediction of events.
       
  • 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'BrienAbstractIntroductionShifting 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 DongenAbstractTimely 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 VillariniAbstractNumerous 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
           data
    • 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 projectAbstractTo 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 LinAbstractThe 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.
       
  • 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. PratoAbstractAlthough 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 DongenAbstractFatigue 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 DongenAbstractIn 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. RitchieAbstractTrucks 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. WegmanAbstractThis 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 AntoniouAbstractGiven 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 JafarabadiAbstractThe 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. HanowskiAbstractSimilar 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 StipancicAbstractThe 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 PapadimitriouAbstractConsiderable 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.
       
 
 
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