for Journals by Title or ISSN
for Articles by Keywords
help

Publisher: Elsevier   (Total: 3030 journals)

 A  B  C  D  E  F  G  H  I  J  K  L  M  N  O  P  Q  R  S  T  U  V  W  X  Y  Z  

        1 2 3 4 5 6 7 8 | Last   [Sort by number of followers]   [Restore default list]

Showing 1 - 200 of 3030 Journals sorted alphabetically
AASRI Procedia     Open Access   (Followers: 15)
Academic Pediatrics     Hybrid Journal   (Followers: 20, SJR: 1.402, h-index: 51)
Academic Radiology     Hybrid Journal   (Followers: 16, SJR: 1.008, h-index: 75)
Accident Analysis & Prevention     Partially Free   (Followers: 79, SJR: 1.109, h-index: 94)
Accounting Forum     Hybrid Journal   (Followers: 22, SJR: 0.612, h-index: 27)
Accounting, Organizations and Society     Hybrid Journal   (Followers: 27, SJR: 2.515, h-index: 90)
Achievements in the Life Sciences     Open Access   (Followers: 4)
Acta Anaesthesiologica Taiwanica     Open Access   (Followers: 5, SJR: 0.338, h-index: 19)
Acta Astronautica     Hybrid Journal   (Followers: 303, SJR: 0.726, h-index: 43)
Acta Automatica Sinica     Full-text available via subscription   (Followers: 3)
Acta Biomaterialia     Hybrid Journal   (Followers: 25, SJR: 2.02, h-index: 104)
Acta Colombiana de Cuidado Intensivo     Full-text available via subscription  
Acta de Investigación Psicológica     Open Access   (Followers: 2)
Acta Ecologica Sinica     Open Access   (Followers: 8, SJR: 0.172, h-index: 29)
Acta Haematologica Polonica     Free   (SJR: 0.123, h-index: 8)
Acta Histochemica     Hybrid Journal   (Followers: 3, SJR: 0.604, h-index: 38)
Acta Materialia     Hybrid Journal   (Followers: 196, SJR: 3.683, h-index: 202)
Acta Mathematica Scientia     Full-text available via subscription   (Followers: 5, SJR: 0.615, h-index: 21)
Acta Mechanica Solida Sinica     Full-text available via subscription   (Followers: 9, SJR: 0.442, h-index: 21)
Acta Oecologica     Hybrid Journal   (Followers: 9, SJR: 0.915, h-index: 53)
Acta Otorrinolaringologica (English Edition)     Full-text available via subscription   (Followers: 1)
Acta Otorrinolaringológica Española     Full-text available via subscription   (Followers: 3, SJR: 0.311, h-index: 16)
Acta Pharmaceutica Sinica B     Open Access   (Followers: 2)
Acta Poética     Open Access   (Followers: 4)
Acta Psychologica     Hybrid Journal   (Followers: 21, SJR: 1.365, h-index: 73)
Acta Sociológica     Open Access  
Acta Tropica     Hybrid Journal   (Followers: 5, SJR: 1.059, h-index: 77)
Acta Urológica Portuguesa     Open Access  
Actas Dermo-Sifiliograficas     Full-text available via subscription   (Followers: 4)
Actas Dermo-Sifiliográficas (English Edition)     Full-text available via subscription   (Followers: 3)
Actas Urológicas Españolas     Full-text available via subscription   (Followers: 3, SJR: 0.383, h-index: 19)
Actas Urológicas Españolas (English Edition)     Full-text available via subscription   (Followers: 2)
Actualites Pharmaceutiques     Full-text available via subscription   (Followers: 5, SJR: 0.141, h-index: 3)
Actualites Pharmaceutiques Hospitalieres     Full-text available via subscription   (Followers: 4, SJR: 0.112, h-index: 2)
Acupuncture and Related Therapies     Hybrid Journal   (Followers: 4)
Ad Hoc Networks     Hybrid Journal   (Followers: 11, SJR: 0.967, h-index: 57)
Addictive Behaviors     Hybrid Journal   (Followers: 15, SJR: 1.514, h-index: 92)
Addictive Behaviors Reports     Open Access   (Followers: 5)
Additive Manufacturing     Hybrid Journal   (Followers: 7, SJR: 1.039, h-index: 5)
Additives for Polymers     Full-text available via subscription   (Followers: 20)
Advanced Drug Delivery Reviews     Hybrid Journal   (Followers: 120, SJR: 5.2, h-index: 222)
Advanced Engineering Informatics     Hybrid Journal   (Followers: 11, SJR: 1.265, h-index: 53)
Advanced Powder Technology     Hybrid Journal   (Followers: 16, SJR: 0.739, h-index: 33)
Advances in Accounting     Hybrid Journal   (Followers: 8, SJR: 0.299, h-index: 15)
Advances in Agronomy     Full-text available via subscription   (Followers: 15, SJR: 2.071, h-index: 82)
Advances in Anesthesia     Full-text available via subscription   (Followers: 24, SJR: 0.169, h-index: 4)
Advances in Antiviral Drug Design     Full-text available via subscription   (Followers: 3)
Advances in Applied Mathematics     Full-text available via subscription   (Followers: 6, SJR: 1.054, h-index: 35)
Advances in Applied Mechanics     Full-text available via subscription   (Followers: 10, SJR: 0.801, h-index: 26)
Advances in Applied Microbiology     Full-text available via subscription   (Followers: 21, SJR: 1.286, h-index: 49)
Advances In Atomic, Molecular, and Optical Physics     Full-text available via subscription   (Followers: 16, SJR: 3.31, h-index: 42)
Advances in Biological Regulation     Hybrid Journal   (Followers: 4, SJR: 2.277, h-index: 43)
Advances in Botanical Research     Full-text available via subscription   (Followers: 3, SJR: 0.619, h-index: 48)
Advances in Cancer Research     Full-text available via subscription   (Followers: 26, SJR: 2.215, h-index: 78)
Advances in Carbohydrate Chemistry and Biochemistry     Full-text available via subscription   (Followers: 9, SJR: 0.9, h-index: 30)
Advances in Catalysis     Full-text available via subscription   (Followers: 5, SJR: 2.139, h-index: 42)
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: 24, SJR: 0.183, h-index: 23)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10, SJR: 0.665, h-index: 29)
Advances in Chronic Kidney Disease     Full-text available via subscription   (Followers: 8, SJR: 1.268, h-index: 45)
Advances in Clinical Chemistry     Full-text available via subscription   (Followers: 28, SJR: 0.938, h-index: 33)
Advances in Colloid and Interface Science     Full-text available via subscription   (Followers: 18, SJR: 2.314, h-index: 130)
Advances in Computers     Full-text available via subscription   (Followers: 16, SJR: 0.223, h-index: 22)
Advances in Developmental Biology     Full-text available via subscription   (Followers: 11)
Advances in Digestive Medicine     Open Access   (Followers: 4)
Advances in DNA Sequence-Specific Agents     Full-text available via subscription   (Followers: 5)
Advances in Drug Research     Full-text available via subscription   (Followers: 22)
Advances in Ecological Research     Full-text available via subscription   (Followers: 39, SJR: 3.25, h-index: 43)
Advances in Engineering Software     Hybrid Journal   (Followers: 25, SJR: 0.486, h-index: 10)
Advances in Experimental Biology     Full-text available via subscription   (Followers: 7)
Advances in Experimental Social Psychology     Full-text available via subscription   (Followers: 38, SJR: 5.465, h-index: 64)
Advances in Exploration Geophysics     Full-text available via subscription   (Followers: 3)
Advances in Fluorine Science     Full-text available via subscription   (Followers: 8)
Advances in Food and Nutrition Research     Full-text available via subscription   (Followers: 41, SJR: 0.674, h-index: 38)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 14)
Advances in Genetics     Full-text available via subscription   (Followers: 15, SJR: 2.558, h-index: 54)
Advances in Genome Biology     Full-text available via subscription   (Followers: 11)
Advances in Geophysics     Full-text available via subscription   (Followers: 6, SJR: 2.325, h-index: 20)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 18, SJR: 0.906, h-index: 24)
Advances in Heterocyclic Chemistry     Full-text available via subscription   (Followers: 8, SJR: 0.497, h-index: 31)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Imaging and Electron Physics     Full-text available via subscription   (Followers: 2, SJR: 0.396, h-index: 27)
Advances in Immunology     Full-text available via subscription   (Followers: 33, SJR: 4.152, h-index: 85)
Advances in Inorganic Chemistry     Full-text available via subscription   (Followers: 9, SJR: 1.132, h-index: 42)
Advances in Insect Physiology     Full-text available via subscription   (Followers: 3, SJR: 1.274, h-index: 27)
Advances in Integrative Medicine     Hybrid Journal   (Followers: 4)
Advances in Intl. Accounting     Full-text available via subscription   (Followers: 4)
Advances in Life Course Research     Hybrid Journal   (Followers: 7, SJR: 0.764, h-index: 15)
Advances in Lipobiology     Full-text available via subscription   (Followers: 1)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Marine Biology     Full-text available via subscription   (Followers: 16, SJR: 1.645, h-index: 45)
Advances in Mathematics     Full-text available via subscription   (Followers: 10, SJR: 3.261, h-index: 65)
Advances in Medical Sciences     Hybrid Journal   (Followers: 5, SJR: 0.489, h-index: 25)
Advances in Medicinal Chemistry     Full-text available via subscription   (Followers: 5)
Advances in Microbial Physiology     Full-text available via subscription   (Followers: 4, SJR: 1.44, h-index: 51)
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: 10)
Advances in Molecular Toxicology     Full-text available via subscription   (Followers: 6, SJR: 0.324, h-index: 8)
Advances in Nanoporous Materials     Full-text available via subscription   (Followers: 3)
Advances in Oncobiology     Full-text available via subscription   (Followers: 3)
Advances in Organometallic Chemistry     Full-text available via subscription   (Followers: 15, SJR: 2.885, h-index: 45)
Advances in Parallel Computing     Full-text available via subscription   (Followers: 7, SJR: 0.148, h-index: 11)
Advances in Parasitology     Full-text available via subscription   (Followers: 7, SJR: 2.37, h-index: 73)
Advances in Pediatrics     Full-text available via subscription   (Followers: 20, SJR: 0.4, h-index: 28)
Advances in Pharmaceutical Sciences     Full-text available via subscription   (Followers: 14)
Advances in Pharmacology     Full-text available via subscription   (Followers: 13, SJR: 1.718, h-index: 58)
Advances in Physical Organic Chemistry     Full-text available via subscription   (Followers: 7, SJR: 0.384, h-index: 26)
Advances in Phytomedicine     Full-text available via subscription  
Advances in Planar Lipid Bilayers and Liposomes     Full-text available via subscription   (Followers: 3, SJR: 0.248, h-index: 11)
Advances in Plant Biochemistry and Molecular Biology     Full-text available via subscription   (Followers: 8)
Advances in Plant Pathology     Full-text available via subscription   (Followers: 5)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Protein Chemistry     Full-text available via subscription   (Followers: 18)
Advances in Protein Chemistry and Structural Biology     Full-text available via subscription   (Followers: 17, SJR: 1.5, h-index: 62)
Advances in Psychology     Full-text available via subscription   (Followers: 56)
Advances in Quantum Chemistry     Full-text available via subscription   (Followers: 5, SJR: 0.478, h-index: 32)
Advances in Radiation Oncology     Open Access  
Advances in Small Animal Medicine and Surgery     Hybrid Journal   (Followers: 1, SJR: 0.1, h-index: 2)
Advances in Space Research     Full-text available via subscription   (Followers: 332, SJR: 0.606, h-index: 65)
Advances in Structural Biology     Full-text available via subscription   (Followers: 7)
Advances in Surgery     Full-text available via subscription   (Followers: 6, SJR: 0.823, h-index: 27)
Advances in the Study of Behavior     Full-text available via subscription   (Followers: 28, SJR: 1.321, h-index: 56)
Advances in Veterinary Medicine     Full-text available via subscription   (Followers: 14)
Advances in Veterinary Science and Comparative Medicine     Full-text available via subscription   (Followers: 12)
Advances in Virus Research     Full-text available via subscription   (Followers: 5, SJR: 1.878, h-index: 68)
Advances in Water Resources     Hybrid Journal   (Followers: 42, SJR: 2.408, h-index: 94)
Aeolian Research     Hybrid Journal   (Followers: 5, SJR: 0.973, h-index: 22)
Aerospace Science and Technology     Hybrid Journal   (Followers: 304, SJR: 0.816, h-index: 49)
AEU - Intl. J. of Electronics and Communications     Hybrid Journal   (Followers: 8, SJR: 0.318, h-index: 36)
African J. of Emergency Medicine     Open Access   (Followers: 4, SJR: 0.344, h-index: 6)
Ageing Research Reviews     Hybrid Journal   (Followers: 7, SJR: 3.289, h-index: 78)
Aggression and Violent Behavior     Hybrid Journal   (Followers: 390, SJR: 1.385, h-index: 72)
Agri Gene     Hybrid Journal  
Agricultural and Forest Meteorology     Hybrid Journal   (Followers: 15, SJR: 2.18, h-index: 116)
Agricultural Systems     Hybrid Journal   (Followers: 29, SJR: 1.275, h-index: 74)
Agricultural Water Management     Hybrid Journal   (Followers: 36, SJR: 1.546, h-index: 79)
Agriculture and Agricultural Science Procedia     Open Access  
Agriculture and Natural Resources     Open Access   (Followers: 1)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 48, SJR: 1.879, h-index: 120)
Ain Shams Engineering J.     Open Access   (Followers: 5, SJR: 0.434, h-index: 14)
Air Medical J.     Hybrid Journal   (Followers: 3, SJR: 0.234, h-index: 18)
AKCE Intl. J. of Graphs and Combinatorics     Open Access   (SJR: 0.285, h-index: 3)
Alcohol     Hybrid Journal   (Followers: 9, SJR: 0.922, h-index: 66)
Alcoholism and Drug Addiction     Open Access   (Followers: 5)
Alergologia Polska : Polish J. of Allergology     Full-text available via subscription   (Followers: 1)
Alexandria Engineering J.     Open Access   (Followers: 1, SJR: 0.436, h-index: 12)
Alexandria J. of Medicine     Open Access  
Algal Research     Partially Free   (Followers: 7, SJR: 2.05, h-index: 20)
Alkaloids: Chemical and Biological Perspectives     Full-text available via subscription   (Followers: 3)
Allergologia et Immunopathologia     Full-text available via subscription   (Followers: 1, SJR: 0.46, h-index: 29)
Allergology Intl.     Open Access   (Followers: 5, SJR: 0.776, h-index: 35)
ALTER - European J. of Disability Research / Revue Européenne de Recherche sur le Handicap     Full-text available via subscription   (Followers: 6, SJR: 0.158, h-index: 9)
Alzheimer's & Dementia     Hybrid Journal   (Followers: 45, SJR: 4.289, h-index: 64)
Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring     Open Access   (Followers: 5)
Alzheimer's & Dementia: Translational Research & Clinical Interventions     Open Access   (Followers: 3)
American Heart J.     Hybrid Journal   (Followers: 45, SJR: 3.157, h-index: 153)
American J. of Cardiology     Hybrid Journal   (Followers: 47, SJR: 2.063, h-index: 186)
American J. of Emergency Medicine     Hybrid Journal   (Followers: 34, SJR: 0.574, h-index: 65)
American J. of Geriatric Pharmacotherapy     Full-text available via subscription   (Followers: 6, SJR: 1.091, h-index: 45)
American J. of Geriatric Psychiatry     Hybrid Journal   (Followers: 14, SJR: 1.653, h-index: 93)
American J. of Human Genetics     Hybrid Journal   (Followers: 32, SJR: 8.769, h-index: 256)
American J. of Infection Control     Hybrid Journal   (Followers: 25, SJR: 1.259, h-index: 81)
American J. of Kidney Diseases     Hybrid Journal   (Followers: 31, SJR: 2.313, h-index: 172)
American J. of Medicine     Hybrid Journal   (Followers: 48, SJR: 2.023, h-index: 189)
American J. of Medicine Supplements     Full-text available via subscription   (Followers: 3)
American J. of Obstetrics and Gynecology     Hybrid Journal   (Followers: 174, SJR: 2.255, h-index: 171)
American J. of Ophthalmology     Hybrid Journal   (Followers: 51, SJR: 2.803, h-index: 148)
American J. of Ophthalmology Case Reports     Open Access   (Followers: 2)
American J. of Orthodontics and Dentofacial Orthopedics     Full-text available via subscription   (Followers: 6, SJR: 1.249, h-index: 88)
American J. of Otolaryngology     Hybrid Journal   (Followers: 22, SJR: 0.59, h-index: 45)
American J. of Pathology     Hybrid Journal   (Followers: 23, SJR: 2.653, h-index: 228)
American J. of Preventive Medicine     Hybrid Journal   (Followers: 21, SJR: 2.764, h-index: 154)
American J. of Surgery     Hybrid Journal   (Followers: 32, SJR: 1.286, h-index: 125)
American J. of the Medical Sciences     Hybrid Journal   (Followers: 13, SJR: 0.653, h-index: 70)
Ampersand : An Intl. J. of General and Applied Linguistics     Open Access   (Followers: 5)
Anaerobe     Hybrid Journal   (Followers: 4, SJR: 1.066, h-index: 51)
Anaesthesia & Intensive Care Medicine     Full-text available via subscription   (Followers: 52, SJR: 0.124, h-index: 9)
Anaesthesia Critical Care & Pain Medicine     Full-text available via subscription   (Followers: 3)
Anales de Cirugia Vascular     Full-text available via subscription  
Anales de Pediatría     Full-text available via subscription   (Followers: 2, SJR: 0.209, h-index: 27)
Anales de Pediatría (English Edition)     Full-text available via subscription  
Anales de Pediatría Continuada     Full-text available via subscription   (SJR: 0.104, h-index: 3)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 2, SJR: 2.577, h-index: 7)
Analytica Chimica Acta     Hybrid Journal   (Followers: 38, SJR: 1.548, h-index: 152)
Analytical Biochemistry     Hybrid Journal   (Followers: 154, SJR: 0.725, h-index: 154)
Analytical Chemistry Research     Open Access   (Followers: 7, SJR: 0.18, h-index: 2)
Analytical Spectroscopy Library     Full-text available via subscription   (Followers: 10)
Anesthésie & Réanimation     Full-text available via subscription  
Anesthesiology Clinics     Full-text available via subscription   (Followers: 21, SJR: 0.421, h-index: 40)
Angiología     Full-text available via subscription   (SJR: 0.124, h-index: 9)
Angiologia e Cirurgia Vascular     Open Access  
Animal Behaviour     Hybrid Journal   (Followers: 143, SJR: 1.907, h-index: 126)
Animal Feed Science and Technology     Hybrid Journal   (Followers: 5, SJR: 1.151, h-index: 83)
Animal Reproduction Science     Hybrid Journal   (Followers: 5, SJR: 0.711, h-index: 78)
Annales d'Endocrinologie     Full-text available via subscription   (SJR: 0.394, h-index: 30)
Annales d'Urologie     Full-text available via subscription  
Annales de Cardiologie et d'Angéiologie     Full-text available via subscription   (SJR: 0.177, h-index: 13)
Annales de Chirurgie de la Main et du Membre Supérieur     Full-text available via subscription  
Annales de Chirurgie Plastique Esthétique     Full-text available via subscription   (Followers: 2, SJR: 0.354, h-index: 22)
Annales de Chirurgie Vasculaire     Full-text available via subscription   (Followers: 1)

        1 2 3 4 5 6 7 8 | Last   [Sort by number of followers]   [Restore default list]

Journal Cover Analytic Methods in Accident Research
  [SJR: 2.577]   [H-I: 7]   [2 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 2213-6657
   Published by Elsevier Homepage  [3030 journals]
  • A random thresholds random parameters hierarchical ordered probit analysis
           of highway accident injury-severities
    • Authors: Grigorios Fountas; Panagiotis Ch. Anastasopoulos
      Pages: 1 - 16
      Abstract: Publication date: September 2017
      Source:Analytic Methods in Accident Research, Volume 15
      Author(s): Grigorios Fountas, Panagiotis Ch. Anastasopoulos
      This study uses highway accident data collected in the State of Washington, between 2011 and 2013, to study the factors that affect accident injury-severities. To account for the fixed thresholds limitation of the traditional ordered probability models – which typically leads to incorrect estimation of outcome probabilities for the intermediate categories – and for the possibility of unobserved factors systematically varying across the observations, a random thresholds hierarchical ordered probit model with random parameters is estimated. This approach simultaneously allows the explanatory parameters to vary across roadway segments, and the thresholds to vary both as a function of explanatory parameters and across the observations, thus accounting for unobserved and threshold heterogeneity, respectively. Using goodness-of-fit measures, likelihood ratio tests and forecasting accuracy measures, the model estimation results are compared with the hierarchical and fixed thresholds ordered probit model counterparts, with fixed and random parameters. The comparative assessment among the ordered probit modeling approaches reveals the relative benefits and the overall statistical superiority of the random thresholds random parameters hierarchical ordered probit model.

      PubDate: 2017-04-27T06:04:05Z
      DOI: 10.1016/j.amar.2017.03.002
      Issue No: Vol. 15 (2017)
       
  • A negative binomial crash sum model for time invariant heterogeneity in
           panel crash data: Some insights
    • Authors: Ghasak I.M.A. Mothafer; Toshiyuki Yamamoto; Venkataraman N. Shankar
      Pages: 1 - 9
      Abstract: Publication date: June 2017
      Source:Analytic Methods in Accident Research, Volume 14
      Author(s): Ghasak I.M.A. Mothafer, Toshiyuki Yamamoto, Venkataraman N. Shankar
      This paper presents a negative binomial crash sum model as an alternative for modeling time invariant heterogeneity in short panel crash data. Time invariant heterogeneity arising through multiple years of observation for each segment is viewed as a common unobserved effect at the segment level, and typically treated with panel models involving fixed or random effects. Random effects model unobserved heterogeneity through the error term, typically following a gamma or normal distribution. We take advantage of the fact that gamma heterogeneity in a multi-period Poisson count modeling framework is equivalent to a negative binomial distribution for a dependent variable which is the summation of crashes across years. The Poisson panel model referred to in this paper is the random effects Poisson gamma (REPG). In the REPG model, the dependent variable is an annual number of a specific crash type. The multi-year crash sum model is a negative binomial (NB) model that is based on three consecutive years of crash data (2005–2007). In the multi-year crash sum model, the dependent variable is the sum of crashes of a specific type for the three-year period. Four categories (in addition to total crashes) of crash types are considered in this study including rear end, sideswipe, fixed objects and all-other types. The empirical results show that when time effects are insignificant in short panels such as the one used in this study, the three-year crash sum model is a computationally simpler alternative to a panel model for modeling time invariant heterogeneity while imposing fewer data requirements such as annual measurements.

      PubDate: 2017-01-22T22:41:36Z
      DOI: 10.1016/j.amar.2016.12.003
      Issue No: Vol. 14 (2017)
       
  • A multivariate spatial model of crash frequency by transportation modes
           for urban intersections
    • Authors: Helai Huang; Hanchu Zhou; Jie Wang; Fangrong Chang; Ming Ma
      Pages: 10 - 21
      Abstract: Publication date: June 2017
      Source:Analytic Methods in Accident Research, Volume 14
      Author(s): Helai Huang, Hanchu Zhou, Jie Wang, Fangrong Chang, Ming Ma
      This study proposes a multivariate spatial model to simultaneously analyze the occurrence of motor vehicle, bicycle and pedestrian crashes at urban intersections. The proposed model can account for both the correlation among different modes involved in crashes at individual intersections and spatial correlation between adjacent intersections. According to the results of the model comparison, multivariate spatial model outperforms the univariate spatial model and the multivariate model in the goodness-of-fit. The results confirm the highly correlated heterogeneous residuals in modeling crash risk among motor vehicles, bicycles and pedestrians. In regard to spatial correlation, the estimates of variance for spatial correlations of all three crash modes in the multivariate and univariate models are statistically significant; however, the correlations for spatial residuals between different crash modes at adjacent sites are not statistically significant. More interestingly, the results show that the proportion of variation explained by the spatial effects is much higher for motor vehicle crashes than for bicycle and pedestrian crashes, which indicates spatial correlations between adjacent intersections are significantly different between the motor vehicle and non-motorized modes.

      PubDate: 2017-01-22T22:41:36Z
      DOI: 10.1016/j.amar.2017.01.001
      Issue No: Vol. 14 (2017)
       
  • A Modified Rank Ordered Logit model to analyze injury severity of
           occupants in multivehicle crashes
    • Authors: Shelley Bogue; Rajesh Paleti; Lacramioara Balan
      Pages: 22 - 40
      Abstract: Publication date: June 2017
      Source:Analytic Methods in Accident Research, Volume 14
      Author(s): Shelley Bogue, Rajesh Paleti, Lacramioara Balan
      The current study developed a simultaneous model of injury severity outcomes of all occupants in multi-vehicle crashes including all the drivers and the passengers of all vehicles involved in a crash. Specifically, a Modified Rank Ordered Logit (MROL) methodology that can predict the relative order of occupant injury severity as well as the actual injury severity was developed. The final model captures the effects of several key occupant, vehicle, and accident level variables on four possible levels of injury severity. The results indicate the presence of accident-specific unobserved factors that influence the severity outcomes of all people involved in the crash as well as unobserved heterogeneity in the effect of key covariates including occupant’s gender and speed limit. The performance of the MROL model was compared with the traditional mixed multinomial logit (MMNL) model that is the most commonly used model for injury severity analysis. Overall, the results demonstrate superior predictive ability of the MROL model in comparison to the MMNL model. The traditional MMNL model performed satisfactory in terms of replicating the simple shares of different injury severity levels across all occupants. However, the performance of the MMNL model dropped significantly when the observed and predicted shares were compared for combinations of injury severity levels among crashes involving multiple occupants. Lastly, elasticity effects were computed to demonstrate considerably different policy implications of the MROL and MMNL models.

      PubDate: 2017-03-13T00:29:02Z
      DOI: 10.1016/j.amar.2017.03.001
      Issue No: Vol. 14 (2017)
       
  • The effect of variations in spatial units on unobserved heterogeneity in
           macroscopic crash models
    • Authors: Richard Amoh-Gyimah; Meead Saberi; Majid Sarvi
      Pages: 28 - 51
      Abstract: Publication date: March 2017
      Source:Analytic Methods in Accident Research, Volume 13
      Author(s): Richard Amoh-Gyimah, Meead Saberi, Majid Sarvi
      Macroscopic safety models establish a relationship between crashes and the contributing factors in a defined spatial unit. Negative binomial (NB) and Bayesian negative binomial models with conditional autoregressive prior (CAR) are techniques widely used to establish this relationship. However, these models do not account for unobserved heterogeneity and their output is global and fixed irrespective of the spatial unit of the analysis. There is a timely need to understand how variations in spatial units affect unobserved heterogeneity. This study uses two advanced modeling techniques, the random parameter negative binomial (RPNB) and the semi-parametric geographically weighted Poisson regression (S-GWPR), to investigate whether explanatory variables found to be significant and random in one spatial aggregation will remain significant and random when another spatial aggregation is used. The key finding is that variations in spatial units do have an impact on unobserved heterogeneity. We also found that variations in spatial units have a greater impact on unobserved heterogeneity in the RPNB models compared to the S-GWPR models. We found that the S-GWPR model performs better than the RPNB model with the lowest value of mean absolute deviation (MAD) and Akaiki information criterion (AIC) but the two modeling techniques produce similar results in terms of the sign of the coefficients across the selected spatial units of analysis. Overall, the study provides a methodological basis for assessing the impact of spatial units on unobserved heterogeneity.

      PubDate: 2017-01-22T22:41:36Z
      DOI: 10.1016/j.amar.2016.11.001
      Issue No: Vol. 13 (2017)
       
  • Grouped random parameters bivariate probit analysis of perceived and
           observed aggressive driving behavior: A driving simulation study
    • Authors: Md Tawfiq Sarwar; Panagiotis Ch. Anastasopoulos; Nima Golshani; Kevin F. Hulme
      Pages: 52 - 64
      Abstract: Publication date: March 2017
      Source:Analytic Methods in Accident Research, Volume 13
      Author(s): Md Tawfiq Sarwar, Panagiotis Ch. Anastasopoulos, Nima Golshani, Kevin F. Hulme
      This paper uses driving simulation data and surveys conducted in 2014 and 2015 in Buffalo, NY, to study the factors that affect perceived (self-reported, based on surveys) and observed (as measured, based on driving simulation experiments) aggressive driving behavior. Perceived and observed aggressive driving behavior are likely to share unobserved characteristics. To simultaneously account for this cross-equation error correlation, and for unobserved heterogeneity and panel data effects, a grouped random parameters bivariate probit model is estimated. The results control and account for a number of socio-demographic, driving experience and exposure, and behavioral and other characteristics. The findings reveal that different variables play in how aggressive driving behavior is perceived and observed, and the results imply that some drivers may perceive their driving behavior as non-aggressive when it is aggressive (or the opposite). The grouped random parameters bivariate probit model results are compared to their univariate probit, full information maximum likelihood bivariate probit, bivariate probit model with random effects, and random parameters bivariate probit model counterparts, and the results reveal the statistical superiority of the former, in terms of explanatory power, model fit, and forecasting accuracy.

      PubDate: 2017-01-22T22:41:36Z
      DOI: 10.1016/j.amar.2016.12.001
      Issue No: Vol. 13 (2017)
       
  • Multilevel Dirichlet process mixture analysis of railway grade crossing
           crash data
    • Authors: Shahram Heydari; Liping Fu; Dominique Lord; Bani K. Mallick
      Pages: 27 - 43
      Abstract: Publication date: March 2016
      Source:Analytic Methods in Accident Research, Volume 9
      Author(s): Shahram Heydari, Liping Fu, Dominique Lord, Bani K. Mallick
      This article introduces a flexible Bayesian semiparametric approach to analyzing crash data that are of hierarchical or multilevel nature. We extend the traditional varying intercept (random effects) multilevel model by relaxing its standard parametric distributional assumption. While accounting for unobserved cross-group heterogeneity in the data through intercept, the proposed method allows identifying latent subpopulations (and consequently outliers) in data based on a Dirichlet process mixture. It also allows estimating the number of latent subpopulations using an elegant mathematical structure instead of prespecifying this number arbitrarily as in conventional latent class or finite mixture models. In this paper, we evaluate our method on two recent railway grade crossing crash datasets, at province and municipality levels, from Canada for the years 2008–2013. We use cross-validation predictive densities and pseudo-Bayes factor for Bayesian model selection. While confirming the need for a multilevel modeling approach for both datasets, the results reveal the inadequacy of the standard parametric assumption in the varying intercept model for the municipality-level dataset. In fact, our proposed method is shown to improve model fitting significantly for the latter data. In a fully probabilistic framework, we also identify the expected number of latent clusters that share similar unidentified features among Canadian provinces and municipalities. It is possible thus to further investigate the reasons for such similarities and dissimilarities. This can have important policy implications for various safety management programs.

      PubDate: 2016-03-09T07:18:50Z
      DOI: 10.1016/j.amar.2016.02.001
      Issue No: Vol. 9 (2016)
       
  • Fast Bayesian inference for modeling multivariate crash counts
    • Authors: Volodymyr Serhiyenko; Sha A. Mamun; John N. Ivan; Nalini Ravishanker
      Pages: 44 - 53
      Abstract: Publication date: March 2016
      Source:Analytic Methods in Accident Research, Volume 9
      Author(s): Volodymyr Serhiyenko, Sha A. Mamun, John N. Ivan, Nalini Ravishanker
      This paper investigates the multivariate Poisson Lognormal modeling of counts for different types of crashes. This multivariate model can account for the overdispersion as well as positive and/or negative association between counts. Approximate Bayesian inference via the Integrated Nested Laplace Approximations significantly decreases computational time which makes it attractive for researchers. The models are developed for single vehicle, same direction and opposite direction crash types using three years (2009–2011) of crash data on Connecticut divided limited access highway segments. Annual average daily traffic, segment length, and road specific covariates (median type, shoulder width, area type, and on-ramp indicator) are used as predictor variables. The results from the multivariate Poisson Lognormal model suggest that an increase in the annual average daily traffic, segment length, and shoulder width together with urban area type and presence of an on-ramp are associated with in an increase in crashes. The median type covariate has a mixed effect for different median types on different type of crashes. The multivariate Poisson Lognormal model results are compared with the results obtained from two univariate regression models, univariate Poisson Lognormal and univariate negative binomial, with respect to model implications and precision on analysis of crash counts. The results show that the coefficient estimates of predictors have almost similar effects across all three crash type count models; however, standard errors in the multivariate Poisson Lognormal model are smaller than standard errors from other two univariate models in most cases. Results on posterior means for the correlation coefficients between crash types indicate that there are significant correlations exist between the crash count vectors, which indicate that ignoring such a correlation could possibly lead to incorrect variance estimation for the parameters. Results on predicted mean absolute error (PMAE) indicate that Bayesian multivariate Poisson Lognormal model provides up to 33% less prediction error compared to the univariate negative binomial model, although there are no significant difference of PMAE values between multivariate and univariate Poisson Lognormal models results. The analysis results demonstrated that the Bayesian multivariate Poisson Lognormal model provides correct estimates for parameters in predicting crash counts by accounting for correlations in the multivariate crash counts.

      PubDate: 2016-03-09T07:18:50Z
      DOI: 10.1016/j.amar.2016.02.002
      Issue No: Vol. 9 (2016)
       
  • The effect of long term non-invasive pavement deterioration on accident
           injury-severity rates: A seemingly unrelated and multivariate equations
           approach
    • Authors: Md Tawfiq Sarwar; Panagiotis Ch. Anastasopoulos
      Pages: 1 - 15
      Abstract: Publication date: March 2017
      Source:Analytic Methods in Accident Research, Volume 13
      Author(s): Md Tawfiq Sarwar, Panagiotis Ch. Anastasopoulos
      This paper seeks to measure the effect of long term non-invasive pavement deterioration on accident injury-severity rates, and demonstrate the potential of considering safety as one of the criteria in the pavement management decision making process. Using data from Indiana, a system of seemingly unrelated regression equations (SURE) is estimated to predict pavement deterioration curves over a 30-year projection period based on three commonly used pavement performance indicators. The annual predictors of the pavement roughness, rutting depth, and pavement condition rating are then used in a multivariate tobit equations model of vehicle accident injury-severity rates. The results provide the expected change of the no injury, injury, and fatality rates, due to the non-invasive pavement deterioration, and are compared to a budget-unrestricted scenario under which rehabilitation occurs routinely. Even though the aim of the paper is not to provide an optimal pavement management program, the findings suggest that safety should be considered as one of the decision making criteria.

      PubDate: 2016-11-19T06:27:33Z
      DOI: 10.1016/j.amar.2016.10.003
      Issue No: Vol. 13 (2016)
       
  • Using a flexible multivariate latent class approach to model correlated
           outcomes: A joint analysis of pedestrian and cyclist injuries
    • Authors: Shahram Heydari; Liping Fu; Luis F. Miranda-Moreno; Lawrence Joseph
      Pages: 16 - 27
      Abstract: Publication date: March 2017
      Source:Analytic Methods in Accident Research, Volume 13
      Author(s): Shahram Heydari, Liping Fu, Luis F. Miranda-Moreno, Lawrence Joseph
      Several recent transportation safety studies have indicated the importance of accounting for correlated outcomes, for example, among different crash types, including differing injury-severity levels. In this paper, we discuss inference for such data by introducing a flexible Bayesian multivariate model. In particular, we use a Dirichlet process mixture to keep the dependence structure unconstrained, relaxing the usual homogeneity assumptions. The resulting model collapses into a latent class multivariate model that is in the form of a flexible mixture of multivariate normal densities for which the number of mixtures (latent components) not only can be large but also can be inferred from the data as part of the analysis. Therefore, besides accounting for correlation among crash types through a heterogeneous correlation structure, the proposed model helps address unobserved heterogeneity through its latent class component. To our knowledge, this is the first study to propose and apply such a model in the transportation literature. We use the model to investigate the effects of various factors such as built environment characteristics on pedestrian and cyclist injury counts at signalized intersections in Montreal, modeling both outcomes simultaneously. We demonstrate that the homogeneity assumption of the standard multivariate model does not hold for the dataset used in this study. Consequently, we show how such a spurious assumption affects predictive performance of the model and the interpretation of the variables based on marginal effects. Our flexible model better captures the underlying complex structure of the correlated data, resulting in a more accurate model that contributes to a better understanding of safety correlates of non-motorist road users. This in turn helps decision-makers in selecting more appropriate countermeasures targeting vulnerable road users, promoting the mobility and safety of active modes of transportation.

      PubDate: 2016-12-25T16:07:29Z
      DOI: 10.1016/j.amar.2016.12.002
      Issue No: Vol. 13 (2016)
       
  • An empirical assessment of the effects of economic recessions on
           pedestrian-injury crashes using mixed and latent-class models
    • Authors: Ali Behnood; Fred L. Mannering
      Pages: 1 - 17
      Abstract: Publication date: December 2016
      Source:Analytic Methods in Accident Research, Volume 12
      Author(s): Ali Behnood, Fred L. Mannering
      This study explores the differences in pedestrian injury severity in three distinct economic time periods from the recent global recession (the Great Recession): pre-recession, recession, and post-recession. Using data from pedestrian crashes in Chicago, Illinois over an eight-year period, separate time-period models of pedestrian-injury severities (with possible outcomes of severe injury, moderate injury, and minor injury) were estimated using latent-class logit and mixed logit models. Likelihood ratio tests were conducted to examine the overall stability of model estimates across time periods and marginal effects of each explanatory variable were also considered to investigate the temporal stability of the effect of individual parameter estimates on pedestrian injury-severity probabilities. A wide range of variables potentially affecting injury severities was considered including time, location, and severity of crashes, as well as data on roadway and environmental conditions, pedestrian characteristics, and crash characteristics. Our findings show significant temporal instability, which likely results from a combination of the economic recession and the long-term evolution of the influence of factors that affect pedestrian-injury severity. Understanding and explicitly modeling the evolution of driver and pedestrian behavior is a promising direction for future research, but this would unfortunately require far more extensive data than is currently available in traditional safety databases.

      PubDate: 2016-08-09T15:38:46Z
      DOI: 10.1016/j.amar.2016.07.002
      Issue No: Vol. 12 (2016)
       
  • Bayesian nonparametric modeling in transportation safety studies:
           Applications in univariate and multivariate settings
    • Authors: Shahram Heydari; Liping Fu; Lawrence Jopseph; Luis F. Miranda-Moreno
      Pages: 18 - 34
      Abstract: Publication date: December 2016
      Source:Analytic Methods in Accident Research, Volume 12
      Author(s): Shahram Heydari, Liping Fu, Lawrence Jopseph, Luis F. Miranda-Moreno
      In transportation safety studies, it is often necessary to account for unobserved heterogeneity and multimodality in data. The commonly used standard or over-dispersed generalized linear models (e.g., negative binomial models) do not fully address unobserved heterogeneity, assuming that crash frequencies follow unimodal exponential families of distributions. This paper employs Bayesian nonparametric Dirichlet process mixture models demonstrating some of their major advantages in transportation safety studies. We examine the performance of the proposed approach using both simulated and real data. We compare the proposed model with other models commonly used in road safety literature including the Poisson-Gamma, random effects, and conventional latent class models. We use pseudo Bayes factors as the goodness-of-fit measure, and also examine the performance of the proposed model in terms of replicating datasets with high proportions of zero crashes. In a multivariate setting, we extend the standard multivariate Poisson-lognormal model to a more flexible Dirichlet process mixture multivariate model. We allow for interdependence between outcomes through a nonparametric random effects density. Finally, we demonstrate how the robustness to parametric distributional assumptions (usually the multivariate normal density) can be examined using a mixture of points model when different (multivariate) outcomes are modeled jointly.

      PubDate: 2016-10-15T01:16:26Z
      DOI: 10.1016/j.amar.2016.09.001
      Issue No: Vol. 12 (2016)
       
  • Safety-oriented pavement performance thresholds: Accounting for unobserved
           heterogeneity in a multi-objective optimization and goal programming
           approach
    • Authors: Panagiotis Ch. Anastasopoulos; Md Tawfiq Sarwar; Venky N. Shankar
      Pages: 35 - 47
      Abstract: Publication date: December 2016
      Source:Analytic Methods in Accident Research, Volume 12
      Author(s): Panagiotis Ch. Anastasopoulos, Md Tawfiq Sarwar, Venky N. Shankar
      The cornerstone of transportation infrastructure asset management is managing the physical infrastructure, with pavement preservation being one of the most critical and costly assets. Preserving pavements in an appropriate manner extends their service life, and most importantly improves motorists’ safety and satisfaction while saving public tax dollars. To that end, this paper presents a methodology to estimate pavement performance thresholds that are cost-effective and safe for users. Using data from Indiana, the relationships of the three criteria, i.e., safety (accident rates), normalized treatment cost and pavement service life, with the pavement performance (roughness, rutting, overall rating, and surface deflection), road geometry, traffic characteristics and climate - are investigated and estimated. These relationships are utilized in a multi-objective optimization and goal-programming scheme to identify performance threshold values that trigger preservation treatments. These analytically determined threshold values are found to be comparable to historical thresholds and thresholds derived from experts’ and users’ opinions.

      PubDate: 2016-11-05T02:39:30Z
      DOI: 10.1016/j.amar.2016.10.001
      Issue No: Vol. 12 (2016)
       
  • The Palm distribution of traffic conditions and its application to
           accident risk assessment
    • Authors: Ilkka Norros; Pirkko Kuusela; Satu Innamaa; Eetu Pilli-Sihvola; Riikka Rajamäki
      Pages: 48 - 65
      Abstract: Publication date: December 2016
      Source:Analytic Methods in Accident Research, Volume 12
      Author(s): Ilkka Norros, Pirkko Kuusela, Satu Innamaa, Eetu Pilli-Sihvola, Riikka Rajamäki
      We introduce a method for assessing the influence of various road, weather and traffic conditions on traffic accidents. The idea is to contrast the distribution of conditions as seen by the driver involved in an accident with their distribution as seen by an arbitrary driver. The latter is considered as a variant of the notion of Palm probability of a point process, and it is easy to compute when road, weather and traffic measurement data are available. The method includes straightforward assessment of the statistical significance of the findings. We then study a single large example case, Ring-road I in Helsinki observed over five years, and present a comprehensive analysis of the influence of traffic, road and weather conditions on traffic accidents. Our results are in line with existing knowledge; for example, the traffic volume as such has hardly any influence on accidents, whereas the afternoon rush hours are considerably more risky than the morning ones, and heavy rain and snowfall as well as reduced visibility in general increase the accident risk substantially. The notion of Palm probability offers a transparent and uniform approach to such questions, and the proposed approach can be applied as a semi-automatic risk assessment tool prior to deeper analyses.

      PubDate: 2016-11-05T02:39:30Z
      DOI: 10.1016/j.amar.2016.10.002
      Issue No: Vol. 12 (2016)
       
  • Unobserved heterogeneity and the statistical analysis of highway accident
           data
    • Authors: Fred L. Mannering; Venky Shankar; Chandra R. Bhat
      Pages: 1 - 16
      Abstract: Publication date: September 2016
      Source:Analytic Methods in Accident Research, Volume 11
      Author(s): Fred L. Mannering, Venky Shankar, Chandra R. Bhat
      Highway accidents are complex events that involve a variety of human responses to external stimuli, as well as complex interactions between the vehicle, roadway features/condition, traffic-related factors, and environmental conditions. In addition, there are complexities involved in energy dissipation (once an accident has occurred) that relate to vehicle design, impact angles, the physiological characteristics of involved humans, and other factors. With such a complex process, it is impossible to have access to all of the data that could potentially determine the likelihood of a highway accident or its resulting injury severity. The absence of such important data can potentially present serious specification problems for traditional statistical analyses that can lead to biased and inconsistent parameter estimates, erroneous inferences and erroneous accident predictions. This paper presents a detailed discussion of this problem (typically referred to as unobserved heterogeneity) in the context of accident data and analysis. Various statistical approaches available to address this unobserved heterogeneity are presented along with their strengths and weaknesses. The paper concludes with a summary of the fundamental issues and directions for future methodological work that addresses unobserved heterogeneity.

      PubDate: 2016-05-10T14:43:03Z
      DOI: 10.1016/j.amar.2016.04.001
      Issue No: Vol. 11 (2016)
       
  • Random parameters multivariate tobit and zero-inflated count data models:
           Addressing unobserved and zero-state heterogeneity in accident
           injury-severity rate and frequency analysis
    • Authors: Panagiotis Ch. Anastasopoulos
      Pages: 17 - 32
      Abstract: Publication date: September 2016
      Source:Analytic Methods in Accident Research, Volume 11
      Author(s): Panagiotis Ch. Anastasopoulos
      This paper uses data collected over a five-year period between 2005 and 2009 in Indiana to estimate random parameters multivariate tobit and zero-inflated count data models of accident injury-severity rates and frequencies, respectively. The proposed modeling approach accounts for unobserved factors that may vary systematically across segments with and without observed or reported accident injury-severities, thus addressing unobserved, zero-accident state and non-zero-accident state heterogeneity. Moreover, the multivariate setting allows accounting for contemporaneous cross-equation error correlation for modeling accident injury-severity rates and frequencies as systems of seemingly unrelated equations. The tobit and zero-inflated count data modeling approaches address the excessive amount of zeros inherent in the two sets of dependent variables (accident injury-severity rates and frequencies, respectively), which are – in nature – continuous and discrete count data, respectively, that are left-censored with a clustering at zero. The random parameters multivariate tobit and zero-inflated count data models are counter-imposed with their equivalent fixed parameters and lower order models, and the results illustrate the statistical superiority of the presented models. Finally, the relative benefits of random parameters modeling are explored by demonstrating the forecasting accuracy of the random parameters multivariate models with the software-generated mean β s of the random parameters, and with the observation-specific β s of the random parameters.

      PubDate: 2016-07-24T01:13:58Z
      DOI: 10.1016/j.amar.2016.06.001
      Issue No: Vol. 11 (2016)
       
  • Analysis of occupant injury severity in winter weather crashes: A fully
           Bayesian multivariate approach
    • Authors: Mohammad Saad Shaheed; Konstantina Gkritza; Alicia L. Carriquiry; Shauna L. Hallmark
      Pages: 33 - 47
      Abstract: Publication date: September 2016
      Source:Analytic Methods in Accident Research, Volume 11
      Author(s): Mohammad Saad Shaheed, Konstantina Gkritza, Alicia L. Carriquiry, Shauna L. Hallmark
      Multivariate injury severity models that consider the cross-group heterogeneity in the crash data where individuals or occupants are nested within vehicles and vehicles are nested within crashes are limited in the literature. Most previous studies on crash injury severity were conducted at the crash level ignoring the potential correlation in severity for the vehicles involved in the same crashes or individuals involved in the same vehicles. Ignoring these correlation and dependence effects might result in underestimation of standard errors and erroneous inferences. The objective of this paper is to correctly determine the factors affecting occupant injury severity in winter seasons by addressing the within-crash and between-crash correlation of injury severity. To achieve this, fully Bayesian hierarchical multinomial logit models were developed for estimating occupant injury severity in weather-related crashes, non weather-related crashes, and all crashes. These models were developed using disaggregate crash data with occupants nested within crashes for four winter seasons in Iowa. Significant factors affecting occupant injury severity included factors related to occupants (gender, seating position, occupant trapped status, ejection status, and occupant protection used), as well as crash-level factors (road junction type, first harmful event and major cause of crash). Weather-related variables, such as visibility, pavement and air temperature, were also significant factors in winter weather crashes. Interaction effects involving crash-level variables and occupant-level variables were also found significant. Overall, the model diagnostics suggested significant within-crash correlation in the study dataset justifying the use of a multivariate model specification that addresses multivariate error term correlation issues.

      PubDate: 2016-07-24T01:13:58Z
      DOI: 10.1016/j.amar.2016.06.002
      Issue No: Vol. 11 (2016)
       
  • Modeling the equivalent property damage only crash rate for road segments
           using the hurdle regression framework
    • Authors: Lu Ma; Xuedong Yan; Chong Wei; Jiangfeng Wang
      Pages: 48 - 61
      Abstract: Publication date: September 2016
      Source:Analytic Methods in Accident Research, Volume 11
      Author(s): Lu Ma, Xuedong Yan, Chong Wei, Jiangfeng Wang
      The understanding of the distributional characteristics of the equivalent property damage only (EPDO) crash rate is limited in the existing literature. Models without a proper distribution of EPDO rate could result in biased estimations and misinterpretations of factors. The importance of prediction accuracy and modeling performance for the EPDO rate should be acknowledged since they directly affect the allocation of limited public funds to safety management for road networks. The general objective of this study is to investigate the distributional characteristics of the EPDO rate and accordingly develop proper econometric models for connecting the EPDO rate to explanatory variables. A hurdle framework was proposed in order to accommodate the zero-positive mixed domain of the EPDO rate. For the positive part of the EPDO rate, three representative distributions (lognormal, gamma and normal) were tested and then the three hurdle models were compared against the Tobit model and the random-parameters Tobit model. The empirical results illustrate the lognormal hurdle model's superior modeling performance in comparison to the other four models, and more importantly that conclusion also holds for several different definitions of the EPDO rate under different combinations of property damage only (PDO) equivalency factors.

      PubDate: 2016-08-13T17:15:41Z
      DOI: 10.1016/j.amar.2016.07.001
      Issue No: Vol. 11 (2016)
       
  • The effect of speed limits on drivers' choice of speed: A random
           parameters seemingly unrelated equations approach
    • Authors: Panagiotis Ch. Anastasopoulos; Fred L. Mannering
      Pages: 1 - 11
      Abstract: Publication date: June 2016
      Source:Analytic Methods in Accident Research, Volume 10
      Author(s): Panagiotis Ch. Anastasopoulos, Fred L. Mannering
      Drivers’ choice of speed has long been known to be a critical factor in both the likelihood and severity of vehicle crashes. Given this, understanding drivers’ choice of speed and the possible effect that posted speed limits may have on this choice, is a critical element of safety research. This paper seeks to provide new insights on drivers’ speed-choice process by studying U.S. interstate highways (all of which are constructed to the same design-speed standard) under three distinct speed limits (55mi/h, 65mi/h and 70mi/h). Using a survey of interstate drivers that asked respondents their normal operating speed on interstates posted with these speed limits (under light traffic conditions), a random parameters seemingly unrelated regression estimation approach is used to account for both the interrelation among the choices under the three speed limits and for the unobserved heterogeneity across respondents. The estimation results show that a wide variety of factors influence the choice of speed in the presence of speed limits, including driver age, gender, marital status, number of children, driver education level, household income, age when the driver was first licensed, and opinions about pavement quality. The findings in this paper have important implications relating to the factors that may affect speed-limit compliance, and also demonstrate the methodological potential of the random parameters seemingly unrelated regression estimation approach to address a number of safety-related problems involving a series of inter-related continuous dependent variables.

      PubDate: 2016-04-09T12:09:30Z
      DOI: 10.1016/j.amar.2016.03.001
      Issue No: Vol. 10 (2016)
       
  • Modeling nonlinear relationship between crash frequency by severity and
           contributing factors by neural networks
    • Authors: Qiang Zeng; Helai Huang; Xin Pei; S.C. Wong
      Pages: 12 - 25
      Abstract: Publication date: June 2016
      Source:Analytic Methods in Accident Research, Volume 10
      Author(s): Qiang Zeng, Helai Huang, Xin Pei, S.C. Wong
      This study develops neural network models to explore the nonlinear relationship between crash frequency by severity and risk factors. To eliminate the possibility of over-fitting and to deal with black-box characteristic, a network structure optimization and a rule extraction method are proposed. A case study compares the performance of the modified neural network models with that of the traditional multivariate Poisson-lognormal model for predicting crash frequency by severity on road segments in Hong Kong. The results indicate that the trained and optimized neural networks have better fitting and predictive performance than the multivariate Poisson-lognormal model. Moreover, the smaller differences between training and testing errors in the optimized neural networks with pruned input and hidden nodes demonstrate the ability of the structure optimization algorithm to identify insignificant factors and to improve the model's generalizability. Furthermore, two rule-sets are extracted from the optimized neural networks to explicitly reveal the exact effect of each significant explanatory variable on the crash frequency by severity under different conditions. The rules imply that there is a nonlinear relationship between risk factors and crash frequencies with each injury-severity outcome. With the structure optimization algorithm and rule extraction method, the modified neural network models have great potential for modeling crash frequency by severity, and should be considered a good alternative for road safety analysis.

      PubDate: 2016-04-09T12:09:30Z
      DOI: 10.1016/j.amar.2016.03.002
      Issue No: Vol. 10 (2016)
       
  • A spatially autoregressive and heteroskedastic space-time pedestrian
           exposure modeling framework with spatial lags and endogenous network
           topologies
    • Authors: Jungyeol Hong; Venky N. Shankar; Narayan Venkataraman
      Pages: 26 - 46
      Abstract: Publication date: June 2016
      Source:Analytic Methods in Accident Research, Volume 10
      Author(s): Jungyeol Hong, Venky N. Shankar, Narayan Venkataraman
      The main objective of this study is to derive a modeling framework for characterizing the space-time exposure of pedestrians in crosswalks, where the spatial measure is characterized by pedestrian density and the temporal measure is characterized by crosswalk time occupancy. This characterization has not been observed in the literature, but is a characterization that allows one to differentiate the components of pedestrian exposure with enhanced resolution in space and time. However, real-time observations to generate space-time data are time consuming and expensive over a large urban network. A hybrid microsimulation-statistical approach is utilized for data generation and statistical analysis in this study. The exposure models predicting crosswalk density and occupancy were estimated using spatial autoregressive models with spatial lags, autoregressive and heteroskedastic spatial disturbances and endogenous regressors. An instrumental variables generalized method of moments (IV-GMM) approach was used for estimation, and the spatial models account for spatial dependence among crosswalks through the estimation of spatial lag and spatial correlation parameters. In a case study of the downtown crosswalk grid in Seattle, Washington, 688 crosswalks were modeled using ten network topology measures capturing node degree, centrality, clustering, modularity, attractiveness and eccentricity measures. The models utilized these network topology variables to account for stochasticity in network design effects on pedestrian dynamics. Several important findings resulted from this study. First, and most important, it was found that network topology measures had an endogenous impact on pedestrian density. Second, the pedestrian time occupancy equation is characterized by endogenous selection effects. That is, in crosswalks with persistent pedestrian volumes and positive densities, the impact of pedestrian trip generation volumes and pedestrian density were corrected for endogeneity and selection bias. The combined results of the pedestrian density and time occupancy equations indicate that endogeneity and selection bias are critical issues that should not be ignored in pedestrian exposure modeling. Pedestrian trip generation volumes representing block level facility generation were found to be elastic. This finding indicates the utility of our modeling framework for estimating the impact of land use on pedestrian space-time exposure at the block level. Out-of-sample prediction tests of the density and time occupancy models and comparisons with pedestrian count data from field observations indicated substantial predictive accuracies. Finally, it was determined that degree and hub were highly sensitive network design parameters in terms of their influence on density. The average total impact (marginal effect) of these measures indicates that attention should be paid to crosswalk network design from the standpoint of degree and hub characteristics. These results show that our space-time density-occupancy modeling framework is a plausible and efficient predictive tool that can be used to estimate pedestrian crosswalk exposure using building level and network topology data alone. We find that the IV-GMM technique is a useful approach for the emergent problem of inference in hybrid simulation-statistical transportation datasets, due to fewer assumptions on distributional assumptions about the data, while accounting for statistical effects relating to endogeneity, potential selection effects and heteroscedasticity.

      PubDate: 2016-05-18T01:34:41Z
      DOI: 10.1016/j.amar.2016.05.001
      Issue No: Vol. 10 (2016)
       
 
 
JournalTOCs
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Email: journaltocs@hw.ac.uk
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
 
Home (Search)
Subjects A-Z
Publishers A-Z
Customise
APIs
Your IP address: 54.145.81.105
 
About JournalTOCs
API
Help
News (blog, publications)
JournalTOCs on Twitter   JournalTOCs on Facebook

JournalTOCs © 2009-2016