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HEALTH AND SAFETY (509 journals)                  1 2 3 | Last

Showing 1 - 200 of 203 Journals sorted alphabetically
16 de Abril     Open Access  
A Life in the Day     Hybrid Journal   (Followers: 9)
Acta Informatica Medica     Open Access   (Followers: 1)
Acta Scientiarum. Health Sciences     Open Access  
Adultspan Journal     Hybrid Journal   (Followers: 1)
Advances in Child Development and Behavior     Full-text available via subscription   (Followers: 10)
Advances in Public Health     Open Access   (Followers: 19)
African Health Sciences     Open Access   (Followers: 2)
African Journal for Physical, Health Education, Recreation and Dance     Full-text available via subscription   (Followers: 6)
African Journal of Health Professions Education     Open Access   (Followers: 4)
Afrimedic Journal     Open Access   (Followers: 2)
Air Quality, Atmosphere & Health     Hybrid Journal   (Followers: 2)
AJOB Primary Research     Partially Free   (Followers: 2)
American Journal of Family Therapy     Hybrid Journal   (Followers: 10)
American Journal of Health Economics     Full-text available via subscription   (Followers: 13)
American Journal of Health Education     Hybrid Journal   (Followers: 25)
American Journal of Health Promotion     Hybrid Journal   (Followers: 24)
American Journal of Health Studies     Full-text available via subscription   (Followers: 8)
American Journal of Preventive Medicine     Hybrid Journal   (Followers: 21)
American Journal of Public Health     Full-text available via subscription   (Followers: 175)
American Journal of Public Health Research     Open Access   (Followers: 27)
American Medical Writers Association Journal     Full-text available via subscription   (Followers: 2)
Analytic Methods in Accident Research     Hybrid Journal   (Followers: 2)
Annali dell'Istituto Superiore di Sanità     Open Access  
Annals of Global Health     Open Access   (Followers: 8)
Annals of Health Law     Open Access   (Followers: 3)
Annals of Tropical Medicine and Public Health     Open Access   (Followers: 15)
Applied Biosafety     Hybrid Journal  
Applied Research In Health And Social Sciences : Interface And Interaction     Open Access  
Archives of Medicine and Health Sciences     Open Access   (Followers: 2)
Asia Pacific Journal of Counselling and Psychotherapy     Hybrid Journal   (Followers: 8)
Asia Pacific Journal of Health Management     Full-text available via subscription   (Followers: 1)
Asia-Pacific Journal of Public Health     Hybrid Journal   (Followers: 8)
Asian Journal of Gambling Issues and Public Health     Open Access   (Followers: 3)
Association of Schools of Allied Health Professions     Full-text available via subscription   (Followers: 5)
Atención Primaria     Open Access   (Followers: 1)
Australasian Journal of Paramedicine     Open Access   (Followers: 2)
Australian Advanced Aesthetics     Full-text available via subscription   (Followers: 4)
Australian Family Physician     Full-text available via subscription   (Followers: 1)
Australian Indigenous HealthBulletin     Free   (Followers: 6)
Autism & Developmental Language Impairments     Open Access   (Followers: 1)
Behavioral Healthcare     Full-text available via subscription   (Followers: 4)
Best Practices in Mental Health     Full-text available via subscription   (Followers: 6)
Bijzijn     Hybrid Journal   (Followers: 2)
Bijzijn XL     Hybrid Journal   (Followers: 1)
Biomedical Safety & Standards     Full-text available via subscription   (Followers: 9)
BLDE University Journal of Health Sciences     Open Access  
BMC Oral Health     Open Access   (Followers: 5)
BMC Pregnancy and Childbirth     Open Access   (Followers: 19)
BMJ Simulation & Technology Enhanced Learning     Full-text available via subscription   (Followers: 7)
Brazilian Journal of Medicine and Human Health     Open Access  
Buletin Penelitian Kesehatan     Open Access   (Followers: 2)
Buletin Penelitian Sistem Kesehatan     Open Access  
Bulletin of the World Health Organization     Open Access   (Followers: 15)
Cadernos de Educação, Saúde e Fisioterapia     Open Access   (Followers: 1)
Cadernos Saúde Coletiva     Open Access   (Followers: 1)
Canadian Family Physician     Partially Free   (Followers: 11)
Canadian Journal of Community Mental Health     Full-text available via subscription   (Followers: 10)
Canadian Journal of Human Sexuality     Hybrid Journal   (Followers: 1)
Canadian Journal of Public Health     Full-text available via subscription   (Followers: 18)
Case Reports in Women's Health     Open Access   (Followers: 2)
Case Studies in Fire Safety     Open Access   (Followers: 11)
Central Asian Journal of Global Health     Open Access   (Followers: 2)
Central European Journal of Public Health     Full-text available via subscription   (Followers: 4)
CES Medicina     Open Access  
Child Abuse Research in South Africa     Full-text available via subscription   (Followers: 1)
Child's Nervous System     Hybrid Journal  
Childhood Obesity and Nutrition     Open Access   (Followers: 10)
Children     Open Access   (Followers: 2)
CHRISMED Journal of Health and Research     Open Access  
Christian Journal for Global Health     Open Access  
Ciência & Saúde Coletiva     Open Access   (Followers: 2)
Ciencia y Cuidado     Open Access  
Ciencia, Tecnología y Salud     Open Access  
ClinicoEconomics and Outcomes Research     Open Access   (Followers: 1)
CME     Hybrid Journal   (Followers: 1)
CoDAS     Open Access  
Community Health     Open Access   (Followers: 1)
Conflict and Health     Open Access   (Followers: 8)
Curare     Open Access  
Current Opinion in Behavioral Sciences     Hybrid Journal   (Followers: 1)
Day Surgery Australia     Full-text available via subscription   (Followers: 2)
Digital Health     Open Access  
Dramatherapy     Hybrid Journal   (Followers: 2)
Drogues, santé et société     Full-text available via subscription  
Duazary     Open Access   (Followers: 1)
Early Childhood Research Quarterly     Hybrid Journal   (Followers: 13)
East African Journal of Public Health     Full-text available via subscription   (Followers: 2)
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity     Hybrid Journal   (Followers: 16)
EcoHealth     Hybrid Journal   (Followers: 3)
Education for Health     Open Access   (Followers: 4)
electronic Journal of Health Informatics     Open Access   (Followers: 4)
ElectronicHealthcare     Full-text available via subscription   (Followers: 3)
Elsevier Ergonomics Book Series     Full-text available via subscription   (Followers: 4)
Emergency Services SA     Full-text available via subscription   (Followers: 2)
Ensaios e Ciência: Ciências Biológicas, Agrárias e da Saúde     Open Access  
Environmental Disease     Open Access  
Environmental Sciences Europe     Open Access   (Followers: 2)
Epidemics     Open Access   (Followers: 3)
Epidemiology, Biostatistics and Public Health     Open Access   (Followers: 18)
Ethics, Medicine and Public Health     Full-text available via subscription  
Ethiopian Journal of Health Development     Open Access   (Followers: 8)
Ethiopian Journal of Health Sciences     Open Access   (Followers: 7)
Ethnicity & Health     Hybrid Journal   (Followers: 14)
European Journal of Investigation in Health, Psychology and Education     Open Access   (Followers: 1)
European Medical, Health and Pharmaceutical Journal     Open Access  
Evaluation & the Health Professions     Hybrid Journal   (Followers: 8)
Evidence-based Medicine & Public Health     Open Access   (Followers: 4)
Evidência - Ciência e Biotecnologia - Interdisciplinar     Open Access  
Face à face     Open Access   (Followers: 1)
Families, Systems, & Health     Full-text available via subscription   (Followers: 8)
Family & Community Health     Partially Free   (Followers: 12)
Family Medicine and Community Health     Open Access   (Followers: 3)
Family Relations     Partially Free   (Followers: 11)
Fatigue : Biomedicine, Health & Behavior     Hybrid Journal   (Followers: 1)
Food and Public Health     Open Access   (Followers: 10)
Frontiers in Public Health     Open Access   (Followers: 8)
Gaceta Sanitaria     Open Access   (Followers: 3)
Galen Medical Journal     Open Access  
Geospatial Health     Open Access  
Gesundheitsökonomie & Qualitätsmanagement     Hybrid Journal   (Followers: 11)
Giornale Italiano di Health Technology Assessment     Full-text available via subscription  
Global Health : Science and Practice     Open Access   (Followers: 4)
Global Health Promotion     Hybrid Journal   (Followers: 15)
Global Journal of Health Science     Open Access   (Followers: 5)
Global Journal of Public Health     Open Access   (Followers: 9)
Globalization and Health     Open Access   (Followers: 5)
Hacia la Promoción de la Salud     Open Access  
Hastings Center Report     Hybrid Journal   (Followers: 7)
HEADline     Hybrid Journal  
Health & Place     Hybrid Journal   (Followers: 14)
Health & Justice     Open Access   (Followers: 5)
Health : An Interdisciplinary Journal for the Social Study of Health, Illness and Medicine     Hybrid Journal   (Followers: 7)
Health and Human Rights     Free   (Followers: 8)
Health and Social Care Chaplaincy     Hybrid Journal   (Followers: 9)
Health and Social Work     Hybrid Journal   (Followers: 46)
Health Behavior and Policy Review     Full-text available via subscription   (Followers: 1)
Health Care Analysis     Hybrid Journal   (Followers: 11)
Health Inform     Full-text available via subscription  
Health Information Management Journal     Hybrid Journal   (Followers: 10)
Health Issues     Full-text available via subscription   (Followers: 1)
Health Policy     Hybrid Journal   (Followers: 32)
Health Policy and Technology     Hybrid Journal  
Health Professional Student Journal     Open Access   (Followers: 1)
Health Promotion International     Hybrid Journal   (Followers: 20)
Health Promotion Journal of Australia : Official Journal of Australian Association of Health Promotion Professionals     Full-text available via subscription   (Followers: 10)
Health Promotion Practice     Hybrid Journal   (Followers: 15)
Health Prospect     Open Access   (Followers: 1)
Health Psychology     Full-text available via subscription   (Followers: 47)
Health Psychology Research     Open Access   (Followers: 18)
Health Psychology Review     Hybrid Journal   (Followers: 39)
Health Renaissance     Open Access  
Health Research Policy and Systems     Open Access   (Followers: 9)
Health SA Gesondheid     Open Access   (Followers: 2)
Health Science Reports     Open Access  
Health Sciences and Disease     Open Access   (Followers: 1)
Health Services Insights     Open Access   (Followers: 1)
Health Systems     Hybrid Journal   (Followers: 2)
Health Voices     Full-text available via subscription  
Health, Culture and Society     Open Access   (Followers: 10)
Health, Risk & Society     Hybrid Journal   (Followers: 9)
Healthcare     Open Access   (Followers: 1)
Healthcare in Low-resource Settings     Open Access   (Followers: 1)
Healthcare Quarterly     Full-text available via subscription   (Followers: 8)
HERD : Health Environments Research & Design Journal     Full-text available via subscription  
Highland Medical Research Journal     Full-text available via subscription  
Hispanic Health Care International     Full-text available via subscription  
HIV & AIDS Review     Full-text available via subscription   (Followers: 9)
Home Health Care Services Quarterly     Hybrid Journal   (Followers: 5)
Hong Kong Journal of Social Work, The     Hybrid Journal   (Followers: 2)
Hospitals & Health Networks     Free   (Followers: 2)
IEEE Journal of Translational Engineering in Health and Medicine     Open Access   (Followers: 3)
IMTU Medical Journal     Full-text available via subscription  
Indian Journal of Health Sciences     Open Access   (Followers: 1)
Inmanencia. Revista del Hospital Interzonal General de Agudos (HIGA) Eva Perón     Open Access  
Innovative Journal of Medical and Health Sciences     Open Access  
Institute for Security Studies Papers     Full-text available via subscription   (Followers: 6)
interactive Journal of Medical Research     Open Access  
International Health     Hybrid Journal   (Followers: 4)
International Journal for Equity in Health     Open Access   (Followers: 7)
International Journal for Quality in Health Care     Hybrid Journal   (Followers: 31)
International Journal of Applied Behavioral Sciences     Open Access   (Followers: 2)
International Journal of Behavioural and Healthcare Research     Hybrid Journal   (Followers: 7)
International Journal of Circumpolar Health     Open Access   (Followers: 1)
International Journal of Community Medicine and Public Health     Open Access   (Followers: 5)
International Journal of E-Health and Medical Communications     Full-text available via subscription   (Followers: 2)
International Journal of Environmental Research and Public Health     Open Access   (Followers: 19)
International Journal of Evidence-Based Healthcare     Hybrid Journal   (Followers: 8)
International Journal of Food Safety, Nutrition and Public Health     Hybrid Journal   (Followers: 13)
International Journal of Health & Allied Sciences     Open Access   (Followers: 1)
International Journal of Health Care Quality Assurance     Hybrid Journal   (Followers: 7)
International Journal of Health Geographics     Open Access   (Followers: 6)
International Journal of Health Policy and Management     Open Access   (Followers: 2)
International Journal of Health Professions     Open Access   (Followers: 2)
International Journal of Health Promotion and Education     Hybrid Journal   (Followers: 12)
International Journal of Health Sciences Education     Open Access   (Followers: 2)
International Journal of Health Services     Full-text available via subscription   (Followers: 9)
International Journal of Health Studies     Open Access   (Followers: 3)
International Journal of Health System and Disaster Management     Open Access   (Followers: 2)
International Journal of Healthcare Delivery Reform Initiatives     Full-text available via subscription   (Followers: 1)

        1 2 3 | Last

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  [3031 journals]
  • 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
    • 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
    • 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
    • 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
    • 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)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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