Multivariate random parameter Tobit modeling of crashes involving aging drivers, passengers, bicyclists, and pedestrians: Spatiotemporal variations.

The increase in 65 years and older population in the United States compels the investigation of the crashes involving all aging (65+) roadway users (drivers, passengers, bicyclists, and pedestrians) in order to ensure their safety. As such, the objective of this research is to provide a spatiotemporal comparative investigation of the crashes involving these aging roadway users in Florida via concurrently using the same set of predictors in order to obtain comparable findings among them. First, a new metric, namely Crash Rate Difference (CRD) approach is developed, which enables one to capture potential spatial and temporal (e.g., weekend and weekday) variations in crash rates of aging user-involved crashes. Second, a multivariate random parameter Tobit model is utilized to determine the factors that drive both the crash occurrence probability and the crash rate of 65+ roadway users, accounting for the unobserved heterogeneity. Findings show that there are statistically significant heterogeneous effects of predictors on the crash rates of different roadway users, which evidences the unobserved heterogeneity across observations. Results also indicate that the presence of facilities such as hospitals, religious facilities, or supermarkets is very influential on crash rates of 65+ roadway users, advocating that roadways around these facilities should be particularly scrutinized by road safety stakeholders. Interestingly, the effect of these facilities on crashes also differs significantly between weekdays and weekends. Moreover, the roadway segments with high crash rates vary temporally depending on whether it is a weekday or a weekend. These findings regarding the spatiotemporal variations clearly indicate the need to develop and design better traffic safety measures and plans addressing these specific roadway segments, which can be tailored to alleviate traffic safety problems for 65+ roadway users.

[1]  Chandra R. Bhat,et al.  Unobserved heterogeneity and the statistical analysis of highway accident data , 2016 .

[2]  J. Geweke,et al.  Bayesian Treatment of the Independent Student- t Linear Model , 1993 .

[3]  David B. Dunson,et al.  Bayesian Data Analysis , 2010 .

[4]  Thomas J Triggs,et al.  Driver distraction: the effects of concurrent in-vehicle tasks, road environment complexity and age on driving performance. , 2006, Accident; analysis and prevention.

[5]  Chris Brunsdon,et al.  Estimating probability surfaces for geographical point data: an adaptive kernel algorithm , 1995 .

[6]  S. Figen Kalyoncuoglu,et al.  An alternative approach for modelling and simulation of traffic data: artificial neural networks , 2004, Simul. Model. Pract. Theory.

[7]  Salvador Hernandez,et al.  Fatality rates for crashes involving heavy vehicles on highways: A random parameter tobit regression approach , 2016 .

[8]  Eren Erman Ozguven,et al.  Spatial investigation of aging-involved crashes: A GIS-based case study in Northwest Florida , 2017 .

[9]  Martyn Plummer,et al.  JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling , 2003 .

[10]  Marlos Viana,et al.  SHORT NOTE Relative Effects of Age and Compromised Vision on Driving Performance , 1995, Hum. Factors.

[11]  M. Legge,et al.  Age related changes in drivers' crash risk and crash type. , 1998, Accident; analysis and prevention.

[12]  Mohamed Abdel-Aty,et al.  Macroscopic spatial analysis of pedestrian and bicycle crashes. , 2012, Accident; analysis and prevention.

[13]  S. Chib.  Bayes inference in the Tobit censored regression model , 1992 .

[14]  Chandra R. Bhat,et al.  Analytic methods in accident research: Methodological frontier and future directions , 2014 .

[15]  Hedley Rees,et al.  Limited-Dependent and Qualitative Variables in Econometrics. , 1985 .

[16]  Panagiotis Ch. Anastasopoulos.  Random parameters multivariate tobit and zero-inflated count data models: addressing unobserved and zero-state heterogeneity in accident injury-severity rate and frequency analysis , 2016 .

[17]  Hakjin Jang,et al.  Bayesian analysis for zero-inflated regression models with the power prior: applications to road safety countermeasures. , 2010, Accident; analysis and prevention.

[18]  Rebecca Steinbach,et al.  The road most travelled: the geographic distribution of road traffic injuries in England , 2013, International Journal of Health Geographics.

[19]  Lisa K. Spainhour,et al.  Age-Based Stratification of Drivers to Evaluate the Effects of Age on Crash Involvement , 2017 .

[20]  William H. Greene,et al.  Marginal Effects in the Censored Regression Model , 1999 .

[21]  Mohamed Abdel-Aty,et al.  Presence of passengers: does it increase or reduce driver's crash potential? , 2008, Accident; analysis and prevention.

[22]  A. Raftery,et al.  How Many Iterations in the Gibbs Sampler , 1991 .

[23]  Helai Huang,et al.  A multivariate random-parameters Tobit model for analyzing highway crash rates by injury severity. , 2017, Accident; analysis and prevention.

[24]  Andrew P Tarko,et al.  Tobit analysis of vehicle accident rates on interstate highways. , 2008, Accident; analysis and prevention.

[25]  Gudmundur F. Ulfarsson,et al.  Age and pedestrian injury severity in motor-vehicle crashes: a heteroskedastic logit analysis. , 2008, Accident; analysis and prevention.

[26]  Jean-Claude Thill,et al.  Geospatial and machine learning techniques for wicked social science problems: analysis of crash severity on a regional highway corridor , 2015, Journal of Geographical Systems.

[27]  D. Hensher,et al.  A mixed generalized ordered response model for examining pedestrian and bicyclist injury severity level in traffic crashes. , 2008, Accident; analysis and prevention.

[28]  F. Mannering.  Temporal instability and the analysis of highway accident data , 2018 .

[29]  Fred L. Mannering,et al.  The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives , 2010 .

[30]  Thomas L. Traynor.  The impact of driver alcohol use on crash severity: A crash specific analysis , 2005 .

[31]  Mohamed Abdel-Aty,et al.  Using conditional probability to find driver age effect in crashes , 1999 .

[32]  Ashim Kumar Debnath,et al.  A Tobit model for analyzing speed limit compliance in work zones , 2014 .

[33]  Mohamed Abdel-Aty,et al.  Investigating the different characteristics of weekday and weekend crashes. , 2013, Journal of safety research.

[34]  Suren Chen,et al.  Modeling Crash Rates for a Mountainous Highway by Using Refined-Scale Panel Data , 2015 .

[35]  G. Guyatt,et al.  The independent contribution of driver, crash, and vehicle characteristics to driver fatalities. , 2002, Accident; analysis and prevention.

[36]  Fred L Mannering,et al.  A multivariate tobit analysis of highway accident-injury-severity rates. , 2012, Accident; analysis and prevention.

[37]  M A Abdel-Aty,et al.  An assessment of the effect of driver age on traffic accident involvement using log-linear models. , 1998, Accident; analysis and prevention.

[38]  D. Collia,et al.  The 2001 National Household Travel Survey: a look into the travel patterns of older Americans. , 2003, Journal of safety research.

[39]  Susan A Ferguson,et al.  Collisions Involving Senior Drivers: High-Risk Conditions and Locations , 2006, Traffic injury prevention.

[40]  Bruce Hellinga,et al.  TOWARDS MODELING THE IMPACT OF AN AGEING DRIVER POPULATION ON INTERSECTION DESIGN AND TRAFFIC MANAGEMENT , 1999 .

[41]  Thobias Sando,et al.  GIS-based Spatial and Temporal Analysis of Aging-Involved Accidents: a Case Study of Three Counties in Florida , 2017 .

[42]  Suren Chen,et al.  Refined-scale panel data crash rate analysis using random-effects tobit model. , 2014, Accident; analysis and prevention.

[43]  Mohamed Abdel-Aty,et al.  A Bayesian spatial random parameters Tobit model for analyzing crash rates on roadway segments. , 2017, Accident; analysis and prevention.

[44]  G McGwin,et al.  Characteristics of traffic crashes among young, middle-aged, and older drivers. , 1999, Accident; analysis and prevention.

[45]  Nikiforos Stamatiadis,et al.  Older Drivers and Intersection Traffic Control Devices , 1991 .

[46]  Kirolos Haleem,et al.  Effect of driver's age and side of impact on crash severity along urban freeways: a mixed logit approach. , 2013, Journal of safety research.

[47]  Keechoo Choi,et al.  A two-stage bivariate logistic-Tobit model for the safety analysis of signalized intersections , 2014 .

[48]  Jay Liebowitz,et al.  Older drivers and accidents: A meta analysis and data mining application on traffic accident data , 2005, Expert Syst. Appl..

[49]  A. Smiley,et al.  The role of exposure in comparisons of crash risk among different drivers and driving environments. , 1993, Accident; analysis and prevention.

[50]  Fred L Mannering,et al.  A study of factors affecting highway accident rates using the random-parameters tobit model. , 2012, Accident; analysis and prevention.

[51]  Lisa J. Molnar,et al.  Older Adult Safety and Mobility , 2009 .

[52]  Barbara Allan,et al.  Health Care Providers and Older Adult Service Organizations to Assist in the Prevention and Early Recognition of Florida’s At-Risk Drivers , 2015 .

[53]  Selina Mårdh,et al.  Identifying factors for traffic safety support in older drivers , 2016 .