Investigating varying effect of road-level factors on crash frequency across regions: A Bayesian hierarchical random parameter modeling approach

Abstract This study aims to quantitatively examine the variations in effect of road-level factors on crash frequency across different regions. Treating the hierarchical structure existing in the crash data that road entity nested within the geographic region, a hierarchical random parameter model, which allows the coefficients of road-level variables to vary with regions, is proposed. A Poisson lognormal model and a hierarchical random intercept model are also built for the purpose of comparison. A specific roadway facility type, urban two-lane two-way roadway segments in Florida, with crash and road level data including traffic volume, road length, surface condition, and access density for three-year period are used for a case study. The result shows that, in the hierarchical-random parameter model, the local regression coefficients and marginal effects of the road level factors vary over a wide range in the selected counties, which clearly illustrates the non-stationary in the relationships between road level factors and crash frequency across the counties. In regard to the model comparison, the hierarchical random parameter model outperforms the Poisson lognormal model and the hierarchical random intercept model in term of deviance information criterion (DIC). This further confirms the necessity of the use of hierarchical random parameter model in analyzing the crash frequency for road entities in different regions. This study provides a potential in guidance of model construction that considers regional variations (heterogeneities) in safety effects of road-level factors.

[1]  Srinivas R. Geedipally,et al.  Developing a Random Parameters Negative Binomial-Lindley Model to analyze highly over-dispersed crash count data , 2018 .

[2]  P. Dischinger,et al.  Social, behavioral and driving characteristics of injured pedestrians: a comparison with other unintentional trauma patients. , 2007, Accident; analysis and prevention.

[3]  Susan Scavo Gallagher,et al.  Injury Prevention and Public Health: Practical Knowledge, Skills, and Strategies , 1999 .

[4]  Panagiotis Ch. Anastasopoulos,et al.  A random thresholds random parameters hierarchical ordered probit analysis of highway accident injury-severities , 2017 .

[5]  Dominique Lord,et al.  The statistical analysis of highway crash-injury severities: a review and assessment of methodological alternatives. , 2011, Accident; analysis and prevention.

[6]  Chandra R. Bhat,et al.  A new spatial and flexible multivariate random-coefficients model for the analysis of pedestrian injury counts by severity level , 2017 .

[7]  Andrew Gelman,et al.  Data Analysis Using Regression and Multilevel/Hierarchical Models , 2006 .

[8]  Pengpeng Xu,et al.  Revisiting crash spatial heterogeneity: A Bayesian spatially varying coefficients approach. , 2017, Accident; analysis and prevention.

[9]  Chuan Ding,et al.  Spatial heterogeneous impact of built environment on household auto ownership levels: evidence from analysis at traffic analysis zone scales , 2016 .

[10]  Ming Ma,et al.  A multivariate spatial model of crash frequency by transportation modes for urban intersections , 2017 .

[11]  Jonathan Aguero-Valverde,et al.  Multivariate spatial models of excess crash frequency at area level: case of Costa Rica. , 2013, Accident; analysis and prevention.

[12]  Andrew P Jones,et al.  The use of multilevel models for the prediction of road accident outcomes. , 2003, Accident; analysis and prevention.

[13]  Lalita Thakali,et al.  Benchmarking regions using a heteroskedastic grouped random parameters model with heterogeneity in mean and variance: Applications to grade crossing safety analysis , 2018, Analytic Methods in Accident Research.

[14]  Juneyoung Park,et al.  Crash modeling for intersections and segments along corridors: a Bayesian multilevel joint model with random parameters , 2017 .

[15]  Jie Wang,et al.  The effect of zonal factors in estimating crash risks by transportation modes: Motor vehicle, bicycle and pedestrian. , 2017, Accident; analysis and prevention.

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

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

[18]  Xuesong Wang,et al.  Safety modeling of urban arterials in Shanghai, China. , 2014, Accident; analysis and prevention.

[19]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[20]  Mohamed Abdel-Aty,et al.  Crash Estimation at Signalized Intersections Along Corridors: Analyzing Spatial Effect and Identifying Significant Factors , 2006 .

[21]  Helai Huang,et al.  Road network safety evaluation using Bayesian hierarchical joint model. , 2016, Accident; analysis and prevention.

[22]  Mohamed Abdel-Aty,et al.  Transferring and calibrating safety performance functions among multiple States. , 2018, Accident; analysis and prevention.

[23]  Anthony S. Bryk,et al.  Hierarchical Linear Models: Applications and Data Analysis Methods , 1992 .

[24]  Brendan J. Russo,et al.  Comparison of factors affecting injury severity in angle collisions by fault status using a random parameters bivariate ordered probit model , 2014 .

[25]  S. Erdogan.  Explorative spatial analysis of traffic accident statistics and road mortality among the provinces of Turkey. , 2009, Journal of safety research.

[26]  Rune Elvik,et al.  International transferability of accident modification functions for horizontal curves. , 2013, Accident; analysis and prevention.

[27]  M. Quddus.  Modelling area-wide count outcomes with spatial correlation and heterogeneity: an analysis of London crash data. , 2008, Accident; analysis and prevention.

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

[29]  Sudip Barua,et al.  Multivariate random parameters collision count data models with spatial heterogeneity , 2016 .

[30]  Samuel Labi,et al.  Impact of road-surface condition on rural highway safety: A multivariate random parameters negative binomial approach , 2017 .

[31]  Mohamed Abdel-Aty,et al.  Effects of Pavement Surface Conditions on Traffic Crash Severity , 2015 .

[32]  Behram Wali,et al.  Do safety performance functions used for predicting crash frequency vary across space? Applying geographically weighted regressions to account for spatial heterogeneity. , 2017, Accident; analysis and prevention.

[33]  George Yannis,et al.  Multilevel analysis in road safety research. , 2013, Accident; analysis and prevention.

[34]  Satish V. Ukkusuri,et al.  The role of built environment on pedestrian crash frequency , 2012 .

[35]  F. Mannering,et al.  The effect of passengers on driver-injury severities in single-vehicle crashes: A random parameters heterogeneity-in-means approach , 2017 .

[36]  P. Jovanis,et al.  Spatial analysis of fatal and injury crashes in Pennsylvania. , 2006, Accident; analysis and prevention.

[37]  Yu Zhang,et al.  A new access density definition and its correlation with crash rates by microscopic traffic simulation method. , 2014, Accident; analysis and prevention.

[38]  Mohamed Abdel-Aty,et al.  Multi-level hot zone identification for pedestrian safety. , 2015, Accident; analysis and prevention.

[39]  R. B. Albin,et al.  Evaluating Median Crossover Likelihoods with Clustered Accident Counts: An Empirical Inquiry Using the Random Effects Negative Binomial Model , 1998 .

[40]  Le Minh Kieu,et al.  Spatiotemporal and random parameter panel data models of traffic crash fatalities in Vietnam. , 2016, Accident; analysis and prevention.

[41]  Simon Washington,et al.  On the significance of omitted variables in intersection crash modeling. , 2012, Accident; analysis and prevention.

[42]  Mohamed Abdel-Aty,et al.  Multilevel data and bayesian analysis in traffic safety. , 2010, Accident; analysis and prevention.

[43]  Gudmundur F. Ulfarsson,et al.  Random parameter models of interstate crash frequencies by severity, number of vehicles involved, collision and location type. , 2013, Accident; analysis and prevention.

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

[45]  Weixu Wang,et al.  Using Geographically Weighted Poisson Regression for county-level crash modeling in California , 2013 .

[46]  Panagiotis Ch. Anastasopoulos,et al.  Analysis of accident injury-severities using a correlated random parameters ordered probit approach with time variant covariates , 2018, Analytic Methods in Accident Research.

[47]  Qiang Zeng,et al.  Bayesian spatial joint modeling of traffic crashes on an urban road network. , 2014, Accident; analysis and prevention.

[48]  Chuan Ding,et al.  Influences of built environment characteristics and individual factors on commuting distance: A multilevel mixture hazard modeling approach , 2017 .

[49]  B. Mallick,et al.  Bayesian multivariate spatial models for roadway traffic crash mapping , 2006 .

[50]  Anuj Sharma,et al.  Using the multivariate spatio-temporal Bayesian model to analyze traffic crashes by severity , 2018 .

[51]  Eric T. Donnell,et al.  Quantifying the safety effects of horizontal curves on two-way, two-lane rural roads. , 2016, Accident; analysis and prevention.

[52]  Helai Huang,et al.  County-Level Crash Risk Analysis in Florida: Bayesian Spatial Modeling , 2010 .

[53]  Gudmundur F. Ulfarsson,et al.  A heterogeneity-in-means count model for evaluating the effects of interchange type on heterogeneous influences of interstate geometrics on crash frequencies , 2014 .

[54]  Fred L Mannering,et al.  A note on modeling vehicle accident frequencies with random-parameters count models. , 2009, Accident; analysis and prevention.

[55]  Fred L. Mannering,et al.  The effect of speed limits on drivers' choice of speed: A random parameters seemingly unrelated equations approach , 2016 .

[56]  C. Robert,et al.  Bayesian Modeling Using WinBUGS , 2009 .

[57]  Pengpeng Xu,et al.  Modeling crash spatial heterogeneity: random parameter versus geographically weighting. , 2015, Accident; analysis and prevention.

[58]  Mohamed Abdel-Aty,et al.  Intersection crash prediction modeling with macro-level data from various geographic units. , 2017, Accident; analysis and prevention.

[59]  Anuj Sharma,et al.  Exploring spatio-temporal effects in traffic crash trend analysis , 2017 .

[60]  Patrick Morency,et al.  The link between built environment, pedestrian activity and pedestrian-vehicle collision occurrence at signalized intersections. , 2011, Accident; analysis and prevention.

[61]  Mohamed Abdel-Aty,et al.  Developing a grouped random parameters multivariate spatial model to explore zonal effects for segment and intersection crash modeling , 2018, Analytic Methods in Accident Research.

[62]  Steven Jones,et al.  Multilevel analysis of the role of human factors in regional disparities in crash outcomes. , 2017, Accident; analysis and prevention.

[63]  Gudmundur F. Ulfarsson,et al.  A note on modeling pedestrian-injury severity in motor-vehicle crashes with the mixed logit model. , 2010, Accident; analysis and prevention.

[64]  Nima Golshani,et al.  Grouped random parameters bivariate probit analysis of perceived and observed aggressive driving behavior: A driving simulation study , 2017 .

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

[66]  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 .