Exploring Risk Factors With Crashes by Collision Type at Freeway Diverge Areas: Accounting for Unobserved Heterogeneity

The objective of this paper is to evaluate the impact of various risk factors on traffic crashes presenting different collision types at freeway diverge areas. Three-year period crash data from 367 freeway diverge areas were obtained. Three types of collisions, including rear-end, sideswipe, and angle collisions, were considered. A random parameters multivariate Poisson-lognormal (RP-MVPLN) model was developed to accommodate both correlations between crashes across collision type and the unobserved heterogeneity across observations. For the performance comparison, an MVPLN was developed and compared with the RP-MVPLN model under the Bayesian framework. The result showed that the RP-MVPLN model outperformed the MVPLN model, which highlighted that accounting for the unobserved heterogeneous effects of risk factors could improve the model fit. The model estimation result showed that the risk factors, as well as their impacts on different collision types, were different. The mainline annual average daily traffic (AADT), the lane-balanced design, and the number of lanes on the mainline were found to be significantly associated with all types of collisions, whereas the deceleration lane length and road surface type only affected rear-end crashes. The exit ramp length and ramp AADT had significant impact on rear-end crashes and sideswipe crashes, but they did not affect angle crashes. The speed limit was negatively related with rear-end crashes and angle crashes, while it had no impact on sideswipe crashes. Three risk factors, which are the mainline AADT, ramp AADT, and speed limit, were found to have significant heterogeneous effects on crashes across observations.

[1]  Zhetao Li,et al.  Reliability Enhancement Toward Functional Safety Goal Assurance in Energy-Aware Automotive Cyber-Physical Systems , 2018, IEEE Transactions on Industrial Informatics.

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

[3]  Shaw-Pin Miaou,et al.  Modeling Traffic Crash-Flow Relationships for Intersections: Dispersion Parameter, Functional Form, and Bayes Versus Empirical Bayes Methods , 2003 .

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

[5]  Tarek Sayed,et al.  Accounting for heterogeneity among treatment sites and time trends in developing crash modification functions. , 2014, Accident; analysis and prevention.

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

[7]  Mario Romero,et al.  Experimental Observation of Vehicle Evolution on Deceleration Lanes with Different Lengths , 2006 .

[8]  Paul P Jovanis,et al.  Bayesian Multivariate Poisson Lognormal Models for Crash Severity Modeling and Site Ranking , 2009 .

[9]  Tarek Sayed,et al.  A cross-comparison of different techniques for modeling macro-level cyclist crashes. , 2018, Accident; analysis and prevention.

[10]  Zhibin Li,et al.  Analysis of Crash Risks by Collision Type at Freeway Diverge Area Using Multivariate Modeling Technique , 2015 .

[11]  Jiguang Zhao,et al.  Safety performance evaluation of left-side off-ramps at freeway diverge areas. , 2011, Accident; analysis and prevention.

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

[13]  Keqin Li,et al.  Minimizing Development Cost With Reliability Goal for Automotive Functional Safety During Design Phase , 2018, IEEE Transactions on Reliability.

[14]  J J Lu,et al.  Impacts of freeway exit ramp configurations on traffic operations and safety , 2010 .

[15]  Yanyong Guo,et al.  Exploring unobserved heterogeneity in bicyclists' red-light running behaviors at different crossing facilities. , 2018, Accident; analysis and prevention.

[16]  Xiaoxiang Ma,et al.  Multivariate space-time modeling of crash frequencies by injury severity levels , 2017 .

[17]  Thomas F. Golob,et al.  Safety Aspects of Freeway Weaving Sections , 2003 .

[18]  Andrew Thomas,et al.  WinBUGS - A Bayesian modelling framework: Concepts, structure, and extensibility , 2000, Stat. Comput..

[19]  Pan Liu,et al.  How Lane Arrangements on Freeway Mainlines and Ramps Affect Safety of Freeways with Closely Spaced Entrance and Exit Ramps , 2010 .

[20]  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 .

[21]  Joe G Bared,et al.  SAFETY EVALUATION OF ACCELERATION AND DECELERATION LANE LENGTHS , 1999 .

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

[23]  Tarek Sayed,et al.  A full Bayes multivariate intervention model with random parameters among matched pairs for before-after safety evaluation. , 2011, Accident; analysis and prevention.

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

[25]  Jian John Lu,et al.  Evaluating the safety impacts of the number and arrangement of lanes on freeway exit ramps. , 2009, Accident; analysis and prevention.

[26]  Anne T McCartt,et al.  Types and characteristics of ramp-related motor vehicle crashes on urban interstate roadways in Northern Virginia. , 2004, Journal of safety research.

[27]  Paul Damien,et al.  A multivariate Poisson-lognormal regression model for prediction of crash counts by severity, using Bayesian methods. , 2008, Accident; analysis and prevention.

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

[29]  Joseph E. Hummer,et al.  Development of Safety Prediction Models for Influence Areas of Ramps in Freeways , 2009 .

[30]  Eun Sug Park,et al.  Multivariate Poisson-Lognormal Models for Jointly Modeling Crash Frequency by Severity , 2007 .

[31]  Jian John Lu,et al.  Safety Evaluation of Truck-Related Crashes at Freeway Diverge Areas , 2011 .

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

[33]  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 .

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

[35]  Keqin Li,et al.  Fast Functional Safety Verification for Distributed Automotive Applications During Early Design Phase , 2018, IEEE Transactions on Industrial Electronics.

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

[37]  Karin M Bauer,et al.  STATISTICAL MODELS OF ACCIDENTS ON INTERCHANGE RAMPS AND SPEED-CHANGE LANES , 1998 .

[38]  Yanyong Guo,et al.  Evaluating factors affecting electric bike users’ registration of license plate in China using Bayesian approach , 2018, Transportation Research Part F: Traffic Psychology and Behaviour.

[39]  Athanasios Theofilatos,et al.  Meta-Analysis of Crash-Risk Factors in Freeway Entrance and Exit Areas , 2017 .

[40]  James A Bonneson,et al.  Calibration of Predictive Models for Estimating Safety of Ramp Design Configurations , 2005 .

[41]  Mohamed Abdel-Aty,et al.  Multivariate crash modeling for motor vehicle and non-motorized modes at the macroscopic level. , 2015, Accident; analysis and prevention.

[42]  Tarek Sayed,et al.  A method to account for outliers in the development of safety performance functions. , 2010, Accident; analysis and prevention.

[43]  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.

[44]  Baoshan Huang,et al.  Multivariate random-parameters zero-inflated negative binomial regression model: an application to estimate crash frequencies at intersections. , 2014, Accident; analysis and prevention.

[45]  K. El-Basyouny,et al.  Collision prediction models using multivariate Poisson-lognormal regression. , 2009, Accident; analysis and prevention.