Bayesian hierarchical modeling traffic conflict extremes for crash estimation: A non-stationary peak over threshold approach

This study presents a Bayesian hierarchical model to estimate crashes from traffic conflict extremes in a non-stationary context. The model combines a peak over threshold approach with non-stationary thresholds in terms of regression quantiles and covariate-dependent parameters of the generalized Pareto distribution. The developed model was applied to estimate rear-end crashes from traffic conflicts of the same type collected from four signalized intersections. The conflicts were measured by the modified time to collision (MTTC) and traffic volume, shock wave area, average shock wave speed, and platoon ratio of each signal cycle were employed as covariates. Thresholds corresponding to quantiles ranging from 80% to 95% were tested and the threshold stability plot indicated the 90% quantile was reasonable. Threshold excesses were then declustered at the signal cycle level, and the remained ones were used to develop the Bayesian hierarchical generalized Pareto distribution models (BHM_GPD). The model estimation results show that accounting for non-stationarity significantly improves the model fit. As well, the best fitted model generated accurate crash estimates with relatively narrow confidence intervals. The developed BHM_GPD model was also compared to the Bayesian hierarchical generalized extreme value model (BHM_GEV). The results show that the two models generate comparable crash estimates in terms of accuracy, but the crash estimates from the BHM_GPD model are generally more precise than those of BHM_GEV model. It is also found that although the peak over threshold approach combined with declustering reduces the number of extreme samples, it ensures the use of actual extremes. Moreover, the limited sample size issue is overcome by the proposed Bayesian hierarchical framework, which allows sharing information from different sites and accounting for unobserved heterogeneity. The findings also imply that the BHM_GEV model is preferred when traffic conflicts are relatively evenly distributed over blocks; otherwise the BHM_GPD model should be a better choice.

• Record URL:
  https://doi.org/10.1016/j.amar.2019.100106
• Record URL:
  http://www.sciencedirect.com/science/article/pii/S2213665719300624
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/22136657
• Supplemental Notes:
  • © 2019 Elsevier Ltd. All rights reserved. Abstract reprinted with permission of Elsevier.
• Authors:
  • Zheng, Lai
  • Sayed, Tarek
• Publication Date: 2019-12

Language

• English

Media Info

• Media Type: Web
• Features: Appendices; Figures; References; Tables;
• Pagination: 100106
• Serial:
  • Analytic Methods in Accident Research
  • Volume: 24
  • Issue Number: 0
  • Publisher: Elsevier
  • ISSN: 2213-6657
  • Serial URL: http://www.sciencedirect.com/science/journal/22136657

Subject/Index Terms

• TRT Terms: Bayes' theorem; Estimation theory; Rear end crashes; Regression analysis; Signalized intersections; Traffic conflicts; Traffic crashes; Traffic models
• Subject Areas: Highways; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01722144
• Record Type: Publication
• Files: TRIS
• Created Date: Nov 14 2019 9:30AM