Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models

Muhammad Wisal Khattak, Hans De Backer (), Pieter De Winne, Tom Brijs and Ali Pirdavani
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Muhammad Wisal Khattak: UGent, Department of Civil Engineering, Technologiepark 60, 9052 Zwijnaarde, Belgium
Hans De Backer: UGent, Department of Civil Engineering, Technologiepark 60, 9052 Zwijnaarde, Belgium
Pieter De Winne: UGent, Department of Civil Engineering, Technologiepark 60, 9052 Zwijnaarde, Belgium
Tom Brijs: UHasselt, Transportation Research Institute (IMOB), Martelarenlaan 42, 3500 Hasselt, Belgium
Ali Pirdavani: UHasselt, Transportation Research Institute (IMOB), Martelarenlaan 42, 3500 Hasselt, Belgium

Sustainability, 2024, vol. 16, issue 4, 1-22

Abstract: The empirical Bayes (EB) method is widely acclaimed for crash hotspot identification (HSID), which integrates crash prediction model estimates and observed crash frequency to compute the expected crash frequency of a site. The traditional negative binomial (NB) models, often used to estimate crash predictive models, typically struggle with accounting for the unobserved heterogeneity in crash data. Complex extensions of the NB models are applied to overcome these shortcomings. These techniques also present new challenges, for instance, applying the EB procedures, especially for out-of-sample data. This study applies a random parameter negative binomial (RPNB) model within the EB framework for HSID using out-of-sample data, comparing its performance with a varying dispersion parameter NB model (VDPNB). The research also evaluates the potential for safety improvement (PSI) scores for both models and compares them with EB estimates using three generalised criteria: high crashes consistency test (HCCT), common sites consistency test (CSCT), and absolute rank differences test (ARDT). The results yield dual insights. Firstly, the study highlights associations between crash covariates and frequency, emphasising the significance of roadway geometric design characteristics (e.g., lane width, number of lanes, and parking type) and traffic volume. Some variables also influenced overdispersion parameters in the VDPNB model. In the RPNB model, annual average daily traffic (AADT) and lane width emerged as random parameters. Secondly, the HSID performance assessment revealed the superiority of the EB method over PSI. Notably, the RPNB model, compared to the VDPNB, demonstrates superior performance in EB estimates for HSID with out-of-sample data. This research recommends adopting the EB method with RPNB models for robust HSID.

Keywords: hotspot identification; empirical Bayes; potential for safety improvement; random parameter negative binomial model; varying dispersion parameter negative binomial model (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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