Please use this identifier to cite or link to this item:
http://hdl.handle.net/1942/42428
Title: | Comparative Evaluation of Crash Hotspot Identification Methods: Empirical Bayes vs. Potential for Safety Improvement Using Variants of Negative Binomial Models | Authors: | KHATTAK, Wisal De Backer, Hans De Winne, Pieter BRIJS, Tom PIRDAVANI, Ali |
Issue Date: | 2024 | Publisher: | MDPI | Source: | Sustainability, 16 (4) (Art N° 1537) | Series/Report: | Emerging Technologies and Sustainable Road Safety | 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. | Notes: | De Backer, H (corresponding author), UGent, Dept Civil Engn, Technol Pk 60, B-9052 Zwijnaarde, Belgium. muhammadwisal.khattak@uhasselt.be; hans.debacker@ugent.be; p.dewinne@ugent.be; tom.brijs@uhasselt.be; ali.pirdavani@uhasselt.be |
Keywords: | hotspot identification;empirical Bayes;potential for safety improvement;random parameter negative binomial model;varying dispersion parameter negative binomial model | Document URI: | http://hdl.handle.net/1942/42428 | e-ISSN: | 2071-1050 | DOI: | 10.3390/su16041537 | ISI #: | 001168421300001 | Rights: | 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). | Category: | A1 | Type: | Journal Contribution |
Appears in Collections: | Research publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
sustainability-16-01537-v2.pdf | Published version | 823.47 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.