Application of the Empirical Bayes Method with the Finite Mixture Model for Identifying Accident-Prone Spots

Yajie Zou,Yinhai Wang,Lingtao Wu,Kristian Henrickson,Zhaoru Zhang

doi:10.1155/2015/958206

Abstract

Hotspot identification (HSID) is an important component of the highway safety management process. A number of methods have been proposed to identify hotspots. Among these methods, previous studies have indicated that the empirical Bayes (EB) method can outperform other methods for identifying hotspots, since the EB method combines the historical crash records of the site and expected number of crashes obtained from a safety performance function (SPF) for similar sites. However, the SPFs are usually developed based on a large number of sites, which may contain heterogeneity in traffic characteristic. As a result, the hotspot identification accuracy of EB methods can possibly be affected by SPFs, when heterogeneity is present in crash data. Thus, it is necessary to consider the heterogeneity and homogeneity of roadway segments when using EB methods. To address this problem, this paper proposed three different classification-based EB methods to identify hotspots. Rural highway crash data collected in Texas were analyzed and classified into different groups using the proposed methods. Based on the modeling results for Texas crash dataset, it is found that one proposed classification-based EB method performs better than the standard EB method as well as other HSID methods.

Highlights

For the purpose of prioritizing safety improvements on roadway network, identifying sites with consistently elevated accident risk, often referred to as hotspots or black spots, is of critical importance
empirical Bayes (EB) methods (i.e., EB, mean-based EB, FMNB-based EB, and GFMNB-based EB methods) perform better than other methods (i.e., accident frequency (AF), accident rate (AR), and accident reduction potential (ARP)), which is consistent with previous studies [9, 23,24,25]
The possible explanation for this is that the EB method is based on two clues, the historical crash record of the entity and the expected number of crashes obtained from a safety performance function for similar entities

Summary

Introduction

For the purpose of prioritizing safety improvements on roadway network, identifying sites with consistently elevated accident risk, often referred to as hotspots or black spots, is of critical importance To address this need, a number of analytical methods for hotspot identification (HSID) have been developed over the last several decades, with the overarching objective of optimizing the allocation of limited funding. The empirical Bayes (EB) HSID method addresses these issues by combining two clues, the historical crash record of the entity and the expected number of crashes obtained from a safety performance function (SPF) for similar entities This approach is less sensitive to random fluctuations in Mathematical Problems in Engineering accident frequency and in theory can identify truly high risk locations with greater accuracy. One criticism often raised with regard to the AF method is that this approach lacks the ability to differentiate between actual hotspots and locations with increased accident frequency attributable to the randomness of traffic accidents [9, 10]

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