CGAN-EB: A Non-parametric Empirical Bayes Method for Crash Hotspot Identification Using Conditional Generative Adversarial Networks: A Real-world Crash Data Study

TL;DR

This paper introduces CGAN-EB, a non-parametric empirical Bayes method using conditional generative adversarial networks for crash hotspot identification, demonstrating superior performance over traditional parametric models on real-world data.

Contribution

The paper presents a novel non-parametric EB approach based on CGANs for crash data modeling, eliminating the need for pre-specified relationships and improving hotspot detection accuracy.

Findings

CGAN-EB outperforms NB-EB in prediction accuracy

CGAN-EB provides better hotspot identification results

Method successfully applied to real-world crash data

Abstract

The empirical Bayes (EB) method based on parametric statistical models such as the negative binomial (NB) has been widely used for ranking sites in road network safety screening process. This paper is the continuation of the authors previous research, where a novel non-parametric EB method for modelling crash frequency data data based on Conditional Generative Adversarial Networks (CGAN) was proposed and evaluated over several simulated crash data sets. Unlike parametric approaches, there is no need for a pre-specified underlying relationship between dependent and independent variables in the proposed CGAN-EB and they are able to model any types of distributions. The proposed methodology is now applied to a real-world data set collected for road segments from 2012 to 2017 in Washington State. The performance of CGAN-EB in terms of model fit, predictive performance and network screening outcomes is compared with the conventional approach (NB-EB) as a benchmark. The results indicate that the proposed CGAN-EB approach outperforms NB-EB in terms of prediction power and hotspot identification tests.