International Journal of Transportation Science and Technology (Mar 2024)

Application of Conditional Deep Generative Networks (CGAN) in empirical bayes estimation of road crash risk and identifying crash hotspots

  • Mohammad Zarei,
  • Bruce Hellinga,
  • Pedram Izadpanah

Journal volume & issue
Vol. 13
pp. 258 – 269

Abstract

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The conditional generative adversarial network (CGAN) is used in this paper for empirical Bayes (EB) analysis of road crash hotspots. EB is a well-known method for estimating the expected crash frequency of sites (e.g. road segments, intersections) and then prioritising these sites to identify a subset of high priority sites (e.g. hotspots) for additional safety audits/improvements. In contrast to the conventional EB approach, which employs a statistical model such as the negative binomial model (NB-EB) to model crash frequency data, the recently developed CGAN-EB approach uses a conditional generative adversarial network, a form of deep neural network, that can model any form of distributions of the crash frequency data. Previous research has shown that the CGAN-EB performs as well as or better than NB-EB, however that work considered only a small range of crash data characteristics and did not examine the spatial and temporal transferability. In this paper a series of simulation experiments are devised and carried out to assess the CGAN-EB performance across a wide range of conditions and compares it to the NB-EB. The simulation results show that CGAN-EB performs as well as NB-EB when conditions favor the NB-EB model (i.e. data conform to the assumptions of the NB model) and outperforms NB-EB in experiments reflecting conditions frequently encountered in practice (i.e. low sample mean crash rates, and when crash frequency does not follow a log-linear relationship with covariates). Also, temporal and spatial transferability of both approaches were evaluated using field data and both CGAN-EB and NB-EB approaches were found to have similar performance.

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