CGAN-EB: A Non-parametric Empirical Bayes Method for Crash Hotspot Identification Using Conditional Generative Adversarial Networks: A Real-world Crash Data Study
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 57 references that are in the map. First posted on arXiv 16 Dec 2021.
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5 of 57 references.
References · 5
- 2014Conditional Generative Adversarial Netscited by 2,162
- 2017Generative Adversarial Networks: An Overviewcited by 590
- 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 285
- 2019Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Surveycited by 93
- 2018Comparison of non-linear activation functions for deep neural networks on MNIST classification taskcited by 19
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Citing papers per year: 2021: 1.
1 paper.