Crash Data Augmentation Using Conditional Generative Adversarial Networks (CGAN) for Improving Safety Performance Functions
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Abstract (arXiv)
In this paper, we present a crash frequency data augmentation method based on Conditional Generative Adversarial Networks to improve crash frequency models. The proposed method is evaluated by comparing the performance of Base SPFs (developed using original data) and Augmented SPFs (developed using original data plus synthesised data) in terms of hotspot identification performance, model prediction accuracy, and dispersion parameter estimation accuracy. The experiments are conducted using simulated and real-world crash data sets. The results indicate that the synthesised crash data by CGAN have the same distribution as the original data and the Augmented SPFs outperforms Base SPFs in almost all aspects especially when the dispersion parameter is low.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 8 of its 34 references that are in the map. First posted on arXiv 21 Dec 2021.
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8 of 34 references.
References · 8
- 2014Conditional Generative Adversarial Netscited by 2,152
- 2016NIPS 2016 Tutorial: Generative Adversarial Networkscited by 284
- 2020A Review on Generative Adversarial Networks: Algorithms, Theory, and Applicationscited by 209
- 2018Synthetic Data Augmentation using GAN for Improved Liver Lesion Classificationcited by 136
- 2020A Survey on Generative Adversarial Networks: Variants, Applications, and Trainingcited by 97
- 2017Imitating Driver Behavior with Generative Adversarial Networkscited by 92
- 2018Comparison of non-linear activation functions for deep neural networks on MNIST classification taskcited by 19
- 2021CGAN-EB: A Non-parametric Empirical Bayes Method for Crash Hotspot Identification Using Conditional Generative Adversarial Networks: A Real-world Crash Data Studycited by 1
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Citing papers per year: 2023: 1.
1 paper.