Crash Data Augmentation Using Conditional Generative Adversarial Networks (CGAN) for Improving Safety Performance Functions
This work addresses the need for more accurate crash prediction models in traffic safety, though it is incremental as it applies an existing CGAN method to a specific domain.
The paper tackled the problem of improving crash frequency models by augmenting crash data using Conditional Generative Adversarial Networks, resulting in Augmented Safety Performance Functions that outperformed base models in hotspot identification, prediction accuracy, and dispersion parameter estimation, especially with low dispersion parameters.
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.