  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.
