A Superposition Assessment Framework of Multi-Source Traffic Risks for Mega-Events Using Risk Field Model and Time-Series Generative Adversarial Networks

In this study, a novel traffic risk assessment framework of mega-events that integrate risk field and deep learning is proposed. Considering the inherent difference of different traffic risks, the risk quantification and standardization is conducted first. Then several risk field models are constructed to quantify the impacts of multi-source traffic risk superposition on mega-events. Then a time-series generative adversarial networks (TimeGAN) is used to predict the evolution of superposition risk. We select 2022 Beijing Winter Olympics as a case to explore the superposition effects of different traffic risks on the convoy entrance of this mega-event. The results illustrate the superposition risks are significantly associated with the strength of each traffic risk, and the distance from the traffic risk location to the convoy entrance. Furthermore, the temporal evolutions for different traffic risks and their superposition are forecasted using TimeGAN. The results show the unexpected traffic congestion risk presents the highest predictive performance (i.e., the average error for RMSE, MAE, and MSE is 0.135%) and the superposition traffic risks present the lowest predictive performance (the average error is 0.536%). Comparison between different methods demonstrates TimeGAN outperforms other methods in predicting both single traffic risks and superposition risks. The research findings could be potentially referenced in multi-source traffic risk management for mega-events.