A data aggregation-based spatiotemporal model for rail transit risk path forecasting

Failure-related urban rail events can disrupt transit system operations and traffic flow and lead to serious safety problems worldwide. A domino effect can occur if potential cascading events of major failures are not effectively mitigated and controlled. Therefore, accurate risk path forecasting in rail transit systems is a significant and challenging task. Because of the limitations of traditional models in terms of computational power and feature extraction capabilities, this paper proposes a probabilistic deep learning framework that can process multisource data for risk path forecasting in urban rail transit. This paper first proposes a method for constructing a large-scale risk path ground truth dataset when fault events occur. Then, the framework uses a graph-based feature mapping method to model social media, passenger flow, and station failure information. Finally, we proposed a spatiotemporal feature extractor and a dynamic difference weighting loss function to extract features and optimize parameters. We apply real-world data from 2018 to 2019 from the Beijing urban rail transit system for experimental analysis. The analyzed results demonstrate that the proposed model exceeds the baseline models by at least 2.9% in terms of F1 value, fusing multi-source data exceeds using single-source data by at least 14% in terms of F1 value and the proposed attention mechanism and dynamic loss function weights can effectively improve the forecasting performance of the model. Furthermore, the results of the different steps ahead demonstrate that the above results are robust. The application of the model can effectively handle safety issues such as cascade failures.

Automated summary

This paper proposes a probabilistic deep learning framework for risk path forecasting in urban rail transit. It constructs a large-scale risk path ground truth dataset and uses multi-source data and feature extraction methods to optimize parameters and improve forecasting performance. The experimental analysis results demonstrate that the proposed model outperforms baseline models in terms of F1 value, and integrating multi-source data can further improve forecasting performance.