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• Accurate risk path forecasting in rail transit systems is a significant and challenging task. • A probabilistic deep learning framework that can process multisource data for risk path forecasting. • The model can effectively handle safety issues such as cascade failures. 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 F 1 value, fusing multi-source data exceeds using single-source data by at least 14% in terms of F 1 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. [ABSTRACT FROM AUTHOR]

• A multitask learning model for short-term inflow prediction of multi-traffic modes is proposed. • The adaptive matrix and attention mechanism are used to extract the spatiotemporal features of multi-traffic modes. • We analyze the interaction mechanism in the multi-traffic modes system. • The research highlights the significance of jointly predicting the inflow of multi-traffic modes. Network-wide short-term inflow prediction is important in efficiently managing the urban transportation system. Nowadays, all kinds of traffic modes gradually become interconnected and form a complex multi-traffic modes system, while extensive studies focus on the single-traffic mode and ignore the correlations among different traffic modes. There exist some challenges for short-term inflow prediction of multi-traffic modes: (1) the interaction mechanism among multi-traffic modes is difficult to learn and few studies explore the mechanism, (2) the data of multi-traffic modes are usually heterogenous due to the different spatial units of different traffic modes, and (3) it is challenging to extract the complex and dynamic features of the multi-traffic modes and most existing methods apply static spatiotemporal correlations among multi-traffic modes, while the genuine correlations among different traffic modes might be missing. To tackle these challenges, this study proposed a multitask-learning-based model called M ulti M ode-former (M2-former) with the encoder-decoder structure for network-wide short-term inflow prediction of the multi-traffic modes system. Specifically, the encoder is designed to learn and capture the complex and dynamic spatiotemporal correlations of multi-traffic modes, and the decoder is designed to extract the features of the target traffic mode and share knowledge among multi-traffic modes. Extensive experiments are conducted based on the real-world multi-traffic modes system data of Beijing, China. Results prove the superiority of the M2-former. In addition, the spatial and temporal information interaction mechanisms among multi-traffic modes are also explored. This paper can provide a reliable method and critical insights for the management and understanding of a multi-traffic modes system. [ABSTRACT FROM AUTHOR]