Spatial-Temporal Attention Fusion Network for short-term passenger flow prediction on holidays in urban rail transit systems
This addresses a specific problem for urban rail transit traffic management during holidays, where prediction is crucial but understudied, representing an incremental improvement by adapting deep learning methods to a niche domain.
The paper tackles short-term passenger flow prediction on holidays in urban rail transit systems, a challenging task due to suddenness and irregularity, by proposing a Spatial-Temporal Attention Fusion Network (STAFN) that fuses historical passenger flow and social media data, and results show it outperforms benchmark methods with better robustness and advantages for practical applications.
The short term passenger flow prediction of the urban rail transit system is of great significance for traffic operation and management. The emerging deep learning-based models provide effective methods to improve prediction accuracy. However, most of the existing models mainly predict the passenger flow on general weekdays or weekends. There are only few studies focusing on predicting the passenger flow on holidays, which is a significantly challenging task for traffic management because of its suddenness and irregularity. To this end, we propose a deep learning-based model named Spatial Temporal Attention Fusion Network comprising a novel Multi-Graph Attention Network, a Conv-Attention Block, and Feature Fusion Block for short-term passenger flow prediction on holidays. The multi-graph attention network is applied to extract the complex spatial dependencies of passenger flow dynamically and the conv-attention block is applied to extract the temporal dependencies of passenger flow from global and local perspectives. Moreover, in addition to the historical passenger flow data, the social media data, which has been proven that they can effectively reflect the evolution trend of passenger flow under events, are also fused into the feature fusion block of STAFN. The STAFN is tested on two large-scale urban rail transit AFC datasets from China on the New Year holiday, and the prediction performance of the model are compared with that of several conventional prediction models. Results demonstrate its better robustness and advantages among benchmark methods, which can provide overwhelming support for practical applications of short term passenger flow prediction on holidays.