Event-Aware Multimodal Mobility Nowcasting
This work improves spatio-temporal predictive modeling for multimodal mobility, which is crucial for Mobility-as-a-Service applications, though it appears incremental by enhancing an existing canonical network.
The paper tackles the problem of predicting crowd movements in Mobility-as-a-Service by addressing the challenge of societal events causing deviations from normal mobility patterns, resulting in an enhanced spatio-temporal network (EAST-Net) that outperforms state-of-the-art baselines on real-world datasets.
As a decisive part in the success of Mobility-as-a-Service (MaaS), spatio-temporal predictive modeling for crowd movements is a challenging task particularly considering scenarios where societal events drive mobility behavior deviated from the normality. While tremendous progress has been made to model high-level spatio-temporal regularities with deep learning, most, if not all of the existing methods are neither aware of the dynamic interactions among multiple transport modes nor adaptive to unprecedented volatility brought by potential societal events. In this paper, we are therefore motivated to improve the canonical spatio-temporal network (ST-Net) from two perspectives: (1) design a heterogeneous mobility information network (HMIN) to explicitly represent intermodality in multimodal mobility; (2) propose a memory-augmented dynamic filter generator (MDFG) to generate sequence-specific parameters in an on-the-fly fashion for various scenarios. The enhanced event-aware spatio-temporal network, namely EAST-Net, is evaluated on several real-world datasets with a wide variety and coverage of societal events. Both quantitative and qualitative experimental results verify the superiority of our approach compared with the state-of-the-art baselines. Code and data are published on https://github.com/underdoc-wang/EAST-Net.