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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 / No. 4: AAAI-22 Technical Tracks 4

Event-Aware Multimodal Mobility Nowcasting

February 1, 2023

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Authors

Zhaonan Wang

Center for Spatial Information Science, University of Tokyo School of Computing Technologies, RMIT University


Renhe Jiang

Center for Spatial Information Science, University of Tokyo Information Technology Center, University of Tokyo


Hao Xue

School of Computing Technologies, RMIT University


Flora D. Salim

School of Computing Technologies, RMIT University


Xuan Song

Center for Spatial Information Science, University of Tokyo


Ryosuke Shibasaki

Center for Spatial Information Science, University of Tokyo


DOI:

10.1609/aaai.v36i4.20342


Abstract:

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.

Topics: AAAI

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HOW TO CITE:

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki Event-Aware Multimodal Mobility Nowcasting Proceedings of the AAAI Conference on Artificial Intelligence (2022) 4228-4236.

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki Event-Aware Multimodal Mobility Nowcasting AAAI 2022, 4228-4236.

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki (2022). Event-Aware Multimodal Mobility Nowcasting. Proceedings of the AAAI Conference on Artificial Intelligence, 4228-4236.

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki. Event-Aware Multimodal Mobility Nowcasting. Proceedings of the AAAI Conference on Artificial Intelligence 2022 p.4228-4236.

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki. 2022. Event-Aware Multimodal Mobility Nowcasting. "Proceedings of the AAAI Conference on Artificial Intelligence". 4228-4236.

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki. (2022) "Event-Aware Multimodal Mobility Nowcasting", Proceedings of the AAAI Conference on Artificial Intelligence, p.4228-4236

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki, "Event-Aware Multimodal Mobility Nowcasting", AAAI, p.4228-4236, 2022.

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki. "Event-Aware Multimodal Mobility Nowcasting". Proceedings of the AAAI Conference on Artificial Intelligence, 2022, p.4228-4236.

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki. "Event-Aware Multimodal Mobility Nowcasting". Proceedings of the AAAI Conference on Artificial Intelligence, (2022): 4228-4236.

Zhaonan Wang||Renhe Jiang||Hao Xue||Flora D. Salim||Xuan Song||Ryosuke Shibasaki. Event-Aware Multimodal Mobility Nowcasting. AAAI[Internet]. 2022[cited 2023]; 4228-4236.


ISSN: 2374-3468


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