Event-Aware Multimodal Mobility Nowcasting
- URL: http://arxiv.org/abs/2112.08443v1
- Date: Tue, 14 Dec 2021 12:35:20 GMT
- Title: Event-Aware Multimodal Mobility Nowcasting
- Authors: Zhaonan Wang, Renhe Jiang, Hao Xue, Flora D. Salim, Xuan Song, Ryosuke
Shibasaki
- Abstract summary: Events-awaretemporal network EAST-Net is evaluated on real-world datasets with a wide variety and coverage of societal datasets.
Results verify the superiority of our approach compared with the state-of-the-art baselines.
- Score: 11.540605108140538
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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