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Unified route representation learning for multi-modal transportation recommendation with spatiotemporal pre-training

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Abstract

Multi-modal transportation recommendation aims to provide the most appropriate travel route with various transportation modes according to certain criteria. After analyzing large-scale navigation data, we find that route representations exhibit two patterns: spatio-temporal autocorrelations within transportation networks and the semantic coherence of route sequences. However, there are few studies that consider both patterns when developing multi-modal transportation systems. To this end, in this paper, we study multi-modal transportation recommendation with unified route representation learning by exploiting both spatio-temporal dependencies in transportation networks and the semantic coherence of historical routes. Specifically, we first transform the multi-modal transportation network into time-dependent multi-view transportation graphs and devise a graph-based contextual encoder to impute the missing traffic condition in transportation networks by leveraging various contextual factors. Then, we propose a hierarchical multi-task route representation learning (HMTRL) framework for recommendations, including (1) a spatiotemporal graph neural network module to capture the spatial and temporal autocorrelation, (2) a coherent-aware attentive route representation learning module to explicitly model route coherence from historical routes, and (3) a hierarchical multi-task learning module to differentiate route representations for different transport modes by incorporating multiple auxiliary tasks equipped in different network layers. Moreover, to improve the model generalization capability, we further propose spatiotemporal pre-training strategies to exploit rich self-supervision signals hidden in transportation networks and historical trajectories. Finally, extensive experimental results on two large-scale real-world datasets demonstrate the effectiveness of the proposed system against eight baselines.

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Notes

  1. https://github.com/hanjindong/HMTRL-Pytorch

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant No. 62102110, Foshan HKUST Projects (FSUST21-FYTRI01A, FSUST21-FYTRI02A), Hong Kong RGC TRS T41-603/20-R, and Hetao Shenzhen-Hong Kong Science and Technology Innovation Cooperation Zone (HZQB-KCZYB-2020083).

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Authors and Affiliations

  1. The Thrust of Artificial Intelligence, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China

    Hao Liu, Jindong Han & Hui Xiong

  2. The Department of Computer Science and Engineering, The Hong Kong University of Science and Technology, Hong Kong, SAR, China

    Hao Liu, Kai Chen & Hui Xiong

  3. HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute, Shenzhen, China

    Hao Liu

  4. University of Central Florida, Orlando, USA

    Yanjie Fu

  5. Baidu Research, Beijing, China

    Yanyan Li

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  1. Hao Liu
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Liu, H., Han, J., Fu, Y. et al. Unified route representation learning for multi-modal transportation recommendation with spatiotemporal pre-training. The VLDB Journal 32, 325–342 (2023). https://doi.org/10.1007/s00778-022-00748-y

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