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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2602.10502 |
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| _version_ | 1866912896840105984 |
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| author | Hao, Xixuan Li, Guicheng Wu, Daiqiang Guo, Xusen Zhu, Yumeng Zou, Zhichao Zhen, Peng Yao, Yao Liang, Yuxuan |
| author_facet | Hao, Xixuan Li, Guicheng Wu, Daiqiang Guo, Xusen Zhu, Yumeng Zou, Zhichao Zhen, Peng Yao, Yao Liang, Yuxuan |
| contents | The proliferation of ride-hailing services has fundamentally transformed urban mobility patterns, making accurate ride-hailing forecasting crucial for optimizing passenger experience and urban transportation efficiency. However, ride-hailing forecasting faces significant challenges due to geospatial heterogeneity and high susceptibility to external events. This paper proposes MVGR-Net(Multi-View Geospatial Representation Learning), a novel framework that addresses these challenges through a two-stage approach. In the pretraining stage, we learn comprehensive geospatial representations by integrating Points-of-Interest and temporal mobility patterns to capture regional characteristics from both semantic attribute and temporal mobility pattern views. The forecasting stage leverages these representations through a prompt-empowered framework that fine-tunes Large Language Models while incorporating external events. Extensive experiments on DiDi's real-world datasets demonstrate the state-of-the-art performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_10502 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web Hao, Xixuan Li, Guicheng Wu, Daiqiang Guo, Xusen Zhu, Yumeng Zou, Zhichao Zhen, Peng Yao, Yao Liang, Yuxuan Machine Learning The proliferation of ride-hailing services has fundamentally transformed urban mobility patterns, making accurate ride-hailing forecasting crucial for optimizing passenger experience and urban transportation efficiency. However, ride-hailing forecasting faces significant challenges due to geospatial heterogeneity and high susceptibility to external events. This paper proposes MVGR-Net(Multi-View Geospatial Representation Learning), a novel framework that addresses these challenges through a two-stage approach. In the pretraining stage, we learn comprehensive geospatial representations by integrating Points-of-Interest and temporal mobility patterns to capture regional characteristics from both semantic attribute and temporal mobility pattern views. The forecasting stage leverages these representations through a prompt-empowered framework that fine-tunes Large Language Models while incorporating external events. Extensive experiments on DiDi's real-world datasets demonstrate the state-of-the-art performance. |
| title | Enhancing Ride-Hailing Forecasting at DiDi with Multi-View Geospatial Representation Learning from the Web |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2602.10502 |