Volume 26 Issue 6
Jun.  2026
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ZHAO Xia, SHI Zhuo-ya, LI Zhi-hong, LIU Jian-feng, WU Meng-lin, LI Chen-ji. Forecasting ride-hailing demand via contextual spatiotemporal cross-attention mechanism[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 186-197. doi: 10.19818/j.cnki.1671-1637.2026.075
Citation: ZHAO Xia, SHI Zhuo-ya, LI Zhi-hong, LIU Jian-feng, WU Meng-lin, LI Chen-ji. Forecasting ride-hailing demand via contextual spatiotemporal cross-attention mechanism[J]. Journal of Traffic and Transportation Engineering, 2026, 26(6): 186-197. doi: 10.19818/j.cnki.1671-1637.2026.075
shu

Forecasting ride-hailing demand via contextual spatiotemporal cross-attention mechanism

doi: 10.19818/j.cnki.1671-1637.2026.075
  • 1.

    Institute of Artificial Intelligence, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

  • 2.

    Smart Road and Transportation Research Center, Beijing University of Civil Engineering and Architecture, Beijing 102616, China

  • 3.

    Beijing Urban Construction & Transportation Design & Research Institute Co., Ltd., Beijing 100050, China

Funds:

National Natural Science Foundation of China 52402377

Natural Science Foundation of Beijing 8252005

More Information
  • Corresponding author: LI Zhi-hong, professor, PhD, E-mail: lizhihong@bucea.edu.cn
  • Received Date: 2025-04-01
  • Accepted Date: 2025-09-26
  • Rev Recd Date: 2025-08-24
  • Publish Date: 2026-06-28
    Abstract
  • To accurately characterize passengers' temporal preferences for frequent travel within specific geographic areas, or their spatial preferences for frequently visited locations within given time windows, this paper proposed a ride-hailing demand prediction model named ST-BiAformer (Spatiotemporal Bidirectional Association Transformer), which integrates a contextual spatiotemporal cross-attention mechanism. The model captured sequential dependencies in travel demand by constructing a contextual temporal correlation module; designed a spatiotemporal cross-attention mechanism to cross-extract spatial (or temporal) dependencies within specific temporal (or spatial) contexts, thereby revealing recurrent travel demand patterns along targeted spatiotemporal dimensions; and further developed a spatiotemporal fusion module to enhance the model's performance in both single-step and multi-step demand forecasting. The proposed model was evaluated on multiple datasets through a series of comparative, ablation, and robustness experiments, assessing its predictive performance under various spatiotemporal scenarios. Experimental results demonstrate that, compared with the optimal baseline model, the mean absolute error and root mean square error are reduced by 7.21% and 5.54%, respectively, demonstrating improved overall prediction accuracy. In the 5 min, 10 min, and 15 min forecasting tasks, both error metrics decrease by 1% - 7%, indicating sound single-step and multi-step predictive capability. The model achieves the best performance only when all three constituent submodules work jointly, enabling comprehensive modeling of travel demand dependencies along both contextual temporal and spatiotemporal correlation dimensions. The proposed model is expected to precisely match users' ride-hailing demands across heterogeneous spatiotemporal scenarios at different time periods or locations, thereby providing technical support for enhancing supply-demand scheduling in urban ride-hailing services.

     

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