Highlights Propose an interpretable and high-accuracy model for ride-sourcing demand forecasting. Propose a systematic process to identify heterogeneous predictability of different regions. Reveal heteroscedasticity of demand uncertainty across regions and categorize region types. Exploit correlation between local supply and demand with bidirectional attention mechanism. Verify outperformance and interpretability of Bi-STTNP by large-scale real-world data.

    Abstract Understanding the spatial–temporal stochasticity in shared mobility is crucial for ride-sourcing demand forecasting, supply–demand management, and vehicle dispatch optimization. In contrast to conventional deep learning methods that typically provide point predictions or deterministic predictions, this paper introduces the bidirectional spatial–temporal Transformer neural processes (Bi-STTNP) prediction model, which stands out from conventional deep learning methods by providing probabilistic predictions and uncertainty estimations for ride-sourcing demand. Bi-STTNP captures the multivariate spatial–temporal Gaussian distribution of demand, offering not only demand expectations but also comprehensive uncertainty representations. We propose a predictability identification process based on predictive distributions to assess varying predictability across time slots and regions, improving interpretability. Our model, consisting of the bidirectional supply–demand attention module and spatial–temporal Transformer module, maintains interpretability while ensuring accurate demand expectation predictions. Extensive experiments on a real-world dataset of 15 million ride-sourcing orders in Hangzhou, China, demonstrate that Bi-STTNP outperforms baseline models in predicting demand expectation and quantifying demand uncertainty. Furthermore, we compute loose spatial–temporal predictability lower bounds and categorize regions by predictability, providing insights for optimizing passenger pricing strategies, driver incentives, and vehicle dispatching in ride-sourcing platforms.


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    Title :

    Demand forecasting and predictability identification of ride-sourcing via bidirectional spatial-temporal transformer neural processes


    Contributors:
    Li, Chuanjia (author) / Geng, Maosi (author) / Chen, Yong (author) / Cai, Zeen (author) / Zhu, Zheng (author) / Chen, Xiqun (Michael) (author)


    Publication date :

    2023-11-17




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






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