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

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.

Automated summary

Understanding spatial-temporal stochasticity in shared mobility is crucial, and this study introduces the Bi-STTNP prediction model that provides probabilistic predictions and uncertainty estimations for ride-sourcing demand, outperforming conventional deep learning methods. The model captures the multivariate spatial-temporal Gaussian distribution of demand and offers comprehensive uncertainty representations.