Leveraging Spatial–Temporal Heterogeneity and Cross-Mode Interactions: A Meta-Learning Approach for Multimodal Transportation Demand Prediction

Accurately and jointly predicting multimodal transportation demand is crucial for pre-allocating transport resources, enhancing the resilience of traffic systems. However, current approaches insufficiently explore inter- and intra-mode heterogeneity, resulting in undifferentiated dependency extraction. Moreover, existing research struggles to model cross-mode interactions among three or more transportation modes and adapt to dynamic relations in multimodal demand. To address these limitations, we propose a novel multimodal demand prediction model based on a meta-parameter learning network (MMDNet), centered on characterizing multimodal traffic spatial-temporal heterogeneity and unifying the modeling of cross-mode interactions. Our model features: 1) a spatial-temporal heterogeneity meta-parameter learning method, capturing both inter- and intra-mode heterogeneity to steer more targeted dependency extraction than previous studies; 2) a spatial-temporal evolving unified graph generator, transcending prior studies’ limitations in unifying dynamic interactions across three or more modes by creating dynamic unified graphs. Extensive experiments on three real-world datasets (New York, Beijing and Chicago) covering four different traffic modes are carried out to evaluate the MMDNet. The model achieves a 6.65% performance gain over advanced baselines and demonstrates strong cross-city adaptability. Abundant interpretability analyses show our model can semantically encode explainable cross-mode interactions and differences between modes. Source codes are available at https://github.com/zhjiang1/MMDNet