Uncovering latent urban mobility patterns via smart-card and survey data fusion.

Abstract

Multiday, multimodal, time-dependent origin-destination (TD-OD) flows describe when, where, and how urban travel occurs. However, existing approaches are typically single-mode or rely on dense multimodal observations that are rarely available at scale. We show that multimodal TD-OD flows can be recovered by integrating household travel surveys with smart-card transit data. The proposed framework estimates cross-modal flow ratios from survey data and applies them to time-varying transit flows to recover private-vehicle and walking demand at hourly and day-of-week resolution. Validation against independent datasets in Singapore and Seoul shows strong agreement (common part of commuters  > 0.70; R-squared  > 0.60). The recovered flows support policy-relevant analyses, showing that transit is most competitive for intermediate distances (11-16 km) and transit-only data can underestimate peak epidemic infections by up to 50%. These findings demonstrate the importance of a scalable data fusion for multimodal mobility analysis in sustainable and resilient urban planning.