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. This study develops an approach to recover multimodal hourly urban mobility patterns by fusing small-scale surveys with near-census transit data. The method is validated by reconstructing private-vehicle and walking flows in Singapore and Seoul.
Vo et al. (Wed,) studied this question.