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<p>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 &#xa0;&gt; 0.70; R-squared &#xa0;&gt; 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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月3日

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  • Khoa D. Vo,
  • Seung Woo Ham,
  • Mousumi Roy,
  • Swapnil Mishra,
  • Prateek Bansal

摘要

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.