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. Acknowledgements This work was supported by the Ministry of Education Tier 2 Grant (MOE-T2EP40124-0001, P.B.). S.M. and M.R. acknowledge the support of the National Research Foundation, Singapore, under its NRF fellowship (NRF-NRFF15-2023-0010, S.M.). The authors used OpenAI’s ChatGPT to correct the typos and the grammar of this manuscript. The authors verified the accuracy, validity, and appropriateness of any content generated by the language model. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Rights and permissions Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons