{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T15:30:18Z","timestamp":1786116618939,"version":"build-2736575974"},"reference-count":43,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Public Health"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Shared urban mobility systems are critical infrastructure for ensuring essential travel and maintaining socioeconomic stability during public health crises. However, the long-term system resilience and spatial\u2013temporal equity of these networks under severe macro-shocks (e.g., the COVID-19 pandemic) and subsequent recovery phases remain underexplored.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Utilizing a comprehensive longitudinal dataset (2019\u20132024) from Ningbo, China, this study constructs a multi-dimensional evaluation framework encompassing market supply, operational performance, economic viability, and service equity. We applied a projection pursuit-weighted TOPSIS model to quantify the overall resilience trajectory. Furthermore, to uncover structural disparities in service access, tensor decomposition and the Theil index were employed to measure the spatial\u2013temporal distribution of passenger waiting times.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The empirical findings reveal that the socio-ecological resilience of the ride-hailing system exhibited severe vulnerability during the initial COVID-19 outbreak (Q1 2020), with closeness values dropping to 0.290. While overall resilience peaked during the post-pandemic recovery (Q4 2023), the system experienced a structural \u201cinvolution,\u201d characterized by continuous supply expansion that outpaced demand, leading to degraded per-vehicle operational efficiency. Moreover, the study identified significant spatial\u2013temporal inequalities in mobility access during pandemic restrictions (2020\u20132021), which were progressively mitigated following strict regulatory interventions and market normalization.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This six-year longitudinal analysis highlights the dynamic vulnerability and adaptive capacity of urban mobility networks under prolonged public health emergencies. To build crisis-resilient cities, policymakers must implement data-driven, dynamic supply\u2013demand regulatory mechanisms to prevent resource misallocation, safeguard the livelihoods of gig workers, and ensure equitable access to essential mobility services across all urban sectors.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fpubh.2026.1831678","type":"journal-article","created":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T11:33:27Z","timestamp":1786016007000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Urban mobility resilience and spatial\u2013temporal equity under public health crises: longitudinal evidence from China\u2019s ride-hailing sector (2019\u20132024)"],"prefix":"10.3389","volume":"14","author":[{"given":"Baiying","family":"Sun","sequence":"first","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University","place":["Ningbo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengfeng","family":"Huang","sequence":"additional","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University","place":["Ningbo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kehong","family":"Guan","sequence":"additional","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University","place":["Ningbo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pengjun","family":"Zheng","sequence":"additional","affiliation":[{"name":"Faculty of Maritime and Transportation, Ningbo University","place":["Ningbo, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"103901","DOI":"10.1016\/j.jtrangeo.2024.103901","article-title":"Resilience and recovery: evaluating COVID pandemic effects on ride-hailing mobility and driver income dynamics","volume":"117","author":"Liu","year":"2024","journal-title":"J Transp Geogr"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"368","DOI":"10.1287\/msom.2017.0618","article-title":"The role of surge pricing on a service platform with self-scheduling capacity","volume":"19","author":"Cachon","year":"2017","journal-title":"Manuf Serv Oper Manag"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"4880","DOI":"10.1287\/mnsc.2020.3756","article-title":"Strategic timing and dynamic pricing for online resource allocation","volume":"67","author":"Abhishek","year":"2021","journal-title":"Manag Sci"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"220","DOI":"10.1016\/j.tranpol.2021.11.017","article-title":"Exploring impacts of COVID-19 on city-wide taxi and ride-sourcing markets: evidence from Ningbo, China","volume":"115","author":"Yu","year":"2022","journal-title":"Transp Policy"},{"key":"ref5","doi-asserted-by":"publisher","first-page":"1775","DOI":"10.1287\/isre.2023.1212","article-title":"Ridesharing and digital resilience for urban anomalies: evidence from the New York City taxi market","volume":"34","author":"Zhang","year":"2023","journal-title":"Inf Syst Res"},{"key":"ref6","doi-asserted-by":"publisher","first-page":"103901","DOI":"10.1016\/j.jtrangeo.2024.103901","article-title":"Resilience and recovery: evaluating COVID pandemic effects on ride-hailing mobility and driver income dynamics","volume":"117","author":"Liu","year":"2024","journal-title":"J Transp Geogr"},{"key":"ref7","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1016\/j.tranpol.2021.01.011","article-title":"COVID-19 and transport: findings from a world-wide expert survey","volume":"103","author":"Zhang","year":"2021","journal-title":"Transp Policy"},{"key":"ref8","doi-asserted-by":"publisher","first-page":"791","DOI":"10.1007\/s11116-022-10349-x","article-title":"Were ride-hailing fares affected by the COVID-19 pandemic? 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