{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,6]],"date-time":"2026-10-06T11:16:36Z","timestamp":1791285396998,"version":"4.1.0"},"reference-count":31,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T00:00:00Z","timestamp":1661385600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Transportation Research Record: Journal of the Transportation Research Board"],"published-print":{"date-parts":[[2023,4]]},"abstract":"<jats:p> The COVID-19 pandemic has had serious adverse impacts on public transport service providers. Most public transport lines exhibit reduced ridership levels while, at the same time, some of them may exhibit passenger demand levels beyond the pandemic-imposed capacity limitations. This study models the problem of bus dispatching time optimization within a periodic rolling horizon optimization framework that reacts to travel time and passenger demand variations. This model allows public transport service providers to adjust their bus schedules periodically to avoid in-vehicle crowding beyond the pandemic-imposed capacity limit. The proposed model is a mixed-integer linear program that considers the possible changes to vehicle schedules and tries to minimize the number of vehicles required to perform the service while adhering to the COVID-19 capacity restrictions. Case study results from the implementation of our model on bus Line 2 in the Twente region in the Netherlands are provided demonstrating the potential gains when rescheduling the trip dispatching times and vehicle schedules. <\/jats:p>","DOI":"10.1177\/03611981221114119","type":"journal-article","created":{"date-parts":[[2022,8,25]],"date-time":"2022-08-25T10:29:06Z","timestamp":1661423346000},"page":"765-777","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":11,"title":["Periodic Optimization of Bus Dispatching Times and Vehicle Schedules Considering the COVID-19 Capacity Limits: A Dutch Case Study"],"prefix":"10.1177","volume":"2677","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3009-1527","authenticated-orcid":false,"given":"Konstantinos","family":"Gkiotsalitis","sequence":"first","affiliation":[{"name":"Department of Civil Engineering, University of Twente, Enschede, Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6101-1924","authenticated-orcid":false,"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"National Engineering Laboratory of Integrated Transportation Big Data Application Technology, School of Transportation and Logistics, Southwest Jiaotong University, Chengdu, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,8,25]]},"reference":[{"key":"bibr1-03611981221114119","doi-asserted-by":"publisher","DOI":"10.1177\/0361198121990674"},{"key":"bibr2-03611981221114119","doi-asserted-by":"publisher","DOI":"10.1080\/01441647.2020.1857886"},{"key":"bibr3-03611981221114119","doi-asserted-by":"publisher","DOI":"10.5038\/2375-0901.22.1.1"},{"key":"bibr4-03611981221114119","unstructured":"UITP. 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