{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,3]],"date-time":"2025-12-03T18:12:33Z","timestamp":1764785553459,"version":"build-2065373602"},"reference-count":32,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T00:00:00Z","timestamp":1734912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003995","name":"Natural Science Foundation of Anhui Province","doi-asserted-by":"publisher","award":["2208085ME147","2022AH051800","GFCX202411"],"award-info":[{"award-number":["2208085ME147","2022AH051800","GFCX202411"]}],"id":[{"id":"10.13039\/501100003995","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Project of the Natural Science Foundation of Universities in Anhui Province","award":["2208085ME147","2022AH051800","GFCX202411"],"award-info":[{"award-number":["2208085ME147","2022AH051800","GFCX202411"]}]},{"name":"2024 Independent Innovation Project of Anhui Public Meteorological Service Center","award":["2208085ME147","2022AH051800","GFCX202411"],"award-info":[{"award-number":["2208085ME147","2022AH051800","GFCX202411"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Unlicensed taxis seriously disrupt the transportation market order, and threaten passenger safety. Therefore, this paper proposes a method for identifying unlicensed taxis based on travel characteristics. First, the vehicle mileage and operation time are calculated using traffic surveillance bayonet data, and variance analysis is applied to identification indicators for unlicensed taxis. Secondly, the mathematical model for identifying unlicensed taxis is established. The model is validated using the Hosmer\u2013Lemeshow test, confusion matrix and ROC curve analysis. Finally, by applying methods such as geographic information matching, the spatiotemporal distribution characteristics of suspected unlicensed taxis in a city in Anhui Province are identified. The results show that the model effectively identifies suspected unlicensed taxis (ACC = 99.10%). The daily average mileage, daily average operating time, and number of operating days for suspected unlicensed taxis are significantly higher than those for private cars. Additionally, the suspected unlicensed taxis exhibit regular patterns in their travel origin\u2013destination points and temporal distribution, enabling traffic management authorities to implement targeted regulatory measures.<\/jats:p>","DOI":"10.3390\/s24248206","type":"journal-article","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T10:58:55Z","timestamp":1734951535000},"page":"8206","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["A Spatiotemporal Feature-Based Approach for the Detection of Unlicensed Taxis in Urban Areas"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6142-7292","authenticated-orcid":false,"given":"Yun","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Urban Construction and Transportation, Hefei University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3121-5488","authenticated-orcid":false,"given":"Rongqiao","family":"Li","sequence":"additional","affiliation":[{"name":"School of Civil Engineering and Transportation, Northeast Forestry University, Harbin 150040, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinyan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Urban Construction and Transportation, Hefei University, Hefei 230601, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,23]]},"reference":[{"key":"ref_1","first-page":"64","article-title":"Intelligent Recognition Model and its Mechnism for Illiagle Taxis Based on Spatio-Temporal Features","volume":"18","author":"Hu","year":"2022","journal-title":"Transp. 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