{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T04:42:38Z","timestamp":1683693758817},"reference-count":34,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,5,8]],"date-time":"2023-05-08T00:00:00Z","timestamp":1683504000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Shenzhen Project, China","award":["JSGG20210802154807022"]},{"name":"NSF of Guangdong Province, China","award":["2023A1515012716"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"The Time-Period-Based Most Frequent Path (TPMFP) problem has been a hot topic in traffic studies for many years. The TPMFP problem involves finding the most frequent path between two locations by observing the travelling behaviors of drivers in a specific time period. However, the previous researchers over-simplify the road network, which results in the ignorance of transfer costs at intersections. To address this problem more elegantly, we built up an urban topology model consisting of Intersection Vertices and Connection Vertices. Specifically, we split the Intersection Vertices to eliminate the influence of transfer cost on finding TPMFP and generate Trajectory\u2013Topology from GPS records data. In addition, we further leveraged the Footmark Graph method to find the TPMFP. Finally, we conducted extensive experiments using a real-world dataset containing over eight million GPS records. Compared to the current state-of-the-art method, our proposed approach can find more reasonable MFP in approximately 10% of cases during off-peak hours and 40% of cases during peak hours.<\/jats:p>","DOI":"10.3390\/bdcc7020088","type":"journal-article","created":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T06:28:08Z","timestamp":1683613688000},"page":"88","source":"Crossref","is-referenced-by-count":0,"title":["Finding the Time-Period-Based Most Frequent Path from Trajectory\u2013Topology"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8557-3793","authenticated-orcid":false,"given":"Jianing","family":"Ding","sequence":"first","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China"}]},{"given":"Xin","family":"Jin","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China"}]},{"given":"Zhiheng","family":"Li","sequence":"additional","affiliation":[{"name":"Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen 518000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1111\/j.1467-839X.2007.00241.x","article-title":"Social network analysis: A methodological introduction","volume":"11","author":"Butts","year":"2008","journal-title":"Asian J. 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