{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T03:48:28Z","timestamp":1747194508106,"version":"3.40.5"},"reference-count":18,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2018,3,1]],"date-time":"2018-03-01T00:00:00Z","timestamp":1519862400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"Project of the National Natural Science Fund of China","award":["61303041"],"award-info":[{"award-number":["61303041"]}]},{"name":"Industrial Research Projects of the Shaanxi Province","award":["2015GY002"],"award-info":[{"award-number":["2015GY002"]}]},{"name":"Industrial Research Projects of the Shaanxi Province","award":["2016GY-078"],"award-info":[{"award-number":["2016GY-078"]}]},{"name":"Key Science and Technology Innovation Team of Shaanxi Province","award":["2017KCT-29"],"award-info":[{"award-number":["2017KCT-29"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Distributed Sensor Networks"],"published-print":{"date-parts":[[2018,3]]},"abstract":"<jats:p> Service recommendations help travelers locate en route traffic information service of interest in a timely manner. However, recommendations based on simple traffic information, such as the number of requests for the location of a facility, fail to consider an individual\u2019s preferences. Most existing work on improving service recommendations has continued to utilize the same ratings and rankings of services without consideration of diverse users\u2019 demands. The challenge remains to push forward the modeling of spatiotemporal trajectories to improve service recommendations. In this research, we proposed a new method to address the above challenge. We developed a personalized service-trajectory correlation that could recommend the most appropriate services to users. In addition, we proposed the use of \u201ccongeniality\u201d probability to measure the service demand similarity of two travelers based on their service-visiting behaviors and preferences. We employed a clustering-based scheme, taking into account the spatiotemporal dimensions to refine the trajectories at each spot where travelers stayed at a certain point in time. Experiments were conducted employing a real global positioning system\u2013based dataset. The test results demonstrated that our proposed approach could reduce the deviation of the trajectory measurement to 10% and enhance the success rates of the service recommendations to 60%. <\/jats:p>","DOI":"10.1177\/1550147718767845","type":"journal-article","created":{"date-parts":[[2018,3,28]],"date-time":"2018-03-28T11:28:56Z","timestamp":1522236536000},"page":"155014771876784","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":13,"title":["Personalized service recommendations for travel using trajectory pattern discovery"],"prefix":"10.1177","volume":"14","author":[{"given":"Zongtao","family":"Duan","sequence":"first","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an, Shaanxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5465-610X","authenticated-orcid":false,"given":"Lei","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an, Shaanxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuehui","family":"Gong","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an, Shaanxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yishui","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Chang\u2019an University, Xi\u2019an, Shaanxi China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2018,3,28]]},"reference":[{"first-page":"82","volume-title":"Proceedings of the 11th international conference on web and wireless geographical information systems","author":"Braga RB","key":"bibr1-1550147718767845"},{"key":"bibr2-1550147718767845","doi-asserted-by":"publisher","DOI":"10.1007\/s10707-014-0220-8"},{"key":"bibr3-1550147718767845","doi-asserted-by":"publisher","DOI":"10.1109\/TASE.2014.2369429"},{"key":"bibr4-1550147718767845","first-page":"1","volume":"46","author":"Yu Z","year":"2015","journal-title":"IEEE T Hum-Mach Syst"},{"issue":"3","key":"bibr5-1550147718767845","first-page":"1","volume":"28","author":"Kefalas P","year":"2015","journal-title":"IEEE T Knowl Data En"},{"issue":"2","key":"bibr6-1550147718767845","first-page":"5","volume":"2","author":"Skoumas G","year":"2016","journal-title":"ACM Trans Spat Algorithms Syst"},{"key":"bibr7-1550147718767845","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2016.2541160"},{"volume-title":"Proceedings of the 94th annual meeting transportation research board (TRB)","author":"Zheng H","key":"bibr8-1550147718767845"},{"key":"bibr9-1550147718767845","unstructured":"Song D, He X, Peeta S, Integrated Deployment Architecture for Predictive Real-Time Traffic Routing Incorporating Human Factors Considerations. Technical research report, NEXTRANS Project No. 080PY03, 2014, pp.1\u201350. Washington, DC: USDOT Region 5 Regional University Transportation Center."},{"key":"bibr10-1550147718767845","doi-asserted-by":"publisher","DOI":"10.7708\/ijtte.2016.6(2).05"},{"key":"bibr11-1550147718767845","first-page":"1","volume":"1","author":"Peng HJ","year":"2015","journal-title":"Math Probl Eng"},{"key":"bibr12-1550147718767845","doi-asserted-by":"crossref","unstructured":"Oliveros MM, Kai N. Automatic calibration of agent-based public transit assignment path choice to count data. 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