Personalized Travel Route Recommendation Based on GPS Trajectories

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dc.contributor.advisorWang, Xin
dc.contributor.authorCui, Ge
dc.contributor.committeememberZhong, Ming
dc.contributor.committeememberWang, Ruisheng
dc.contributor.committeememberChen, Zhangxing
dc.contributor.committeememberLiang, Steve H. L.
dc.contributor.committeememberRangelova, Elena V.
dc.date2018-11
dc.date.accessioned2018-07-04T21:10:07Z
dc.date.available2018-07-04T21:10:07Z
dc.date.issued2018-06-28
dc.description.abstractTravelling is a critical component of daily life. With new technology, personalized travel route recommendations are possible and have become a new research area. A personalized travel route recommendation refers to plan an optimal travel route between two geographical locations, based on the road networks and users’ travel preferences. In this thesis, it first proposes a segment-based map matching method to locate GPS trajectories onto the road network, and then extract users’ travel behaviours from their historical routes. Next, users’ travel behaviour frequencies are estimated by using collaborative filtering technique. This thesis defines two types of travel behaviours, appearance behaviour and transition behaviour, from users’ historical GPS trajectories and propose three personalized travel route recommendation methods¬, including CTRR, CTRR + and Map2R, to consider users’ personal travel preferences based on their historical GPS trajectories. A route with the maximum probability of a user’s travel behaviour is then generated. CTRR only considers user’s appearance behaviour and calculates the maximum probability route based on naïve Bayes model. Besides, CTRR is extended to CTRR+ by integrating distance with the user appearance behaviour probability. In MaP2R, it considers both appearance behaviour and transition behaviour, and calculate the maximum probability route based on Markov model. This thesis also conducts some case studies based on a real GPS trajectory dataset from Beijng, China. The experimental results show that the proposed CTRR methods achieve better results for travel route recommendations compared with the shortest distance path method, and both CTRR+ and MaP2R can enhance the performance of CTRR, respectively.en_US
dc.identifier.citationCui, G. (2018). Personalized Travel Route Recommendation Based on GPS Trajectories (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/32236en_US
dc.identifier.doihttp://dx.doi.org/10.11575/PRISM/32236
dc.identifier.urihttp://hdl.handle.net/1880/107014
dc.language.isoeng
dc.publisher.facultyGraduate Studies
dc.publisher.facultySchulich School of Engineering
dc.publisher.institutionUniversity of Calgaryen
dc.publisher.placeCalgaryen
dc.rightsUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.
dc.subjectPersonalized route recommendation
dc.subjectGPS trajectories
dc.subjectMap matching
dc.subject.classificationSociology--Transportationen_US
dc.titlePersonalized Travel Route Recommendation Based on GPS Trajectories
dc.typedoctoral thesis
thesis.degree.disciplineGeomatics Engineering
thesis.degree.grantorUniversity of Calgary
thesis.degree.nameDoctor of Philosophy (PhD)
ucalgary.item.requestcopytrue
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