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Travel Diary Semantics Enrichment of Trajectoriesbased on Trajectory Similarity Measures
KTH, School of Architecture and the Built Environment (ABE), Urban Planning and Environment, Geoinformatics.
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Trajectory data is playing an increasingly important role in our daily lives, as well as in commercial applications and scientific research. With the rapid development andpopularity of GPS, people can locate themselves in real time. Therefore, the users’behavior information can be collected by analyzing their GPS trajectory data, so as topredict their new trajectories’ destinations, ways of travelling and even thetransportation mode they use, which forms a complete personal travel diary. The taskin this thesis is to implement travel diary semantics enrichment of user’s trajectoriesbased on the historical labeled data of the user and trajectory similarity measures.Specially, this dissertation studies the following tasks: Firstly, trip segmentationconcerns detecting the trips from trajectory which is an unbounded sequence oftimestamp locations of the user. This means that it is important to detect the stops,moves and trips of the user between two consecutive stops. In this thesis, a heuristicrule is used to identify the stops. Secondly, tripleg segmentation concerns identifyingthe location / time instances between two triplegs where / when a user changesbetween transport modes in the user's trajectory, also called makes transport modetransitions. Finally, mode inference concerns identifying travel mode for each tripleg.Specially, steps 2 and 3 are both based on the same trajectory similarity measure andproject the information from the matched similar trip trajectory onto the unlabeled triptrajectory. The empirical evaluation of these three tasks is based on real word data set(contains 4240 trips and 5451 triplegs with 14 travel modes for 206 users using oneweek study period) and the experiment performance (including trends, coverage andaccuracy) are evaluated and accuracy is around 25% for trip segmentation; accuracyvaries between 50% and 55% for tripleg segmentation; for mode inference, it isbetween 55% and 60%. Moreover, accuracy is higher for longer trips than shortertrips, probably because people have more mode choices in short distance trips (likemoped, bus and car), which makes the measure more confused and the accuracy canbe increased by nearly 10% with the help of reverse trip identifiable, because it makesa trip have more similar historical trips and increases the probability that a newunlabeled trip can be matched based on its historical trips.

Place, publisher, year, edition, pages
2018. , p. 50
Keywords [en]
trajectory similarity measures, trip segmentation, tripleg segmentation, mode inference
National Category
Other Earth and Related Environmental Sciences
Identifiers
URN: urn:nbn:se:kth:diva-221426OAI: oai:DiVA.org:kth-221426DiVA, id: diva2:1174896
Presentation
2017-06-14, 3085, Drottning Kristinas väg 30, ground floor, Stockholm, 01:00 (English)
Supervisors
Examiners
Available from: 2018-01-17 Created: 2018-01-17 Last updated: 2018-01-17Bibliographically approved

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Master thesis_RUI LIU(1574 kB)85 downloads
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