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Predicting the next turn at road junction from big traffic data
Yan Zhuang1; Simon Fong1; Meng Yuan1; Yunsick Sung2; Kyungeun Cho2; Raymond K. Wong3
2017-03-21
Source PublicationJOURNAL OF SUPERCOMPUTING
ISSN0920-8542
Volume73Issue:7Pages:3128-3148
Abstract

Smart city is an emerging research field nowadays, with emphasis of using big data to enhance citizens' quality of life. One of the prevalent smart city projects is to use big traffic data collected from road users over time, for road planning, traffic light scheduling, traffic jam relief, and public security. In particular, being able to know a road user's current location and predict his/her next move is important in today's intelligent transportation systems. Trajectory prediction has become a prudential research study direction, by which many algorithms have been published before. In this paper, we present a simple probabilistic model which predicts road users' next locations based on the "concept of segments" abstracted from historical trails which the users have taken and accumulated over time in some data archive. Given a trajectory and a current location, the road user's next move in terms of road direction can be predicted at the junction. It is found that each road user would have his/her unique travel pattern hidden in the aggregate big traffic data. These patterns could be modeled from connected segments for simplicity. With the longer the trail and more frequently this trail was travelled, the more accurate that the next turn can be predicted. Simulation experiment was conducted based on summing up the segments from empirical trajectory data that was used in trajectory data mining by Microsoft. The results of our alternative model in contrast to the state of the arts demonstrated good efficacy.

KeywordLocation Prediction Trajectory Mining Gps Trajectory Analysis
DOI10.1007/s11227-017-2013-y
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS IDWOS:000405297000019
PublisherSPRINGER
The Source to ArticleWOS
Fulltext Access
Citation statistics
Cited Times [WOS]:3   [WOS Record]     [Related Records in WOS]
Document TypeJournal article
CollectionDEPARTMENT OF COMPUTER AND INFORMATION SCIENCE
Corresponding AuthorSimon Fong
Affiliation1.Department of Computer and Information Science, University of Macau, Taipa, Macau SAR
2.Department of Multimedia Engineering, Dongguk University, Seoul, South Korea
3.School of Computer Science and Engineering, University of New South Wales, Sydney, Australia
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Yan Zhuang,Simon Fong,Meng Yuan,et al. Predicting the next turn at road junction from big traffic data[J]. JOURNAL OF SUPERCOMPUTING,2017,73(7):3128-3148.
APA Yan Zhuang,Simon Fong,Meng Yuan,Yunsick Sung,Kyungeun Cho,&Raymond K. Wong.(2017).Predicting the next turn at road junction from big traffic data.JOURNAL OF SUPERCOMPUTING,73(7),3128-3148.
MLA Yan Zhuang,et al."Predicting the next turn at road junction from big traffic data".JOURNAL OF SUPERCOMPUTING 73.7(2017):3128-3148.
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