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Predicting Traveler Movement Based on a Hybrid Model of Hierarchical Clustering and Bayesian Network

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In crowded cities, knowing traveler movement in advance can help to efficiently alert the travelers and guide them avoid bad traffic condition areas. With the popularity of GPS enabled devices, GPS data can be used to identify traveler locations, as well as to predict traveler movement. In the paper, we propose a method for predicting traveler movement which is composed of three components: a string similarity function based on Longest Common Subsequences (LCSS), Hierarchical clustering and Bayes’ rule. The relative trajectory patterns are constructed by Hierarchical Clustering in order to diving all historical movement sequences into group having highest similarity score. The spatial-temporal probability of next location in current travel sequence is calculated based on particular transition matrix built from relative trajectory patterns. To validate the proposed method, Global Position System (GPS) data was collected from traveler movements in the urban of Ho Chi Minh City, Vietnam. The result obtained is promising to implement an Intelligent Transport System (ITS) application to determine traveler preference.
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Keywords: Bayesian Network; Hierarchical Clustering; ITS; Spatial-Temporal Sequence

Document Type: Research Article

Affiliations: Faculty of Computer Science and Computer Engineering, Ho Chi Minh City University of Technology, Vietnam

Publication date: 01 September 2016

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