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Journal of Advanced Transportation
Volume 2017, Article ID 1738085, 14 pages
Research Article

Clustering Vehicle Temporal and Spatial Travel Behavior Using License Plate Recognition Data

Key Laboratory of Road and Traffic Engineering, Tongji University, 4800 Cao’an Road, Shanghai 201804, China

Correspondence should be addressed to Chao Yang; nc.ude.ijgnot@cyijgnot

Received 27 December 2016; Revised 13 March 2017; Accepted 2 April 2017; Published 24 April 2017

Academic Editor: Guohui Zhang

Copyright © 2017 Huiyu Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Understanding travel patterns of vehicle can support the planning and design of better services. In addition, vehicle clustering can improve management efficiency through more targeted access to groups of interest and facilitate planning by more specific survey design. This paper clustered 854,712 vehicles in a week using -means clustering algorithm based on license plate recognition (LPR) data obtained in Shenzhen, China. Firstly, several travel characteristics related to temporal and spatial variability and activity patterns are used to identify homogeneous clusters. Then, Davies-Bouldin index (DBI) and Silhouette Coefficient (SC) are applied to capture the optimal number of groups and, consequently, six groups are classified in weekdays and three groups are sorted in weekends, including commuting vehicles and some other occasional leisure travel vehicles. Moreover, a detailed analysis of the characteristics of each group in terms of spatial travel patterns and temporal changes are presented. This study highlights the possibility of applying LPR data for discovering the underlying factor in vehicle travel patterns and examining the characteristic of some groups specifically.