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

Abstract

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 K-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.