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.
Highlights
The trip starting and ending time, travel distance, travel frequency, activity duration, and some analogous features are the typical form of vehicle travel behaviors
The values of within-cluster variation and the Davies-Bouldin index (DBI)/Silhouette Coefficient (SC) are shown as functions of the number of clusters in Figures 5(a) and 5(b)
The value of SC of seven groups is just a little better than six groups but the value of DBI of six groups is much better than seven groups
Summary
The trip starting and ending time, travel distance, travel frequency, activity duration, and some analogous features are the typical form of vehicle travel behaviors. Clustering vehicles based on their travel characteristics is one of the vital methods for studying the representativeness of specific groups among the whole vehicle population and the travel profile of each group provides an aggregated characterization for the vehicles of a group as a whole [7]. It can provide transportation planners with richer travel demand information for improving the system performance or better assessing network investments
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