Comprehending and Analyzing Multiday Trip-Chaining Patterns of Freight Vehicles Using a Multiscale Method with Prolonged Trajectory Data

Abstract

Unlike personal cars for daily commuting, freight vehicles demonstrate vastly different traveling behaviors with longer spatial-temporal activity that is composed of multiday trip chains. Quantitatively identifying and describing the trip chains of freight vehicles could help in understanding typical freight behaviors and, thereby, provide a new perspective for analyzing freight systems. Therefore, based on the large-scale and prolonged vehicle trajectory datasets from global positioning system (GPS) equipment, a multiscale depot-identified method based on the density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed. The base depots and trip ends, which are critical components for multiday freight trip chains, are acquired to construct the complete multiday trip chains. Additionally, a new structure with multifeatures for synthetically depicting multiday trip chains is proposed. Finally, by discriminating the trip chain characteristics, the multiday trip-chaining patterns of freight vehicles are extracted, and their distributions across different vehicle types are analyzed. The results show that some travel patterns are limited to specific vehicle types. For example, the travel pattern in Cluster 3 only occurs for medium-sized ordinary trucks (METs) and tractor vehicles (TRVs). Additionally, the same travel pattern may occur for different vehicle types. The travel patterns of METs and TRVs are the same, but their proportions are different. The discovered patterns could be used in freight demand modeling, freight system simulations, or other customized management for operators.