Understanding Freight Trip-Chaining Behavior Using a Spatial Data-Mining Approach with GPS Data

Abstract

Freight systems are a critical yet complex component of the transportation domain. Understanding the dynamic of freight movements will help in better management of freight demand and eventually improve freight system efficiency. This paper presents a series of data-mining algorithms to extract an individual truck’s trip-chaining information from multiday GPS data. Individual trucks’ anchor points were identified with the spatial clustering algorithm for density-based spatial clustering of applications with noise. The anchor points were linked to construct individual trucks’ trip chains with 3-day GPS data, which showed that 51% of the trucks in the data set had at least one trip chain. A partitioning around medoids nonhierarchical clustering algorithm was applied to group trucks with similar trip-chaining characteristics. Four clusters were generated and validated by visual inspection when the trip-chaining statistics were distinct from each other. This study sheds light on modeling freight-chaining behavior in the context of massive freight GPS data sets. The proposed trip chain extraction and behavior classification algorithms can be readily implemented by transportation researchers and practitioners to facilitate the development of activity-based freight demand models.