Travel Patterns Analysis Using Tensor-Based Model from Large-Scale License Plate Recognition Data

Abstract

Travel patterns reflect the regularity of residents’ mobility, and it is a crucial factor to evaluate the reasonability of urban spatial structure and connectivity of road networks. Therefore, exploring travel patterns is of practical significance for urban planning, traffic management, and improvement of the operational efficiency of the transportation system. In this study, we apply the tensor model to explore travel patterns under temporal and spatial dimensions based on the license plate recognition (LPR) data collected from the Changsha city, China. As travel patterns are influenced by many variables, a method framework based on the tensor model is proposed to explore the influence of variables on travel characteristics. Firstly, we apply clustering algorithms and the principal component analysis method to extract main feature variables, which can achieve the purpose of dimensionality reduction and eliminate the complex collinearity among variables. Then, the tensor decomposition and reconstruction algorithms are performed based on extracted feature variables to analyze their influence on travel patterns. The experiments demonstrate the advantages of the proposed method framework.