Refining Cellular Data for Accurate Trip Chain Identification: A Novel Approach for Urban Travel Analysis

Cellular data play a crucial role in supporting travel demand assessment and urban traffic planning because of their cost-effectiveness and extensive coverage. However, inherent inaccuracies, such as imprecise positioning, data duplication, and abnormal communication frequencies, hinder their ability to depict travelers’ trips accurately. In this paper, we present a novel approach to mitigate these issues by employing base station mapping to refine positioning data, eliminating duplicates and abnormal frequency records. This refinement enables more effective utilization of cellular data for trip chain identification. We address the ping-pong handover effect by employing a finite automaton machine and an approximate nearest neighbor searching method with carefully selected seeds to identify activities. Our method’s accuracy is validated through ground truth value analysis, focusing on permanent resident population estimation and metro passenger flow estimation. Furthermore, we demonstrate the practical effectiveness and value of our proposed method through a series of representative applications in a real-world case study in the city of Nanning, Guangxi Province, China.