Identifying Temporal and Spatial Characteristics of Residents’ Trips from Cellular Signaling Data: Case Study of Beijing

Abstract

This study developed a method to extract valid temporal and spatial characteristics of residents’ trips based on cellular signaling data to support urban transportation planning activity. The study first identified data triggered by active modes as redundant data by analyzing the characteristics of the trigger modes of cellular signaling data, which were then labeled and excluded from further analysis. Thus, only the data triggered by the passive mode were used in the study. Then, the temporal and spatial characteristics of residents’ trips were extracted by mapping the cellular signaling data onto study regions, dividing them into traffic analysis zones (TAZs), and extracting the origin–destination (OD) matrix. Finally, real data from Beijing, China were used in a case study to verify the feasibility of the proposed method. The extracted temporal and spatial characteristics of residents’ trips were compared with those from the 4th Comprehensive Transport Survey. It was observed that the results from the two data sources had a high correlation with correlation coefficients higher than 0.9.