Assessing Urban Travel Patterns: An Analysis of Traffic Analysis Zone-Based Mobility Patterns

Abstract

Information and communication technology development has yielded large-scale spatiotemporal datasets, such as mobile phone, automatic collection system, and car-hailing data, which have resulted in new opportunities to investigate urban transportation systems. However, few studies have focused on regional mobility patterns. This study presents a multistep method for exploring traffic analysis zone (TAZ)-based mobility patterns and the corresponding relations with local land use characteristics. Based on a large-scale mobile phone dataset from a major mobile phone operator in Beijing, we applied the K-means clustering algorithm to the hourly aggregated trip data to create clusters of TAZs with similar temporal mobility patterns. Land use characteristics were then derived and correlated with the temporal TAZ-based mobility patterns. Four clusters of TAZs with the similar patterns and intensities of urban activities during given time windows were identified. Land use indicators, such as residence and commercial and business area indicators, were correlated with specific temporal TAZ-based mobility patterns. The proposed multistep method could be applied in other cities to enrich relevant analyses and improve urban design and transportation planning.