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

Tianwen Liang,Yanyan Chen,Zheng Zhang

doi:10.3390/su11195452

Tianwen Liang, Yanyan Chen + Show 1 more

Open Access

https://doi.org/10.3390/su11195452

Copy DOI

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.

Highlights

Understanding urban mobility patterns is important for transportation planning and transportation demand management policy designation
Given that we focused on human mobility pattern profiles within traffic analysis zone (TAZ), if a user was recorded at a mobile phone base station, we mapped the user to the TAZ where the base station was located
Based on millions of trips extracted from mobile phone data, we built TAZ-based temporal mobility pattern profiles and categorized urban areas into four groups

Summary

Introduction

Understanding urban mobility patterns is important for transportation planning and transportation demand management policy designation. Regional-oriented demand strategies may efficiently alleviate traffic congestion To this end, knowledge of regional-based human mobility patterns could contribute toward establishing planning and management policies. Urban mobility patterns are generally based on digital footprints, which have benefited from the development of information and communication technology (ICT). Studies can be largely categorized into three groups from the perspective of the research focus: (i) studies of individual mobility mechanisms from a micro-perspective [4,5,6], (ii) studies of aggregate mobility characteristics [7,8,9,10,11], and (iii) studies of the interactions between mobility patterns and land use characteristics [12,13,14] We review some typical works in the remainder of this section

Methods

Results

Discussion

Conclusion