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Journal Article

Citation

Zhao P, Hu H. Cities 2019; 92: 164-174.

Copyright

(Copyright © 2019, Elsevier Publishing)

DOI

10.1016/j.cities.2019.03.022

PMID

unavailable

Abstract

Traffic congestion is one of the key issues relating to sustainability and livability in many large cities. In particular, the situation in the growing megacities of developing countries has been worsening and is now attracting considerable attention from researchers and politicians. An understanding of the spatio-temporal patterns of this congestion is necessary in order to formulate effective policies to relieve it. Much of the research to date has focused on single districts for relatively short periods (days or weeks) using GPS, while long-term analysis of spatial and temporal patterns of traffic congestion at the city level has been rare. The aim of this paper is to help fill this gap in the literature by applying a big data analytic approach to a sample of 10.16 million records of traffic congestion indexes for 233 roads in the Beijing area over a six-month period. This analysis revealed four typical traffic congestion patterns in Beijing, which can be described as the weekend mode, holiday mode, weekday mode A, and weekday mode B. Each of these patterns possesses unique spatial and temporal characteristics. Compared with working days, on which congestion is regular and agglomerated, weekends and holidays are characterized by long-lasting congestion peaks throughout the day. Non-commuting travel on weekends and holidays, including trips for tourism, shopping, entertainment, and children's after-school activities, are major contributors to traffic congestion of the weekend and holiday mode. Owing to poor jobs-housing balance, the suburban new towns and job centres had relatively higher congestion than other areas. These findings shed significant light on geographical patterns of traffic congestion in growing megacities.


Language: en

Keywords

Beijing; Big data analytics; Geographical pattern; Traffic congestion; Traffic congestion index

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