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Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning

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  • Jiayu Qin

    (School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100191, China)

  • Gang Mei

    (School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100191, China)

  • Lei Xiao

    (School of Engineering and Technology, China University of Geosciences (Beijing), Beijing 100191, China)

Abstract

Traffic congestion is becoming a critical problem in urban traffic planning. Intelligent transportation systems can help expand the capacity of urban roads to alleviate traffic congestion. As a key concept in intelligent transportation systems, urban traffic networks, especially dynamic traffic networks, can serve as potential solutions for traffic congestion, based on the complex network theory. In this paper, we build a traffic flow network model to investigate traffic congestion problems through taxi GPS trajectories. Moreover, to verify the effectiveness of the traffic flow network, an actual case of identifying the congestion areas is considered. The results indicate that the traffic flow network is reliable. Finally, several key problems related to traffic flow networks are discussed. The proposed traffic flow network can provide a methodological reference for traffic planning, especially to solve traffic congestion problems.

Suggested Citation

  • Jiayu Qin & Gang Mei & Lei Xiao, 2020. "Building the Traffic Flow Network with Taxi GPS Trajectories and Its Application to Identify Urban Congestion Areas for Traffic Planning," Sustainability, MDPI, vol. 13(1), pages 1-18, December.
    • Handle: RePEc:gam:jsusta:v:13:y:2020:i:1:p:266-:d:470478
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    References listed on IDEAS

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    Cited by:

    1. Xueting Zhao & Liwei Hu & Xingzhong Wang & Jiabao Wu, 2022. "Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems," Sustainability, MDPI, vol. 14(24), pages 1-23, December.

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