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Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data

Author

Listed:
  • Yang Liu

    (MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Xuedong Yan

    (MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Yun Wang

    (MOE Key Laboratory for Urban Transportation Complex System Theory and Technology, School of Traffic and Transportation, Beijing Jiaotong University, Beijing 100044, China)

  • Zhuo Yang

    (Department of Civil, Environmental, and Infrastructure Engineering, Volgenau School of Engineering, George Mason University, Fairfax, VA 22030, USA)

  • Jiawei Wu

    (Center for Advanced Transportation System Simulation, Department of Civil Environment Construction Engineering, University of Central Florida, Orlando, FL 32816, USA)

Abstract

Traffic congestion is one of the most serious problems that impact urban transportation efficiency, especially in big cities. Identifying traffic congestion locations and occurring patterns is a prerequisite for urban transportation managers in order to take proper countermeasures for mitigating traffic congestion. In this study, the historical GPS sensing data of about 12,000 taxi floating cars in Beijing were used for pattern analyses of recurrent traffic congestion based on the grid mapping method. Through the use of ArcGIS software, 2D and 3D maps of the road network congestion were generated for traffic congestion pattern visualization. The study results showed that three types of traffic congestion patterns were identified, namely: point type, stemming from insufficient capacities at the nodes of the road network; line type, caused by high traffic demand or bottleneck issues in the road segments; and region type, resulting from multiple high-demand expressways merging and connecting to each other. The study illustrated that the proposed method would be effective for discovering traffic congestion locations and patterns and helpful for decision makers to take corresponding traffic engineering countermeasures in order to relieve the urban traffic congestion issues.

Suggested Citation

  • Yang Liu & Xuedong Yan & Yun Wang & Zhuo Yang & Jiawei Wu, 2017. "Grid Mapping for Spatial Pattern Analyses of Recurrent Urban Traffic Congestion Based on Taxi GPS Sensing Data," Sustainability, MDPI, vol. 9(4), pages 1-15, March.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:4:p:533-:d:94633
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    References listed on IDEAS

    as
    1. I Talmor & D Mahalel, 2007. "Signal design for an isolated intersection during congestion," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 58(4), pages 454-466, April.
    2. repec:ipt:iptwpa:jrc47967 is not listed on IDEAS
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    Cited by:

    1. Kan, Zihan & Kwan, Mei-Po & Liu, Dong & Tang, Luliang & Chen, Yang & Fang, Mengyuan, 2022. "Assessing individual activity-related exposures to traffic congestion using GPS trajectory data," Journal of Transport Geography, Elsevier, vol. 98(C).
    2. Yu, Yi & Cui, Yanlei & Zeng, Jiaqi & He, Chunguang & Wang, Dianhai, 2022. "Identifying traffic clusters in urban networks based on graph theory using license plate recognition data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 591(C).
    3. Jinhua Tan & Li Gong & Xuqian Qin, 2019. "Global Optimality under Internet of Vehicles: Strategy to Improve Traffic Safety and Reduce Energy Dissipation," Sustainability, MDPI, vol. 11(17), pages 1-16, August.
    4. Kyu Soo Chong, 2023. "Classification of Speed Change and Unstable Flow Segments Using Geohash-Encoded Vehicle Big Data," Sustainability, MDPI, vol. 15(20), pages 1-16, October.
    5. Wang, Chun & Zhang, Weihua & Wu, Cong & Hu, Heng & Ding, Heng & Zhu, Wenjia, 2022. "A traffic state recognition model based on feature map and deep learning," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    6. 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.
    7. Tanzina Afrin & Nita Yodo, 2020. "A Survey of Road Traffic Congestion Measures towards a Sustainable and Resilient Transportation System," Sustainability, MDPI, vol. 12(11), pages 1-23, June.

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