Using Loop Detector Big Data and Artificial Intelligence to Predict Road Network Congestion

  • Zhi-wei Guan
  • Xi-yao Liu
  • Ling YangEmail author
  • Hong-lin Zhao
  • Xiao-feng Liu
  • Feng Du
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 503)


Understanding the temporal-spatial congestion evolution is important to mitigate traffic congestion and improve traffic efficiency. Most studies used floating car data to analyze the urban congestion, however, its market penetrate is low in many cities, thus the data is not enough in terms of quantity and coverage. Loop detector is the most frequently used sensor, its data has the attributes of long-term and large-scale coverage, and utilizing the loop detector big data is helpful to analyze the congestion evolution. Therefore, this study proposes a data-driven congestion analysis approach, which consists of loop detector data processing, traffic simulation, and artificial intelligence to predict the urban temporal-spatial congestion evolution. A case study in Tianjin, China is conducted, and the case study result shows that the evening peak has more serious traffic congestion than the morning peak, the prediction accuracy of feed forward back-propagation neural network (BPNN) increases with the time interval aggregation level increasing, and the prediction accuracy is 85.7% with 30 min interval aggregation.


Traffic congestion Traffic big data Artificial intelligence Traffic simulation 



This research was supported by the Service Platform of Intelligent Transportation Cooperative Control Technologies (16PTGCCX00150), the National Natural Science Foundation of China (51408417, 61503284), the Key Project of Natural Science Foundation of Tianjin (16JCZDJC38200), the Transportation Science and Technology Development Plan Project of Tianjin (2017A-24), and the Science and Technology Plan Project of Tianjin (17ZXRGGX00070, 17KPXMSF00010).


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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Zhi-wei Guan
    • 1
  • Xi-yao Liu
    • 1
  • Ling Yang
    • 2
    Email author
  • Hong-lin Zhao
    • 1
  • Xiao-feng Liu
    • 1
  • Feng Du
    • 1
  1. 1.School of Automotive and TransportationTianjin University of Technology and EducationTianjinChina
  2. 2.School of Information Technology EngineeringTianjin University of Technology and EducationTianjinChina

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