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Traffic peak period detection using traffic index cloud maps

Author

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  • Li, Yuni
  • Xiao, Jianli

Abstract

Traffic peak period detection is one key issue in ITS research area, which can afford time information for traffic flow guidance. Classical methods devote themselves to detect the peak period of road segmentations and small road network areas. Namely, these methods focus on traffic peak period detection in small space scale. However, the traffic peak periods of road segmentations and small road network areas cannot present the traffic peak periods of the whole city. In fact, the traffic peak periods of the whole city are more important for the traffic administration department. To solve this problem, a new method for detecting traffic peak periods of the whole city is proposed, which is based on the traffic index cloud maps. Experimental results on the GPS data show that the proposed method can recognize the traffic peak periods in a large space scale accurately.

Suggested Citation

  • Li, Yuni & Xiao, Jianli, 2020. "Traffic peak period detection using traffic index cloud maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 553(C).
    • Handle: RePEc:eee:phsmap:v:553:y:2020:i:c:s0378437120300790
    DOI: 10.1016/j.physa.2020.124277
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    References listed on IDEAS

    as
    1. Xiao, Jianli, 2019. "SVM and KNN ensemble learning for traffic incident detection," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 517(C), pages 29-35.
    2. Nagatani, Takashi, 2016. "Effect of stopover on motion of two competing elevators in peak traffic," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 613-621.
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    Cited by:

    1. Lentzakis, Antonis F. & Seshadri, Ravi & Ben-Akiva, Moshe, 2023. "Predictive distance-based road pricing — Designing tolling zones through unsupervised learning," Transportation Research Part A: Policy and Practice, Elsevier, vol. 170(C).
    2. Ostovar, Maryam & Butt, Ali A. & Harvey, John T. & Ramalingam, Zachary T. & Hernandez, Jesus & Kendall, Alissa, 2022. "Case Studies of Socio-Economic and Environmental Life Cycle Assessment of Complete Streets," Institute of Transportation Studies, Working Paper Series qt4n4081k8, Institute of Transportation Studies, UC Davis.

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