Traffic flow guidance algorithm in intelligent transportation systems considering the effect of non-floating vehicle

  • Yu-Feng Chen
  • Zhan Gao
  • Hong ZhouEmail author
  • Yan Wang
  • Tao Zhang
  • Kai Che
  • Zheng-Tao Xiang


Based on the hypothesis that all vehicles on roads are floating vehicles equipped with vehicular terminals and upload their traffic information to the traffic control center in real time, many traffic flow guidance algorithms are proposed in order to improve the road capacity in Intelligent Transportation System. Based on this complete information scenario, the traffic control center can obtain all traffic information on roads and compute the guidance metrics according to the traffic flow guidance algorithm. The vehicle therefore can select the route with the guidance. However, due to the cost and technology limits, in short time, some vehicles cannot be equipped with vehicular terminals, which are non-floating vehicles and cannot upload their traffic information to the traffic control center. In this incomplete information scenario, the guidance effect will be affected by non-floating vehicles. In this paper, the method of estimating non-floating vehicles’ driving information according to floating vehicles’ information is introduced. With the estimation method, a new traffic flow guidance algorithm, Estimated Weighted Vehicle Density Feedback Strategy based on Weighted Vehicle Density Feedback Strategy (WVDFS) is proposed. In the simulation, the guidance effect of the new algorithm is performed based on the two-route scenario, which is the simplified model of traffic network. NaSch model is also used as the vehicle mobility model, which can simulate the vehicle motion. The simulation results show that non-floating vehicles have great negative influence on the performance of WVDFS, and the applicability of our algorithm is validated to have better performance in incomplete information scenarios. Our algorithm can provide solutions for current traffic flow guidance with incomplete information scenarios.


Traffic flow guidance algorithm Traffic flow Cellular automaton model Estimated weighted vehicle density feedback strategy 



This work was financially supported by Local Science and Technology Development Project Guided by Central Government (Grant No. 2018ZYYD007), CERNET Innovation Project (Grant No. NGII20180615), Natural Science Foundation of Hubei Province (Grant No. 2013CFA054).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

It was obtained from all individual participants included in the study.


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Yu-Feng Chen
    • 1
  • Zhan Gao
    • 1
  • Hong Zhou
    • 2
    Email author
  • Yan Wang
    • 3
  • Tao Zhang
    • 1
  • Kai Che
    • 1
  • Zheng-Tao Xiang
    • 1
  1. 1.School of Electrical and Information EngineeringHubei University of Automotive TechnologyShiyanChina
  2. 2.JiangSu Communications Holding Co. LtdNanjingChina
  3. 3.Highway Monitoring and Response CentreMinistry of Transport of the P.R.CBeijingChina

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