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Cellular automaton model for three-lane urban road considering Internet of Vehicles lane

Author

Listed:
  • Han-Tao Zhao

    (School of Automotive Engineering, Harbin Institute of Technology, Weihai 264209, Shandong, China)

  • Xin Zhao

    (School of Transportation Science and Engineering, Harbin Institute of Technology, Heilongjiang 150090, China)

  • Liu-Yan Xin

    (Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China)

Abstract

Although the technology of Internet of Vehicles (IoV) is developing rapidly, it will still take a long time to realize its overall popularization. Aimed at this transition phase, this paper proposes to set up an IoV lane on the urban road, which is specially designed for the connected vehicles, to provide a better driving environment for the connected vehicles. Considering the operation characteristics of traffic flow under traditional and IoV environment, this paper establishes a three-lane cellular automata model for urban road traffic flow considering IoV lane, modified on the basis of the Modified Comfortable Driving (MCD) model and symmetric two-lane cellular automata (STCA) model, and then takes simulation by MATLAB and makes analysis. The result shows that the setting of IoV lane can improve the velocity of networked vehicles to a great extent with no or just a bit decline in the ordinary vehicles’ speed, and it has a great effect on the mixture traffic flow, including the increase in both traffic volume and the average speed. What’s more, when the networking proportion is between 0.2 and 0.76, and the space occupation ranges from 0.18 to 0.56, the traffic benefit of IoV special lane can reach the best.

Suggested Citation

  • Han-Tao Zhao & Xin Zhao & Liu-Yan Xin, 2020. "Cellular automaton model for three-lane urban road considering Internet of Vehicles lane," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 31(12), pages 1-24, December.
    • Handle: RePEc:wsi:ijmpcx:v:31:y:2020:i:12:n:s012918312050179x
    DOI: 10.1142/S012918312050179X
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    Citations

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    Cited by:

    1. Mohd Anjum & Sana Shahab, 2023. "Emergency Vehicle Driving Assistance System Using Recurrent Neural Network with Navigational Data Processing Method," Sustainability, MDPI, vol. 15(4), pages 1-19, February.
    2. Huang, Wencheng & Zhou, Bowen & Yu, Yaocheng & Yin, Dezhi, 2021. "Vulnerability analysis of road network for dangerous goods transportation considering intentional attack: Based on Cellular Automata," Reliability Engineering and System Safety, Elsevier, vol. 214(C).
    3. Liu, Bo & Zhang, Geng, 2021. "A double velocity control method for a discrete-time cooperative driving system with varying time-delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 565(C).

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