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A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles

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  • Jiawen Wang
  • Shaobo Li
  • Yining Lu
  • Lubang Wang

Abstract

Using a cellular automaton model, this paper studied the evolution mechanism of traffic incidents affecting the capacity of urban expressway under the mixed traffic environment of manual driving and automatic driving. It showed that the length of the automated-driving early-warning zone could affect the capacity of expressway. Specifically, the early-warning zone is divided into an accelerate lane-changing area, a decelerate lane-changing area, and a forced lane-changing area. The areas vary according to the distance between the vehicle and the location of incident. Based on the study, this paper establishes a codirectional two-lane cellular automaton model. The analysis showed that the capacity of the urban expressway varies under different combinations of early-warning area length and division ratio of early-warning zone. In the case of two-lane reduction caused by traffic incidents, the capacity of the expressway is optimized when the length of early-warning zone is between 450 and 600 m, and the ratio of accelerate zone, decelerate zone, and forced zone to the length of early-warning zone is, respectively, 75%, 10%, and 15%. In addition, this study showed that the capacity will rise with the increase in automated vehicles.

Suggested Citation

  • Jiawen Wang & Shaobo Li & Yining Lu & Lubang Wang, 2020. "A Division Method of Determining the Early-Warning Zone on an Expressway for Automated Vehicles," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-10, April.
    • Handle: RePEc:hin:jnddns:9523819
    DOI: 10.1155/2020/9523819
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