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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(2): 184-194
WU Bing, WANG Wen-xuan, LI Lin-bo, LIU Yan-ting. Longitudinal control model for connected autonomous vehicles influenced by multiple preceding vehicles[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 184-194. doi: 10.19818/j.cnki.1671-1637.2020.02.015
Citation: WU Bing, WANG Wen-xuan, LI Lin-bo, LIU Yan-ting. Longitudinal control model for connected autonomous vehicles influenced by multiple preceding vehicles[J]. Journal of Traffic and Transportation Engineering, 2020, 20(2): 184-194. doi: 10.19818/j.cnki.1671-1637.2020.02.015
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Longitudinal control model for connected autonomous vehicles influenced by multiple preceding vehicles

doi: 10.19818/j.cnki.1671-1637.2020.02.015

  • Key Laboratory of Road and Traffic Engineering of Ministry of Education, Tongji University, Shanghai 201804, China

Funds:

National Key Research and Development Project of China 2019YFB1600703

More Information
  • Author Bio:

    WU Bing(1960-), male, professor, PhD, E-mail: wubing@tongji.edu.cn

  • Corresponding author: WU Bing(1960-), male, professor, PhD, E-mail: wubing@tongji.edu.cn
  • Received Date: 2019-09-19
  • Publish Date: 2020-04-25
    Abstract
  • In order to better simulate the car-following characteristics of connected autonomous vehicles(CAV), based on the longitudinal control model(LCM), the LCM in the connected autonomous environment(C-LCM) was constructed considering the influences of speed and acceleration of multiple preceding vehicles in V2 V environment. The stabilities of LCM and C-LCM were analyzed. The stability regions of two models were compared, and the influence of C-LCM on the traffic flow stable region under different communication distances was determined. Numerical simulation was designed to simulate the common traffic scenarios including acceleration and deceleration, and the car-following behavior characteristics of CAV in V2 V environment were analyzed. The traffic flow safety levels with different communication distances and penetration rates of CAV were analyzed with simulation. A fundamental diagram model of mixed traffic flows with different penetration rates of CAV was constructed. Analysis result shows that the traffic flow stability region increases with the increase of considered preceding vehicles numbers, and when only considering one preceding vehicle, the longer the distance between the preceding vehicle and ego vehicle, the bigger the influence of velocity coefficient on the C-LCM stability region. The C-LCM can respond to the behaviors of multiple preceding vehicles in advance and simulate the dynamics characteristics of connected autonomous vehicles better. In the deceleration scenario, the speed overshoot decreases from 0.15 to 0.08, and the maximum speed delay decreases from 7.5 s to 4.9 s. In the acceleration scenario, the speed overshoot decreases from 0.07 to 0.04 and the minimum speed delay decreases from 3.5 s to 2.6 s. With the increase of CAV penetration rate, the safety level of traffic flow increases. The highest safety level is achieved with four CAVs in communication distance, and the TIT and TET indexes decrease by 57.22% and 59.08%, respectively. With the increase of CAV penetration rate, the highway capacity increases from 1 281 veh·h-1 to 3 204 veh·h-1. So the proposed C-LCM can describe the car-following characteristics of different vehicles to achieve the modeling of mixed traffic flow, decrease the complexity of mixed traffic flow, and provide a reference for the impact analysis of CAV on traffic flow.

     

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