Volume 40 Issue 5
Nov.  2022
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Article Navigation >  Journal of Transport Information and Safety  > 2022  >  40(5): 44-52
HAO Wei, XIAO Lei, ZHANG Zhaolei, ZHENG Nan. A Reliability Analysis of the Capacity of Urban Road Network Under a Mixed Human-driven and Connected Traffic Environment[J]. Journal of Transport Information and Safety, 2022, 40(5): 44-52. doi: 10.3963/j.jssn.1674-4861.2022.05.005
Citation: HAO Wei, XIAO Lei, ZHANG Zhaolei, ZHENG Nan. A Reliability Analysis of the Capacity of Urban Road Network Under a Mixed Human-driven and Connected Traffic Environment[J]. Journal of Transport Information and Safety, 2022, 40(5): 44-52. doi: 10.3963/j.jssn.1674-4861.2022.05.005
shu

A Reliability Analysis of the Capacity of Urban Road Network Under a Mixed Human-driven and Connected Traffic Environment

doi: 10.3963/j.jssn.1674-4861.2022.05.005
  • 1.

    School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China

  • 2.

    Department of Civil Engineering, Monash University, Clayton 3168, Australia

  • Received Date: 2022-05-12
    Available Online: 2022-12-05
    Abstract
  • The emerging of mixed traffic involving both connected autonom ous vehicles(CAVs)and human-driven vehicles(HDVs)may change the capacity of urban road networks. A bi-level programming model is proposed to analyze the impacts of mixed traffic flow on the reliability of the capacity of urban road network in an intelligent network environment. Assuming that CAVs follow the path selected based on the system optimization principle and the drivers of the HDVs select their paths according to their own experience, a lower model is developed for the assignment of traffic flow based on the differences in the path selection between the two types of vehicles. Furthermore, the modeling of the assignment of mixed traffic at the lower level is transformed into a nonlinear complementarity problem to reduce runtime. Considering the randomness of road capacity in a network, an upper model is set up for modeling the reliability of capacity by using a uniform random distribution with multiple correlations. A Monte Carlo simulation is used to analyze the reliability of road network capacity under different market penetration rate(MPR)of CAVs. A sensitivity analysis is then carried out for studying the reliability of road capacity under such a scenario. Numerical results show that, when the level of the demand d > 0.5, the reliability of road network capacity decreases. When level of the demand d > 0.7 and the market penetration rate of CAVs λ=0, the reliability is less than 0.4. However, when d > 0.7 and λ=1, the reliability is found close to 1, indicating that CAVs can enhance the reliability of road network capacity. It is also found that when the level of the demand is between 0.7 and 1, the MPRof CAVs significantly affects the reliability of road network capacity. When the road network is overloaded, the MPR has a very minor impact on the reliability of road network capacity with the increase of traffic demand. In addition, when λ increases from 0 to 1, the number of roads showing"capacity paradox"in the road network decreases from 19 to 3. When λ=1, only one road in the entire network show the issue. Study results show that the increase of MPR can not only reduce the possibility of"road capacity paradox", but also improve the stability of the road network.

     

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