Mixed traffic flow of human driven vehicles and automated vehicles on dynamic transportation networks
Mixed traffic flow of human driven vehicles and automated vehicles on dynamic transportation networks
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DOI：
10.1016/j.trc.2021.103159
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发表时间：
2021-07
期刊：
影响因子：
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通讯作者：
Qiangqiang Guo;X. Ban;H. M. A. Aziz
中科院分区：
文献类型：
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作者：
Qiangqiang Guo;X. Ban;H. M. A. Aziz
通过动态控制互联和自动驾驶车辆（CAV）的路线来改善交通网络的系统性能是CAV可以为我们的社会带来的诱人利润。考虑到要实现100%的CAV渗透率可能还有很长的路要走，本文讨论了交通网络上人类驾驶车辆（HDV）和CAV的混合交通流。首先，我们提出了一个基于双队列（DQ）的混合交通流模型来描述链路动态以及在路口的流量转换。基于此混合流模型，我们开发了一个动态的双层框架来捕捉HDV和CAV的行为和交互。这导致了一个最优控制问题与平衡约束（OCPEC），其中HDV的路线选择行为的建模在低层次的瞬时动态用户平衡（IDUE）原则和CAV的路线选择建模的动态系统最优（DSO）原则在上层。我们展示了如何将OCPEC离散为一个带平衡约束的数学规划（MPEC），并讨论了它的性质和求解技巧。MPEC的非凸性和非光滑性使其难以有效求解。为了克服这个缺点，我们开发了一种基于分解的启发式模型预测控制（HMPC）方法，通过将原始MPEC问题分解为两个独立的问题：一个IDUE问题HDVs和一个DSO问题CAV。实验结果表明，与所有车辆均为HDV的情况相比，在HDV和CAV混合交通流下，所提方法能显著提高网络性能.
Improving the system performance of a traffic network by dynamically controlling the routes of connected and automated vehicles (CAVs) is an appealing profit that CAVs can bring to our society. Considering that there may be a long way to achieve 100% CAV penetration, we discuss in this paper the mixed traffic flow of human driven vehicles (HDVs) and CAVs on a transportation network. We first propose a double queue (DQ) based mixed traffic flow model to describe the link dynamics as well as the flow transitions at junctions. Based on this mixed flow model, we develop a dynamic bi-level framework to capture the behavior and interaction of HDVs and CAVs. This results in an optimal control problem with equilibrium constraints (OCPEC), where HDVs’ route choice behavior is modeled at the lower level by the instantaneous dynamic user equilibrium (IDUE) principle and the CAVs’ route choice is modelled by the dynamic system optimal (DSO) principle at the upper level. We show how to discretize the OCPEC to a mathematical programming with equilibrium constraints (MPEC) and discuss its properties and solution techniques. The non-convex and non-smooth properties of the MPEC make it hard to be efficiently solved. To overcome this disadvantage, we develop a decomposition based heuristic model predictive control (HMPC) method by decomposing the original MPEC problem into two separate problems: one IDUE problem for HDVs and one DSO problem for CAVs. The experiment results show that, compared with the scenario that all vehicles are HDVs, the proposed methods can significantly improve the network performance under the mixed traffic flow of HDVs and CAVs.