CAV条件下道路交通拥堵和排放的仿真评估方法的改进研究

Connected and Autonomous Vehicle (CAV) has become an inevitable trend of road traffic. Besides the consensus on pure CAV traffic, it is not yet completely clear how traffic operation and vehicle emission is affected by CAVs in mixed traffic containing both CAVs and human-driven vehicles, which expects to last for a relatively long transitional period. This will be a great challenge for the future traffic management. In view of the above problem, this project plans to collect data of driving behaviors and vehicle trajectories by field tests and driving simulator, and investigate the impact and the mechanism of different level CAV on individual vehicle, platoon, and traffic flow. The effects on acceleration, car following, lane changing by different CAV, penetration rate, compliance rate, platooning and clustering will be characterized, and the advancing methods of time and random event based on the above distribution regularities will be analyzed. In particularly, the change of dynamic capacity, breakdown probability, congestion propagation and dissipation, and VSP distribution will be investigated for heterogeneous CAV traffic flow, and the corresponding characterizing model and vehicle emission model will be developed and coupled. The improvement on the simulation and evaluation method will be conducted for traffic congestion and emission analysis based on the above studies. The findings of this project are expected to be helpful for decision-making of traffic management for the future CAV traffic.

自动驾驶和网联车（CAV）已成为道路交通发展的必然趋势。尽管对纯CAV场景下的交通运行和排放改善有共识，但对将长期存在的过渡期的（人工驾驶和各等级CAV）混合交通流特征仍未得到清晰的认识，这对未来交通管理带来严峻挑战。针对该问题，项目将通过实地和模拟器实验，采集各类CAV条件下的单车和多车驾驶行为轨迹数据，剖析各等级CAV技术对车辆行为、车队行为、交通流状态的递进影响机理，研究CAV等级、渗透率、服从率、车队集群等条件下加减速、跟驰、换道的参数影响及其多态分布，建立异质交通流多态分布下的离散事件仿真推进方法，重点刻画异质混合交通流对动态通行能力、Breakdown概率、拥堵蔓延消散和机动车比功率（VSP）分布的影响，面向仿真建立交通拥堵和排放模型及其耦合分析方法，进而建立CAV条件下针对交通拥堵和排放仿真评估的改进方法。预期成果有助于CAV条件下的交通管理决策。

自动驾驶和网联车（CAV）已成为道路交通发展的必然趋势。尽管对纯CAV场景下的交通运行和排放改善有共识，但对将长期存在的过渡期的（人工驾驶和各等级CAV）混合交通流特征仍未得到清晰的认识，这对未来交通管理带来严峻挑战。针对该问题，本项目通过进行实地和模拟器实验，采集了各类CAV条件下的单车和多车驾驶行为轨迹数据，分析了各等级CAV技术对车辆行为、车队行为、交通流状态的递进影响机理，对CAV等级、渗透率、服从率、车队集群等条件下加减速、跟驰、换道的参数影响及其多态分布进行具体研究，建立了异质交通流多态分布下的离散事件仿真推进方法，重点刻画异质混合交通流对动态通行能力、Breakdown概率、拥堵蔓延消散和机动车比功率（VSP）分布的影响，研究了CAV条件下交通拥堵的网络化蔓延消散模型，确立了基于行驶轨迹的排放热点污染物量化方法和基于行驶轨迹的信号控制交叉口排放时空分布模型，分别展开了面向不同时间力度轨迹数据的驾驶行为图谱库构建，面向驾驶员的生态驾驶评估模型的构建，生态驾驶行为评级方法及诱导策略设计研究，并面向仿真提出了较为完善的交通拥堵和排放模型及其耦合分析策略，进而建立CAV条件下针对交通拥堵和排放仿真评估的改进方法。.本项目成果有助于进行CAV条件下的交通管理决策，缓解交通拥堵，减少交通能耗排放。