A Framework to Model Mixed Conventional and Automated Vehicular Traffic: Ameliorating Operations, Safety and Environmental Impacts

该研究计划提出了将联网和自动驾驶车辆（即自动驾驶车辆）集成到传统交通流中的建模工具。 所提出的模型控制不间断流动设施上传统车辆和自动车辆之间的相互作用。目标是让交通分析师使用此类模型来评估下一代交通系统的可持续性。研究计划分为三个阶段。 第一阶段将把先前开发的混合交通流模型扩展为统一的方法，以捕获混合交通中的车辆交互。该方法适用于评估具有不同比例的自动驾驶车辆和传统车辆的高速公路上的交通状况（即延误、安全、排放）。将开发特定的控制算法来模拟混合交通，考虑不同的驾驶操作（例如，自由流动和超车操作车道选择、进出高速公路坡道等）。所开发的方法必须纳入交通运营的评估方法中。因此，必须调整现有的高速公路容量方法，以考虑自动驾驶车辆对混合交通流的影响。下一代交通系统预计将有助于交通发展的可持续性，因此需要定义准确的建模工具，以帮助决策者促进适当的交通管理政策。 研究计划的第二阶段将开发一种方法来评估自动驾驶汽车的三重好处（即减少交通拥堵造成的延误的潜力，提高车辆冲突增加地点的安全性，以及通过减少车辆的温室气体排放来改善环境影响）。这将通过校准自动车辆控制算法的参数来完成，同时考虑混合交通流中自动车辆和传统车辆之间的各种比率。其他项目提供的交通数据将通过可用的智能交通控制器收集的数据进行补充。 在第三阶段，研究团队将通过评估混合交通流对预留车道设施（例如公交车道、HOV车道等）具体情况的影响来验证所开发的模型。 将考虑不同比例的自动驾驶车辆来分析交通运营、安全和环境。因此，需要在逐步发展下一代交通基础设施与等待更长的时间直到大量自动驾驶汽车进入市场之间进行权衡。该计划考虑了当前 EDI 指南，用于招募和培训新兴的自动驾驶汽车 ITS 领域的学生，从而解决当前公共和私营部门专业知识短缺的问题。

This research program proposes modeling tools that integrate connected and autonomous vehicles (i.e., automated vehicles) into conventional traffic streams. The proposed models control the interactions between conventional and automated vehicles on uninterrupted flow facilities. The goal is for such models to be used by transportation analysts to assess the sustainability of the next generation transportation systems. The research program is divided into three stages. The first stage will extend previously developed mixed traffic flow models into a unified methodology to capture vehicle interactions in mixed traffic. This methodology will be suitable to assess traffic conditions (i.e., delay, safety, emissions) on highways with various proportions of automated and conventional vehicles. Specific control algorithms will be developed to model mixed traffic considering different driving maneuvers (e.g., free-flow and passing maneuvers lane selection, access and egress to/from highway ramps, etc.). The developed methodology has to be integrated into the evaluation methods of traffic operations. Thus, the existing highway capacity methodology has to be adjusted to account for the effects of automated vehicles into mixed traffic flows. Next generation transportation systems are expected to contribute to the sustainability of transportation development, hence the need to define accurate modeling tools that can assist decision makers in promoting appropriate transportation management policies. The second stage of the research program will develop a methodology to evaluate the three-fold benefits of automated vehicles (i.e., potential to reduce delay due to traffic congestion, to improve safety at locations with increased vehicular conflicts, and to ameliorate the environmental impact by reducing vehicle's GHG emissions). This will be done by calibrating the parameters of the control algorithms of automated vehicles, while considering various ratios between automated and conventional vehicles in mixed traffic flows. Traffic data available from other projects will be complemented by data collection through the available smart traffic controllers. In the third stage, the research team will validate the developed models by assessing the effects of mixed traffic flows on the specific case of reserved-lanes facilities (e.g., bus lanes, HOV lanes, etc.). Different ratios of automated vehicles will be considered for the analysis of traffic operations, safety and environment. Hence, a trade-off will be identified between gradual development of the next generation transportation infrastructures versus waiting a longer period until a critical mass of automated vehicles are penetrating the market. This program considers the current EDI guidelines for recruiting and training students in the newly emerging ITS area of automated vehicles, therefore addressing the current shortage of expertise in both the public and private sectors.