One of the significant sources of air pollution and greenhouse gas emissions is the road transportation sector. These emissions are worsened by driving behaviors and network conditions. It is common knowledge that experienced and inexperienced drivers behave differently when operating vehicles. Given the same vehicle in a different timeframe, the drivers’ reactions to similar situations vary, which has a significant influence on the emissions and fuel consumption as their use of acceleration and speed differ. Because the driving patterns of automated vehicles are programmable and provide a platform for smooth driving situations, it is predicted that deploying them might potentially reduce fuel consumption, particularly in urban areas with given traffic situations. This study’s goal is to examine how different degrees of automated vehicles behave when it comes to emissions and how accelerations affect that behavior. Furthermore, the total aggregated emissions on the synthesized urban network are evaluated and compared to legacy vehicles. The emission measuring model is based on the Handbook Emission Factors for Road Transport (HBEFA)3 and is utilized with the Simulation of Urban Mobility (SUMO) microscopic simulation software. The results demonstrate that acceleration value is strongly correlated with individual vehicle emissions. Although the ability of automated vehicles (AVs) to swiftly achieve higher acceleration values has an adverse effect on emissions reduction, it was compensated by the rate of accelerations, which decreases as the automation level increases. According to the simulation results, automated vehicles can reduce carbon monoxide (CO) emissions by 38.56%, carbon dioxide (CO2) emissions by 17.09%, hydrocarbons (HC) emissions by 36.3%, particulate matter (PMx) emissions by 28.12%, nitrogen oxides (NOx) emissions by 19.78% in the most optimistic scenario (that is, when all vehicles are replaced by the upper bound automated vehicles) in the network level.

中文翻译：

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在测试跑道的不同场景下模拟自动驾驶车辆对污染物排放的潜在影响

空气污染和温室气体排放的重要来源之一是公路运输部门。这些排放因驾驶行为和网络状况而恶化。众所周知，经验丰富和缺乏经验的驾驶员在驾驶车辆时的行为不同。同一辆车在不同的时间范围内，驾驶员对类似情况的反应各不相同，这对排放和燃料消耗有重大影响，因为他们对加速度和速度的使用不同。由于自动驾驶汽车的驾驶模式是可编程的，并为平稳驾驶情况提供了平台，因此预计部署它们可能会降低燃料消耗，特别是在交通状况特定的城市地区。本研究的目标是研究不同程度的自动化车辆在排放方面的表现，以及加速如何影响这种行为。此外，对综合城市网络的总排放量进行了评估，并与传统车辆进行了比较。排放测量模型基于道路交通排放因子手册 (HBEFA)3，并与城市交通模拟 (SUMO) 微观模拟软件一起使用。结果表明，加速度值与单个车辆的排放量密切相关。尽管自动驾驶车辆 (AV) 能够快速实现更高加速度值的能力对减排有不利影响，但它可以通过加速度补偿，加速度会随着自动化水平的提高而降低。根据仿真结果，