Modeling the potential impacts of automated vehicles on pollutant emissions under different scenarios of a test track

Abstract

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.

Environmental Impact Assessment of Autonomous Transportation Systems

“…Biramo et al examined the impact of the degree of automation levels on the A-EVs emissions [32]. According to the findings of this study, fully automated vehicles can emit 38% less carbon monoxide, 17% less carbon dioxide, 36% less hydrocarbons, and 28% less particulate matter emissions [32]. Massar et al discussed the influence of automation levels on greenhouse gas emissions of A-EVs [33].…”

“…While A-EVs have attracted significant scientific attention, numerous studies have also been conducted to assess the environmental sustainability of A-EVs as well [11,[29][30][31][32][33]. Vahidi et al studied the energy-saving potential of A-EV technology [29].…”

“…Cox et al conducted an LCA of A-EV at a different operational level by considering changes to driving patterns by applying exponential smoothing of the driving cycle [31]. Biramo et al examined the impact of the degree of automation levels on the A-EVs emissions [32]. According to the findings of this study, fully automated vehicles can emit 38% less carbon monoxide, 17% less carbon dioxide, 36% less hydrocarbons, and 28% less particulate matter emissions [32].…”

Assessment of urban greenhouse gas emissions towards reduction planning and low-carbon city: a case study of Montreal, Canada

“…The rising greenhouse gas (GHG) emissions result in the change of radiative pattern in atmosphere, which would increase the average surface temperature and eventually lead to the changing global climate (Opio et al 2023 ). GHGs can be produced from a broad range of anthropogenic activities at different spatial and temporal scales (Biramo and Mekonnen 2022 ; Chen et al 2020 ). In particular, emissions from urban areas are an import source of GHGs (Tian et al 2020 ; Yu et al 2017 ).…”

Change in the concentration of pollutants in the air over the city of Lucknow, together with HYSPLIT4.0's trajectory and dispersion analysis

“…The capital of Uttar Pradesh is Lucknow Megacity, which has a population of 2.82 million according to 2011 data. Its area is 310 km 2 , and its coordinates are 26° 52' N latitude to 80 ° 56' E longitude, or 128 cadence above the ocean. Urban development has made the transport sector the focus of attention over the past 10 years, along with energy.…”