How can the built environment affect the impact of autonomous vehicles’ operational behaviour on air quality?

Abstract

Autonomous vehicles (AVs) are pointed out as the technology that will reshape the concept of mobility, with significant implications for the economy, the environment, and society. This fact will bring new challenge to cities urban planning. Research to anticipate the AVs impacts, maximizing their benefits and reduce trade-offs are currently crucial. This work investigates the potential challenges and benefits of gradually replace internal combustion engine human driven vehicles with different penetration rates of AVs - 10%, 30%, 50%, 70%, 90%, and 100% - in urban roads of different characteristics, either in terms of traffic singularities or volumes, and its related implications on air quality. For that purpose, two urban areas with distinct features, Porto and Aveiro, were selected as case studies, and a modelling setup composed of a traffic model, an emission model, and a local air quality model was applied. The results revealed that the AVs benefits are directly linked with the urban design and the road characteristics. In the Aveiro case study, the AVs promoted positive changes with average reductions in daily NOx emissions (compared with the baseline scenario, without AVs) ranging between −2.1% (for C10%) and −7.7% (for C100%). In line with the emissions impacts, positive effects were found on air quality, with average reductions of NO2 concentrations up to −4% (for C100%). In Porto urban area, slight differences in NOx emissions were obtained (<2%), which implied no changes in the air quality levels. The distinct impact of AVs in the study areas is mostly explained by the traffic light coordination system and directional split distributions in the main roads. These results provide valuable insights to support decision-makers in the definition of strategies that allow the integration of these new emerging technologies in the road infrastructure, considering the features of the urban design, traffic profile and road characteristics.