Statistical Analysis of Connected and Autonomous Vehicles (CAVs) Effects on the Environment in Terms of Pollutants and Fuel Consumption

Abstract

Over the last decades, interest in integrating autonomous and connected technologies in vehicle design in order to improve mobility, safety, and reduce transportation's environmental impact has dramatically increased. The state-of-the-art specified that connected and autonomous vehicles (CAVs) ameliorate traffic mobility, safety, fuel/energy consumption, and reduce environmental pollution. The State of Maryland (MD) in the United States was selected as a case study, and the paper appraised CAVs' fuel consumption and air pollutants (CO, PM, and NOx), and utilized reasonable linear regression models to forecast CAV's environmental effects. The VISUM software was applied to simulate MD transport network as a multi-modal transport network and the required data on a set of variables were collected through an exhaustive survey. The amount of pollutants and fuel consumption were obtained for timestamps 2010 to 2021 from the macro simulation. Eventually, four linear regression models were suggested to predict the amount of CO, NOx, PM pollutants and, fuel consumption. The results demonstrated that CAVs' pollutants and fuel consumption have a significant correlation with income, age, and race of the CAV customers. Moreover, the reliability of four statistical models was compared with the reliability of macro simulation model outputs in year 2030. The error values of three pollutants and fuel consumption were obtained less than 9% by statistical models in SPSS. This research is expected to assist researchers and policymakers with planning decisions to reduce CAV environmental impacts in MD.