Autonomous Electric Vehicles Can Reduce Carbon Emissions and Air Pollution in Cities
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Abstract
Heavy dependence on personal vehicle usage made the transportation sector a major contributor to global climate change and air pollution in cities. In this study, we analyzed autonomous electric vehicles and compared their potential environmental impacts with public transportation options, carpooling, walking, cycling, and various transportation policy applications such as limiting lane-mile increases, and carbon tax. Fractional split multinomial logit and system dynamics modeling approaches are integrated to create a novel hybrid simulation model to process data from 929 metro/micropolitan areas in the U.S. for transportation mode choice behavior. The results show that the adoption of autonomous electric vehicles can reduce greenhouse gas emissions by up to 34% of the total emissions from transportation by 2050. This study has revealed that transportation-related impacts can only be reduced with a paradigm shift in the current practices of today's transportation industry, with disruptive reforms of automation, electrification, and shared transport.
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The authors declare that they have no conflict of interest. This work was supported in part by an award to the University of Central Florida, as part of Grant No. DTRT13-G-UTC51 from the U.S. Department of Transportation’s University Transportation Centers Program.
Keywords
Autonomous electric vehicles, Shared transport, Transportation mode choice, Shared transport, Autonomous electric vehicles, Multimodal Transportation, System Dynamics, Fractional Split Multinomial Logit, Transportation Mode Choice
Fields of Science
0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
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