Quantifying autonomous vehicles national fuel consumption impacts: A data-rich approach

Yuche Chen, Jeffrey Gonder, Stanley Young and Eric Wood

Transportation Research Part A: Policy and Practice, 2019, vol. 122, issue C, 134-145

Abstract: Autonomous vehicles are drawing significant attention from governments, manufacturers and consumers. Experts predict them to be the primary means of transportation by the middle of this century. Recent literature shows that vehicle automation has the potential to alter traffic patterns, vehicle ownership, and land use, which may affect fuel consumption from the transportation sector. In this paper, we developed a data-rich analytical framework to quantify system-wide fuel impacts of automation in the United States by integrating (1) a dynamic vehicle sales, stock, and usage model, (2) an historical transportation network-level vehicle miles traveled (VMT)/vehicle activity database, and (3) estimates of automation’s impacts on fuel efficiency and travel demand. The vehicle model considers dynamics in vehicle fleet turnover and fuel efficiency improvements of conventional and advanced vehicle fleet. The network activity database contains VMT, free-flow speeds, and historical speeds of road links that can help us accurately identify fuel-savings opportunities of automation. Based on the model setup and assumptions, we found that the impacts of automation on fuel consumption are quite wide-ranging—with the potential to reduce fuel consumption by 45% in our “Optimistic” case or increase it by 30% in our “Pessimistic” case. Second, implementing automation on urban roads could potentially result in larger fuel savings compared with highway automation because of the driving features of urban roads. Through scenario analysis, we showed that the proposed framework can be used for refined assessments as better data on vehicle-level fuel efficiency and travel demand impacts of automation become available.

Keywords: Fuel consumption; Autonomous vehicles; Data-rich energy modeling (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (12)

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DOI: 10.1016/j.tra.2017.10.012

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