Estimate of Fuel Consumption and GHG Emission Impact on an Automated Mobility District: Preprint

Yuche Chen, Jeffrey Gonder, Stanley Young, Xuewei Qi

Research output: Contribution to conferencePaper


This study estimates the range of fuel and emissions impact of an automated-vehicle (AV) based transit system that services campus-based developments, termed an automated mobility district (AMD). The study develops a framework to quantify the fuel consumption and greenhouse gas (GHG) emission impacts of a transit system comprised of AVs, taking into consideration average vehicle fleet composition, fuel consumption/GHG emission of vehicles within specific speed bins, and the average occupancy of passenger vehicles and transit vehicles. The framework is exercised using a previous mobility analysis of a personal rapid transit (PRT) system, a system which shares many attributes with envisioned AV-based transit systems. Total fuel consumption and GHG emissions with and without an AMD are estimated, providing a range of potential system impacts on sustainability. The results of a previous case study based of a proposed implementation of PRT on the Kansas State University (KSU) campus in Manhattan, Kansas, serves as the basis to estimate personal miles traveled supplanted by an AMD at varying levels of service. The results show that an AMD has the potential to reduce total system fuel consumption and GHG emissions, but the amount is largely dependent on operating and ridership assumptions. The study points to the need to better understand ride-sharing scenarios and calls for future research on sustainability benefits of an AMD system at both vehicle and system levels.
Original languageAmerican English
Number of pages10
StatePublished - 2015
Event4th International Conference on Connected Vehicles & Expo (ICCVE 2015) - Shenzhen, China
Duration: 19 Oct 201523 Oct 2015


Conference4th International Conference on Connected Vehicles & Expo (ICCVE 2015)
CityShenzhen, China

NREL Publication Number

  • NREL/CP-5400-65257


  • automated vehicles
  • energy analysis
  • mobility district


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