MPC
Project Details

Title:	Exploratory Modeling and Analysis for Automated Vehicles in Utah
Principal Investigators:	Xiaoyue Cathy Liu
University:	University of Utah
Status:	Completed
Year:	2017
Grant #:	69A3551747108 (FAST Act)
Project #:	MPC-542
RH Display ID:	151380
Keywords:	forecasting, intelligent vehicles, traffic platooning, travel time, vehicle miles of travel

Abstract

Technological advances are impacting transportation across many dimensions. Private industry is driving one of the major advances, automated vehicles (AVs), with a number of companies ranging from Google to Audi testing cars with full automation (SAE Level 5). The organizations tasked with planning for transportation facilities – DOTs and MPOs – are in a reactive mode in figuring out how best to respond.

AV will impact mobility, congestion, and safety, and their introduction into the vehicle fleet clearly brings a great deal of uncertainty with respect to forecasting travel demand and vehicle operations. AVs will deliver mobility to historically low mobility demographics such as the elderly, disabled, and children. AVs will also reduce the burden of long travel times by enabling passengers to focus on tasks other than driving. Both of these effects suggest that AVs will amplify growth in Vehicle Mile Traveled (VMT) that is already projected to increase due to population growth in Utah. Utah is currently the fastest growing state in the U.S., and this growth will most certainly translate into higher levels of VMT in the future. AVs are likely to reinforce the traditional source of VMT growth from population and economic growth. The timing and magnitude of this VMT-augmentation are not well understood, however.

Project Deliverables

• [Final Report] Exploratory Modeling and Analysis for Automated Vehicles in Utah — MPC-22-452
  • View Research Brief

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