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A simulation of connected-autonomous vehicles operating in Ann Arbor, Michigan demonstrated small increases in VMT (2 percent) coupled with disproportionate increases in energy usage.

An evaluation of the impacts of CACC (cooperative adaptive cruise control) and other potential connected and autonomous vehicles technologies on the performance of the transportation network and changes to mobility patterns in the Ann Arbor region.

Date Posted
09/21/2017
Identifier
2017-B01171
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Assessing the regional energy impact of connected vehicle deployment

Summary Information

This study used an advanced transportation systems simulation model that included co-simulation of travel behavior and traffic flows, to determine a potential range of VMT (vehicle miles traveled) impacts from Connected and Autonomous Vehicles (CAV), particularly those with Cooperative Adaptive Cruise Control (CACC) capabilities.

The POLARIS framework developed by Argonne National Laboratory (ANL) was used to develop a travel demand model for the Ann Arbor, Michigan area. The model consisted of an activity-based demand model implemented as a series of actions and behaviors that the traveler agents performed during the simulation, and a network model that included individualized routing and traffic simulation based on the kinematic wave theory of traffic flow. The models in the system were updated with local travel surveys and data derived from vehicle traces from the Connected Vehicle Safety Pilot that occurred in Ann Arbor from 2012-2013.

The impact of CACC vehicles was simulated at different market penetration rates (0%, 20%, 50%, 75%, 100%) on a regional scale by adjusting the effective capacity of road links (as CACC enables a shorter following gap) while also varying the traveler Value of Travel Time savings (VOTT).
Energy consumption for the regional model was predicted using the Autonomie vehicle energy use simulation system, also developed at ANL.

Findings

  • Changes in effective capacity increased overall VMT, although only to a small degree, with about 2 percent induced additional VMT for an increase in capacity of 77 percent.
  • Changes in travel time cost, or value of travel time savings, had a significant impact, especially at very low levels of VOTT, increasing VMT by up to 28 percent. Along with the 28 percent increase in VMT there was a 33 percent increase in energy usage, indicating substantial secondary effects and increasing congestion, even when effective capacity was significantly increased.
Results Type
Deployment Locations