Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

Ard, Tyler; Dollar, Robert Austin; Vahidi, Ardalan; Zhang, Yaozhong; Karbowski, Dominik

doi:10.1016/j.trc.2020.102806

Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

Journal Article · Sat Nov 21 00:00:00 EST 2020 · Transportation Research Part C: Emerging Technologies

DOI:https://doi.org/10.1016/j.trc.2020.102806· OSTI ID:1841437

^[1]; ^[1]; ^[1]; Zhang, Yaozhong ^[2]; Karbowski, Dominik ^[2]

Clemson Univ., SC (United States)
Argonne National Lab. (ANL), Lemont, IL (United States)

In this paper we study the energy and traffic impact of a proposed Anticipative Cruise Controller in a PTV VISSIM microsimulation environment. We dissect our controller into two parts: 1. the unconnected mode, active when following a human-driven vehicle, and 2. the connected mode, active when following another automated vehicle equipped with connectivity. Probabilistic constraints balance safety considerations with inter-vehicle compactness, and vehicle constraints for acceleration capabilities are expressed through the use of powertrain maps. Emergent highway traffic scenarios are then modeled using time headway distributions from empirical traffic data. To study the impact of automation over a range of demands of free-flow to stop-and-go, we vary vehicle flux from low to high and vary automated vehicle penetration from low to high. When examining all-human driving scenarios, network capacity failed to meet demand in high-volume scenarios, such as rush-hour traffic. We further find that with connected automated vehicles introduced, network capacity was improved to support the high-volume scenarios. Finally, we examine energy efficiencies of the fleet for conventional, electric, and hybrid vehicles. We find that automated vehicles perform at a 10%-20% higher energy efficiency over human drivers when considering conventional powertrains, and find that automated vehicles perform at a 3%-9% higher energy efficiency over human drivers when considering electric and hybrid powertrains. Due to secondary effects of smoothing traffic flow and reducing unnecessary braking, energy benefits also apply to human-driven vehicles that interact with automated ones. Such simulated humans were found to drive up to 10% more energy-efficiently than they did in the baseline all-human scenario.

View Accepted Manuscript (DOE)

Research Organization:: Argonne National Laboratory (ANL), Lemont, IL (United States); Clemson Univ., SC (United States)

Sponsoring Organization:: USDOE; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Transportation Office. Vehicle Technologies Office

Grant/Contract Number:: AC02-06CH11357; EE0008232

OSTI ID:: 1841437

Alternate ID(s):: OSTI ID: 1863129
OSTI ID: 1778551

Journal Information:: Transportation Research Part C: Emerging Technologies, Journal Name: Transportation Research Part C: Emerging Technologies Vol. 120; ISSN 0968-090X

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Microsimulation of Energy and Flow Effects from Optimal Automated Driving in Mixed Traffic

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