Microsimulation of energy and flow effects from optimal automated driving in mixed traffic. (November 2020)

• Record Type: Journal Article
• Authors: Ard, Tyler; Dollar, Robert Austin; Vahidi, Ardalan; Zhang, Yaozhong; Karbowski, Dominik
• Is Part Of: Transportation research. Volume 120(2020)
• Publication Date: 2020-11
• Subjects: Traffic microsimulation -- Autonomous vehicles -- Anticipative cruise control -- Energy efficiency -- Model predictive control -- PTV VISSIM; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2020.102806
• Languages: English
• ISSNs: 0968-090X

Abstract

Abstract: This paper studies 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. Graphical abstract: Highlights: Considers conventional, electric, and hybrid vehicle types for energy effects. Introduce probabilistic constraints for safety and traffic flow considerations. Integrates empirical data and high fidelity models for increased realism in simulation. Mixed fleets observe improved energy and flow effects.