Active lane management and control using connected and automated vehicles in a mixed traffic environment. (June 2022)

• Record Type: Journal Article
• Authors: Khattak, Zulqarnain H.; Smith, Brian L.; Fontaine, Michael D.; Ma, Jiaqi; Khattak, Asad J.
• Is Part Of: Transportation research. Volume 139(2022)
• Publication Date: 2022-06
• Subjects: CAV-based traffic management -- Connected and automated vehicles -- Autonomie® -- Lane control -- Cooperative adaptive cruise control -- Cooperative merging -- Mixed autonomy -- Volatility -- Safety critical -- Energy; Transportation -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.011
• DOI: 10.1016/j.trc.2022.103648
• Languages: English
• ISSNs: 0968-090X

Abstract

Highlights: Developed a prototype lane management and control application for mixed autonomy operation of automated and human driven vehicles. The novel application counter the driver behavior-based limitations of traditional lane management strategies and is the future of traffic management. Operational and safety benefits in terms of freeway throughput and conflicts in mixed traffic were analyzed. Utilized powertrain drive cycles from Autonomie® model to assess the energy and fuel efficiency benefits under mixed autonomy operation. Abstract: Traditionally, active traffic management systems (ATM) have included lane management systems (LMS) mounted on overhead gantries to provide merge advisories to human drivers when lanes are closed due to incidents or work zones. Advancements in connected and automated vehicles (CAVs) provide opportunities for developing applications to send advisory messages directly to CAVs, which provides an efficient way of managing traffic. This research develops an enhanced CAV based lane management and control application to overcome the limitations of infrastructure-based systems and improve operations, fuel efficiency, and safety in a mixed autonomy operation of CAVs and human-driven vehicles. Impacts of this approach on operations, fuel efficiency, and safety are compared to traditional LMS using a calibrated simulation model of a site where traditional LMS is present. High fidelity simulation runs of drive cycles were conducted using the Autonomie® model to estimate fuel efficiency effects. The simulation results showed that starting at a lower penetration of 20% CAVs, an average throughput improvement of 3.5% was observed, increasing to a maximum of 14.2% for full penetration of CAVs. The drive cycle simulations revealed a reduction of 47% to 60% in fuel consumption with the CAV based application. Both lane change and rear-end conflicts were also observed to reduce. Likewise, volatility represented by acceleration-deceleration variation, was reduced by an average of 18.2% and 17.6% under varying penetrations of CAVs. Volatility represents variation in driving regimes over an instantaneous period of driving, so this reduction in volatility serves as a positive surrogate indicator of safety and fuel efficiency for this new application as well.