Integrating vehicle trajectory planning and arterial traffic management to facilitate eco-approach and departure deployment

Abstract

Eco-approach and departure (EAD) enable continuous vehicle motion in urban signalized corridors. Since such a motion can extend to the EAD vehicles’ followers, it makes EAD a promising technology to benefit the traffic flow where automated vehicles and conventional vehicles coexist. Most existing EAD studies envision an ideal setting that neglects real-world operational conditions such as lane changes, multi-movement intersection configuration, partially automated fleet, and/or limited traffic state awareness. This study aims to fill the gap by designing an EAD algorithm considering real-world traffic operation constraints. The proposed algorithm uses a model predictive controller to minimize vehicle speed reduction and variation based on the real-time traffic signal control plan and measured queues at the intersection. The required inputs are readily available at many modern intersections. We observed that the proposed controller’s performance might degrade because of lane-changing maneuvers and lead-left turn traffic signals. These observations motivated our development of a lane change management strategy and a signal control implementation strategy to facilitate the EAD implementation. The lane change management strategies separate the EAD operations and lane-changing maneuvers in time and space. The signal control implementation strategy applies lag-left turn signals to enable EAD operation for both the through and left-turn vehicles. Compared to the non-EAD case, our EAD approach produces 2.5% to 7.8% energy savings while keeping similar intersection mobility. Notably, this approach brings about 2.5% to 3.6% energy savings in a 2% CAV case. This result demonstrates the feasibility of deploying EAD at low connected automated vehicle penetration rates.

Keywords:

• Arterial corridor management
• connected automated vehicle
• cooperative driving automation
• eco-approach departure

Acknowledgment

This paper and the work described were sponsored by the U.S. Department of Energy (DOE) Vehicle Technologies Office (VTO) under the Systems and Modeling for Accelerated Research in Transportation (SMART) Mobility Laboratory Consortium, an initiative of the Energy Efficient Mobility Systems (EEMS) Program. The presented work was the outcome of the Cooperative Driving Automation (CDA) Project managed by Dr. Erin Boyd. The authors gratefully acknowledge her for establishing the project concept, advancing implementation, and providing ongoing guidance.

Disclosure statement

No potential conflict of interest was reported by the author(s).