An Eco-Driving Approach With Flow Uncertainty Tolerance for Connected Vehicles Against Waiting Queue Dynamics on Arterial Roads

Abstract

Eco-driving incorporating multiple signalized intersections simultaneously has been proven to substantially benefit connected vehicles (CVs) in energy performance. However, ignoring the dynamic variation of waiting queues before downstream intersections may prevent CVs from following the obtained speed profile on security grounds. In this article, the dynamic variation of the waiting queue is modeled and predicted based on shockwave theory and data-driven-based traffic flow prediction. To formulate the waiting queues as additional time-varying constraints for optimization problems, an extended traffic signal model is constructed based on the prediction. Furthermore, a hierarchical optimization framework is proposed, under which the hybrid optimization problem is decomposed into a discrete problem and a continuous one. Monte Carlo simulation demonstrates that if the proposed eco-driving approach is implemented, failure to follow the reference speed profile decreases by 79.4%. Also, the fuel consumption can be saved by over 4% compared with approaches ignoring the waiting queue.