A Dynamic Advisory Speed Limit Algorithm of Eco-Driving Strategies Based on Phase Time Control

The concept of eco-driving is based on environmental factors such as fuel consumption. A large number of existing studies have proven the effectiveness of eco-driving in environmental protection. However, there still lacks a clear understanding of the impacts of eco-driving on overall performance of a signalized road network at different congestion levels. In this paper, we systematically study the impacts of a green driving strategy based on the advisory speed limit (ASL) to smooth the vehicle trajectories. We first analyze the limitations of the static ASL algorithm and propose the dynamic ASL algorithm based on it. We then review the car-following models, including Newell’s car-following model, BA Newell’s car-following and intelligent driver model (IDM), that we utilize for simulating vehicle movements. For evaluation, we introduce network fundamental diagrams (NFD) and fuel consumption as indicators to measure the efficiency of our algorithm. With numerical results, we demonstrate that our dynamic ASL can change the start-up and clearance behaviors so as to smooth vehicles’ trajectories and improve system mobility. In particular, it mainly plays a role under saturated conditions; with proper car-following models, capacity can be increased by up to 40% and fuel consumption can be reduced by up to 25%. We further demonstrate that our algorithm is effective cooperatively for different market penetration rates (MPR) of connected vehicles and work out a proper range of the ASL implementation area. Our algorithm can make a contribution even when 5% of the vehicles adopt our algorithm, with a higher MPR, it can be increasingly more efficient; and the recommended ASL implementation area is in the range of 100 meters to 150 meters. In the future, we are interested in extending our idea for over-saturated conditions and some more complicated transportation systems