Towards Adaptive Eco-Approach and Departure For Actuated Signalized Corridors
Intelligent energy efficiency is required to reduce the greenhouse gas emissions from the transportation sector. Consequently, it is important to take advantage of all information available to connected vehicles, e.g. signal phase and timing (SPaT) information at downstream intersections. One of the most promising energy-saving intelligent applications is Eco-approach and Departure (EAD), which takes advantage of SPaT data and allows for the computation of the optimal strategy to approach a signalized intersection. Recent research has assumed that signal timing is deterministic, and hence predictable from SPaT information. However, in real-world scenarios this is not the case, and the signal’s actual time-of-change is unpredictable. This work will validate the EAD algorithm on real-world SPaT data from a signalized intersection in a signalized corridor, and provide insights on how to handle uncertain SPaT data when designing EAD algorithms that could adapt to multiple actuated signals in a corridor.