Leveraging Vehicle-to-Infrastructure Communications for Adaptive Traffic Signaling and Better Energy Utilization

According to a recent report by the US Treasury Department, America wastes $8 billion annually in energy costs because of traffic congestion. Adding the cost of lost time, the damage is said to reach around $100 billion. Moreover, high energy consumption adds to air pollution and contributes to the global warming problem. Infrastructure where different entities (cars and traffic signals) can communicate with each other offers the potential for reducing this waste. But by how much? Suppose full information about location, velocity, and acceleration of each vehicle were available for all vehicles in the vicinity of an isolated traffic signal. Could an intelligent traffic signal predict and adjust to the best possible traffic light cycle times to minimize fuel loss per vehicle? If light timing were changed dynamically based on real-time information from new traffic arrivals after a small interval of time, how much lower fuel loss could be achieved than by basing timing on macro-level metrics such as flow rates and limited vehicle information such as that provided by in-pavement loop detectors? Answering these questions involves developing a simulation framework that is based on an understanding of typical yet safe vehicle operation (by human drivers or autonomous vehicles) and of various traffic arrival patterns, as well as the ability to estimate fuel loss (and/or other optimization objectives) in many different situations.