69A43551747123This research develops a two-layer optimization approach that provides energy-optimal control for vehicles and traffic signal controllers. The first layer optimizes the traffic signal timings to minimize the total energy consumption levels of approaching vehicles from upstream traffic. The traffic signal optimization can be easily implemented in real-time signal controllers, and it overcomes the issues in the traditional Webster\u2019s method of overestimating the cycle length when the traffic volume-to-capacity ratio exceeds 50 percent. The second layer optimizer is the vehicle speed controller, which calculates the optimal vehicle brake and throttle levels to minimize the energy consumption of individual vehicles. The A-star dynamic programming method is used to solve the formulated optimization problem in the second layer to expedite the speed computation so that the optimal vehicle trajectories can be computed in real time and easily implemented in a simulation software for testing. The proposed integrated controller is first tested on an isolated signalized intersection, and then on an arterial network with multiple intersections to investigate the performance of the proposed controller under various traffic demand levels. The test results demonstrate that the proposed integrated controller can greatly improve energy efficiency with fuel savings of up to 17.7%. It can also enhance traffic mobility by reducing traffic delays by up to a 47.2% and reducing vehicle stops by up to 24.8%. Moreover, the data collected from 70 participants in the driving simulator demonstrates that the proposed speed guidance system can reduce emissions by up to 20% in uphill scenarios and up to 7% in downhill scenarios. Lastly, different types of speed guidance options have been investigated in the simulator tests, and the color-coded option is the most favorable choice for participants
