Field Assessment of Intersection Performance Enhanced by Traffic Signal Optimization and Vehicle Trajectory Planning

The development of Connected Automated Vehicle (CAV) technology introduces vehicle connectivity and automation into urban intersection management. It offers powerful means for accurate traffic state perception and effective traffic control actuation. This enabled the development of an intersection control algorithm that included an optimal traffic signal control algorithm and a trajectory planning function. The intersection controller aimed to maximize the intersection throughput while improving the vehicle energy efficiency via implementing adaptive signal phasing and time plans and sending reference trajectories to CAVs. Its effectiveness was tested on a closed test track equipped with a real-time traffic simulation platform, a traffic signal controller, and three physical test vehicles. The test results suggest that the intersection control algorithm was able to improve the average speed of the test vehicles by 9%, although the optimal signal controller or the trajectory planning algorithm alone could only provide marginal speed benefits. This demonstrated the advantage of combining multiple advanced traffic management functions for obtaining an elevated performance increase. For the energy efficiency analysis, both traditional gasoline-engine and hybrid vehicles were used. The hybrid test vehicles obtained fuel savings mostly via the optimal traffic signal controller (e.g., 17% fuel consumption reduction). On the other hand, the gasoline-engine test vehicle's fuel savings could be largely attributed to the trajectory planning (e.g., 21% fuel consumption reduction). Overall, the proposed intersection controller demonstrated significant vehicle mobility and energy efficiency improvements for vehicles with different powertrains in a nearly realistic traffic condition.