Signal control optimization for automated vehicles at isolated signalized intersections

• Published in: Transportation research. Part C, Emerging technologies Vol. 49; pp. 1 - 18
• Main Authors: Li, Zhuofei, Elefteriadou, Lily, Ranka, Sanjay
• Format: Journal Article
• Language: English
• Published: Elsevier India Pvt Ltd 01.12.2014
• Subjects: Algorithms; Automated; Automated vehicle; Delay; Demand; Intersections; Matlab; Optimization; Signal control optimization; Vehicles; Signal control optimization; Automated vehicle
• ISSN: 0968-090X; 1879-2359
• DOI: 10.1016/j.trc.2014.10.001

•We develop a signal control optimization algorithm for automated vehicles.•We simulate the proposed algorithm and compare it against actuated signal control.•The proposed algorithm improves the intersection performance.•The proposed algorithm works better for the more congested conditions.•Communication range has impact on the performance of the proposed algorithm. Traffic signals at intersections are an integral component of the existing transportation system and can significantly contribute to vehicular delay along urban streets. The current emphasis on the development of automated (i.e., driverless and with the ability to communicate with the infrastructure) vehicles brings at the forefront several questions related to the functionality and optimization of signal control in order to take advantage of automated vehicle capabilities. The objective of this research is to develop a signal control algorithm that allows for vehicle paths and signal control to be jointly optimized based on advanced communication technology between approaching vehicles and signal controller. The algorithm assumes that vehicle trajectories can be fully optimized, i.e., vehicles will follow the optimized paths specified by the signal controller. An optimization algorithm was developed assuming a simple intersection with two single-lane through approaches. A rolling horizon scheme was developed to implement the algorithm and to continually process newly arriving vehicles. The algorithm was coded in MATLAB and results were compared against traditional actuated signal control for a variety of demand scenarios. It was concluded that the proposed signal control optimization algorithm could reduce the ATTD by 16.2–36.9% and increase throughput by 2.7–20.2%, depending on the demand scenario.