Optimized Flexible Lane Assignment Using Simulation and Metaheuristic Algorithms

Abstract

Technological advances with connected and autonomous vehicles (CAVs) have opened opportunities to increase the efficiency of transportation networks. A novel control framework called combined flexible lane assignment and reservation-based intersection control (CFLARIC) system has been recently proposed for better management of directionally unrestricted CAVs traffic flows in an urban environment. CFLARIC offers a full spectrum of lane assignment possibilities in combination with the appropriate reservation-based intersection control. In CFLARIC, vehicles position themselves in a proper lane before they reach the downstream intersection, which enables resolution of vehicular conflicts both between intersections and within the intersection boxes. Although CFLARIC has shown promising results, only a limited number of lane assignment scenarios have been designed and tested using some preset rules and based on the previous research objectives. Therefore, the optimality of their performance has never been studied. The objective of this study is to address the flexible traffic lane assignment in such a system as a network optimization problem, in which an optimal lane assignment schema is achieved using metaheuristic optimization algorithms. To this end, a combination of NetLogo and the BehaviorSearch tool is utilized for the simulation modeling and optimization process using random search and brute-force as the search algorithms. The output of the optimization process is the lane assignment that leads to a minimum total travel time for a given network geometry and traffic volumes. Results indicate that a flexible control concept such as optimized CFLARIC has great potential to improve the efficiency of traffic control strategies with CAVs.