A Variable-Length Chromosome Genetic Algorithm to Solve a Road Traffic Coordination Multipath Problem

• Main Authors: Luis Cruz-Piris, Ivan Marsa-Maestre, Miguel A. Lopez-Carmona
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Cooperative systems; genetic algorithms; intelligent vehicles; road traffic intersection; optimization
• Online Access: https://ieeexplore.ieee.org/document/8795464/

The problems related to traffic coordination in intersections are quite common in large cities. Current solutions are based on the utilization of static priorities (i.e. yield signs), on variable signaling like traffic lights, or even on the physical modification of the road structures by transforming intersections in roundabouts. The emergence, evolution, and consolidation of technologies that enable the paradigm of connected and autonomous vehicles have allowed the development of new solutions where the vehicles' coordination follow a preset path without stopping when entering the intersections. In this work, we propose using a genetic algorithm with variable-length chromosomes to solve the vehicle coordination multipath problem in intersections. The proposed algorithm is focused on optimizing the vehicles' arrival sequencing according to preset flow rates. While other solutions assume the same flow rates in every branch of the intersection, in our proposal the traffic flows can be asymmetric. We extend one of the existent intersection models, based on fixed paths, to allow multiple paths. This means that each vehicle can go from any input point to any output branch in the intersection. Moreover, we have designed specific selection, crossover and mutation operators, and a new methodology to carry out the crossover function between different sized individuals, which are adapted to the specific peculiarities of the problem. Our proposal has been validated by carrying out tests using input data with known solutions and with random data. The results have been compared with systems based on other optimizers, obtaining improved results in the fitness outcome up to 9.1%, and up to 126% in computation time.