Evolutionary Minimization of Traffic Congestion

Traffic congestion is a major issue that can be solved by suggesting drivers alternative routes they are willing to take. This concept has been formalized as a strategic routing problem in which a single alternative route is suggested to an existing one. We extend this formalization and introduce the multiple-routes (MRs) problem, which is given a start and destination and aims at finding up to $n$ different routes that the drivers strategically disperse over, minimizing the overall travel time of the system. Due to the NP-hard nature of the problem, we introduce the MRs evolutionary algorithm (MREA) as a heuristic solver. We study several mutation and crossover operators and evaluate them on real-world data of Berlin, Germany. We find that a combination of all operators yields the best result, reducing the overall travel time by a factor between 1.8 and 3, in the median, compared to all drivers taking the fastest route. 6mm]Please cite reference [2] in the text of the paper. It was removed from the abstract as having reference in an abstract is contrary to IEEE journal style. For the base case n=2 , we compare our MREA to the highly tailored optimal solver by Blasius et al. (2020), and show that, in the median, our approach finds solutions of quality at least 99.69% of an optimal solution while only requiring 40% of the time.