Evolutionary Minimization of Traffic Congestion

Maximilian Böther (1) , Leon Schiller (1) , Philipp Fischbeck (1) , Louise Molitor (1) , Martin S Krejca (2) , Tobias Friedrich (1)

1 HPI - Hasso Plattner Institute for Software Systems Engineering
2 RO - Recherche Opérationnelle

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 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 Multiple-Routes 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, improving the overall travel time by a factor between 1.8 and 3, in the median, compared to all drivers taking the fastest route. For the base case n = 2, we compare our MREA to the highly tailored optimal solver by Bläsius et al. [ATMOS 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.

Mots clés: strategic routing; traffic congestion; optimization; evolutionary algorithm

Domaines: Informatique [cs]

Maximilian Böther, Leon Schiller, Philipp Fischbeck, Louise Molitor, Martin S Krejca, et al.. Evolutionary Minimization of Traffic Congestion. Genetic and Evolutionary Computation Conference (GECCO), Jul 2021, Lille (en ligne), France. pp.937-945, ⟨10.1145/3449639.3459307⟩. ⟨hal-03201943⟩