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Article Dans Une Revue IEEE Transactions on Evolutionary Computation Année : 2022

Evolutionary Minimization of Traffic Congestion

Résumé

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, reducing 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. [2], 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.
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Dates et versions

hal-03904251 , version 1 (16-12-2022)

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Maximilian Böther, Leon Schiller, Philipp Fischbeck, Louise Molitor, Martin S. Krejca, et al.. Evolutionary Minimization of Traffic Congestion. IEEE Transactions on Evolutionary Computation, In press, 27 (6), pp.1809-1821. ⟨10.1109/TEVC.2022.3228750⟩. ⟨hal-03904251⟩
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