Abstract
In this work, we analyze crossover and mutation operators for traffic signals optimization aiming to understand the problem from a system level perspective. We use MATSim to simulate the transport system of the business district of Quito (Ecuador) with 20000 agents moving in a one-day congested scenario. We relax the usual assumption of common cycle length for all signals and minimize travel time focusing on the optimization of 11 consecutive signals located in a main road. We study individual and combined effects of crossover and mutation for cycle length, offset, and green times. The results of this study provide valuable insights to know better the problem, validate the mobility scenario, and understand the effects of the operators.
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References
Balmer, M., Rieser, M., Meister, K., Charypar, D., Lefebvre, N., Nagel, K.: Matsim-t: Architecture and simulation times. In: Multi-Agent Systems for Traffic and Transportation Engineering (2009)
Ceylan, H.: Traffic signal timing optimisation based on genetic algorithm approach, including drivers routing. Transportation Research 38, 329–342 (2004)
Frederik, R., Topf, J., Karch, C.: Geofabrik (2007), http://www.geofabrik.de (accessed: January 2014)
Grether, D.: Traffic light control in multi-agent transport simulations (2011)
Kelly, B.: A green wave reprieve. Traffic Engineering & Control 53, 55–58 (2012)
Matsim. Multi agent transport simulation, http://matsim.org (accessed: January 2014)
Park, B.: Traffic signal optimization program for oversaturated conditions: Genetic algorithm approach. Transportation Research 1683, 133–142 (2007)
Sanchez-Medina, J.J., Galan-Moreno, M.J., Rubio-Royo, E.: Traffic signal optimization in la almozara district in saragossa under congestion conditions, using genetic algorithms, traffic microsimulation and cluster computing. IEEE Transactions on Intelligent Transportation Systems 11(1), 10 (2010)
Spiegelman, C., Sug-Park, E., Rilett, L.: Transportation Statistics and Microsimulation. CRC Press (2011)
STM. Signal Timming Manual. Federal Highway Administration, USA (2008)
Teklu, F., Sumalee, A., Watling, D.: A genetic algorithm approach for optimizing traffic control signals considering routing. In: Computer-Aided Civil and Infrastructure Engineering, pp. 31–43 (2007)
Warberg, A., Larsen, J., Munk, R.: Green Wave Traffic Optimization - A Survey. IMM-Technical Report-2008-01. Informatics and Mathematical Modelling (2008)
Wardrop, J.G.: Some theoretical aspects of road traffic research. Proceedings of Institution of Civil Engineers 1(2), 325–378 (1952)
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Armas, R., Aguirre, H., Tanaka, K. (2014). Effects of Mutation and Crossover Operators in the Optimization of Traffic Signal Parameters. In: Dick, G., et al. Simulated Evolution and Learning. SEAL 2014. Lecture Notes in Computer Science, vol 8886. Springer, Cham. https://doi.org/10.1007/978-3-319-13563-2_15
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DOI: https://doi.org/10.1007/978-3-319-13563-2_15
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-13562-5
Online ISBN: 978-3-319-13563-2
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