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An Evolutionary Based Approach for the Traffic Lights Optimization Problem

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Mathematical Optimization Theory and Operations Research (MOTOR 2019)

Abstract

We consider the traffic lights optimization problem which arises in city management due to continuously growing traffic. Given a road network and predictions (or statistical data) about the traffic flows through the arcs of this network the problem is to define the offsets and phase length for each traffic light in order to improve the overall quality of the service. The latter can be defined through a number of criteria, such as average speed, average trip duration, total waiting time etc. For this problem, we present an evolutionary based heuristic approach. We use a simulation model on the basis of the SUMO modeling system to evaluate the quality of obtained solutions. The results of numerical experiments on real data confirm the efficiency of the proposed approach.

Supported by RFBR according to the research project 19-01-00562.

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References

  1. McKenney, D., White, T.: Distributed and adaptive traffic signal control within a realistic traffic simulation. Eng. Appl. Artif. Intell. 26(1), 574–583 (2013)

    Article  Google Scholar 

  2. Zheng, X., Recker, W.: An adaptive control algorithm for traffic-actuated signals. Transp. Res. Part C: Emerg. Technol. 30, 93–115 (2013)

    Article  Google Scholar 

  3. Jovanovic, A., Nikoli, M., Teodorovic, D.: Area-wide urban traffic control: a bee colony optimization approach. Transp. Res. Part C: Emerg. Technol. 77, 329–350 (2017)

    Article  Google Scholar 

  4. Li, J.: Discretization modeling, integer programming formulations and dynamic programming algorithms for robust traffic signal timing. Transp. Res. Part C: Emerg. Technol. 19(4), 708–719 (2011)

    Article  Google Scholar 

  5. Coogan, S., Kim, E., Gomes., G., Arcak, M., Varaiya, P.: Offset optimization in signalized traffic networks via semidefinite relaxation. Transp. Res. Part B: Methodol. 100, 82–92 (2017)

    Article  Google Scholar 

  6. Gao, K., Zhang, Y., Sadollah, A., Su, R.: Optimizing urban traffic light scheduling problem using harmony search with ensemble of local search. Appl. Soft Comput. 48, 359–372 (2016)

    Article  Google Scholar 

  7. Garcia-Nieto, J., Alba, E., Carolina Olivera, A.: Swarm intelligence for traffic light scheduling: application to real urban areas. Eng. Appl. Artif. Intell. 25(2), 274–283 (2013)

    Article  Google Scholar 

  8. Transportation Research Record: Highway capacity manual. Technical report, Transportation Research Record (2000)

    Google Scholar 

  9. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  10. Krajzewicz, D., Bonert, M., Wagner, P.: The open source traffic simulation package SUMO. In: RoboCup 2006 Infrastructure Simulation Competition (2006)

    Google Scholar 

  11. Souravlias, D., Luquey, G., Albay, E., Parsopoulos, K.E.: Smart traffic lights: a first parallel computing approach. In: 2016 International Conference on Intelligent Networking and Collaborative Systems (INCoS), Ostrawva, pp. 229–236 (2016)

    Google Scholar 

  12. Zhang, L., Song, Z., Tang, X., Wang, D.: Signal coordination models for long arterials and grid networks. Transp. Res. Part C: Emerg. Technol. 71, 215–230 (2016)

    Article  Google Scholar 

  13. Angulo, E., Romero, F.P., Garcia, R., Serrano-Guerrero, J., Olivas, J.A.: An adaptive approach to enhanced traffic signal optimization by using soft-computing techniques. Expert Syst. Appl. 38(3), 2235–2247 (2011)

    Article  Google Scholar 

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Correspondence to Ivan Davydov .

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Davydov, I., Tolstykh, D. (2019). An Evolutionary Based Approach for the Traffic Lights Optimization Problem. In: Bykadorov, I., Strusevich, V., Tchemisova, T. (eds) Mathematical Optimization Theory and Operations Research. MOTOR 2019. Communications in Computer and Information Science, vol 1090. Springer, Cham. https://doi.org/10.1007/978-3-030-33394-2_2

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  • DOI: https://doi.org/10.1007/978-3-030-33394-2_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33393-5

  • Online ISBN: 978-3-030-33394-2

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