Otimização de Semáforos para Tráfego Terrestre com Estratégias Evolutivas
Abstract
Optimizing traffic lights for land vehicle traffic is a task of great relevance, since the occupation of urban pathways is currently increasing in urban environments, consequently increasing delays, traffic jams and their consequential losses. Reacting to this issue, a significant part of the related work considers vehicular traffic to the detriment of pedestrians. However, the higher their waiting time, the more pedestrians adopt risky behavior. Aiming to treat the issue considering the conflicting interests of vehicles and pedestrians, this work performs the optimization of traffic lights considering the average delays of both types of users, through an Evolution Strategy. The problem is modeled on the basis of reference works of the area. The approach has found solutions that keep the pedestrian delays within the limits given by related work.
References
Beyer, H.-G. and Schwefel, H.-P. (2002). Evolution strategies – A comprehensive introduction. Natural Computing, 1(1):3–52.
Brilon, W. (1994). Traffic engineering and the new German highway capacity manual. Transportation Research Part A: Policy and Practice, 28(6):469–481.
Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Engrg., page 28.
Eiben, A. and Smith, J. (2015). Introduction to Evolutionary Computing. Natural Computing Series. Springer Berlin Heidelberg, Berlin, Heidelberg.
Eshelman, L. J. and Schaffer, J. D. (1993). Real-coded genetic algorithms and intervalschemata. In WHITLEY, L. D., editor, Foundations of Genetic Algorithms, volume 2 of Foundations of Genetic Algorithms, pages 187 – 202. Elsevier.
Gao, K., Zhang, Y., Zhang, Y., and Su, R. (2017). A meta-heuristic with ensemble of local search operators for urban traffic light optimization. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–8.
Gao, K., Zhang, Y., Zhang, Y., Su, R., and Suganthan, P. N. (2019). Meta-heuristics for bi-objective urban traffic light scheduling problems. IEEE Transactions on Intelligent Transportation Systems, 20(7):2618–2629.
Ishaque, M. M. and Noland, R. B. (2005). Multimodal Microsimulation of Vehicle and Pedestrian Signal Timings. Transportation Research Record, page 8.
Jin, J., Ma, X., and Kosonen, I. (2017). A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation. Advances in Engineering Software, 114:348–360.
Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wießner, E. (2018). Microscopic Traffic Simulation using SUMO. In The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE.
Ma, W., Liao, D., Liu, Y., and Lo, H. K. (2015). Optimization of pedestrian phase patterns and signal timings for isolated intersection. Transportation Research Part C: Emerging Technologies, 58:502–514.
Reed, T. and Kidd, J. (2019). INRIX 2018 Global Traffic Scorecard. Technical report, INRIX.
Sanchez-Medina, J., Galan-Moreno, M., and Rubio-Royo, E. (2010). 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):132–141.
Schmöcker, J.-D., Ahuja, S., and Bell, M. G. (2008). Multi-objective signal control of urban junctions – Framework and a London case study. Transportation Research Part C: Emerging Technologies, 16(4):454–470.
Storn, R. and Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, page 19.
Vallyon, C., Binder, S., and Hodgson, S. (2008). Reducing Pedestrian Delays at Traffic Signals. In IPENZ Transportation Group Conference New Plymouth, page 15, New Plymouth, New Zealand.
Virkler, M. R. (1998). Pedestrian Compliance Effects on Signal Delay. Transportation Research Record: Journal of the Transportation Research Board, 1636(1):88–91.
Yang, Z. (2010). Signal Timing Optimization Based on Minimizing Vehicle and Pedestrian Delay by Genetic Algorithm. Dissertação de Mestrado, 2010, University of Illinois at Urbana-Champaign.
Yang, Z. and Benekohal, R. F. (2011). Use of Genetic Algorithm for Phase Optimization at Intersections with Minimization of Vehicle and Pedestrian Delays. Transportation Research Record: Journal of the Transportation Research Board, 2264(1):54–64.
Brilon, W. (1994). Traffic engineering and the new German highway capacity manual. Transportation Research Part A: Policy and Practice, 28(6):469–481.
Deb, K. (2000). An efficient constraint handling method for genetic algorithms. Comput. Methods Appl. Mech. Engrg., page 28.
Eiben, A. and Smith, J. (2015). Introduction to Evolutionary Computing. Natural Computing Series. Springer Berlin Heidelberg, Berlin, Heidelberg.
Eshelman, L. J. and Schaffer, J. D. (1993). Real-coded genetic algorithms and intervalschemata. In WHITLEY, L. D., editor, Foundations of Genetic Algorithms, volume 2 of Foundations of Genetic Algorithms, pages 187 – 202. Elsevier.
Gao, K., Zhang, Y., Zhang, Y., and Su, R. (2017). A meta-heuristic with ensemble of local search operators for urban traffic light optimization. In 2017 IEEE Symposium Series on Computational Intelligence (SSCI), pages 1–8.
Gao, K., Zhang, Y., Zhang, Y., Su, R., and Suganthan, P. N. (2019). Meta-heuristics for bi-objective urban traffic light scheduling problems. IEEE Transactions on Intelligent Transportation Systems, 20(7):2618–2629.
Ishaque, M. M. and Noland, R. B. (2005). Multimodal Microsimulation of Vehicle and Pedestrian Signal Timings. Transportation Research Record, page 8.
Jin, J., Ma, X., and Kosonen, I. (2017). A stochastic optimization framework for road traffic controls based on evolutionary algorithms and traffic simulation. Advances in Engineering Software, 114:348–360.
Lopez, P. A., Behrisch, M., Bieker-Walz, L., Erdmann, J., Flötteröd, Y.-P., Hilbrich, R., Lücken, L., Rummel, J., Wagner, P., and Wießner, E. (2018). Microscopic Traffic Simulation using SUMO. In The 21st IEEE International Conference on Intelligent Transportation Systems. IEEE.
Ma, W., Liao, D., Liu, Y., and Lo, H. K. (2015). Optimization of pedestrian phase patterns and signal timings for isolated intersection. Transportation Research Part C: Emerging Technologies, 58:502–514.
Reed, T. and Kidd, J. (2019). INRIX 2018 Global Traffic Scorecard. Technical report, INRIX.
Sanchez-Medina, J., Galan-Moreno, M., and Rubio-Royo, E. (2010). 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):132–141.
Schmöcker, J.-D., Ahuja, S., and Bell, M. G. (2008). Multi-objective signal control of urban junctions – Framework and a London case study. Transportation Research Part C: Emerging Technologies, 16(4):454–470.
Storn, R. and Price, K. (1997). Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces. Journal of Global Optimization, page 19.
Vallyon, C., Binder, S., and Hodgson, S. (2008). Reducing Pedestrian Delays at Traffic Signals. In IPENZ Transportation Group Conference New Plymouth, page 15, New Plymouth, New Zealand.
Virkler, M. R. (1998). Pedestrian Compliance Effects on Signal Delay. Transportation Research Record: Journal of the Transportation Research Board, 1636(1):88–91.
Yang, Z. (2010). Signal Timing Optimization Based on Minimizing Vehicle and Pedestrian Delay by Genetic Algorithm. Dissertação de Mestrado, 2010, University of Illinois at Urbana-Champaign.
Yang, Z. and Benekohal, R. F. (2011). Use of Genetic Algorithm for Phase Optimization at Intersections with Minimization of Vehicle and Pedestrian Delays. Transportation Research Record: Journal of the Transportation Research Board, 2264(1):54–64.
Published
2021-08-16
How to Cite
GOMES, Lucas de Carvalho; COSTA, Luís Henrique M. K..
Otimização de Semáforos para Tráfego Terrestre com Estratégias Evolutivas. In: URBAN COMPUTING WORKSHOP (COURB), 5. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 140-153.
ISSN 2595-2706.
DOI: https://doi.org/10.5753/courb.2021.17110.
