Bernstein Search Differential Evolution Approach for Real-time and Adaptive Traffic Signal Optimization in Urban Road Network
Keywords:
Bernstein Search Differential Evolution Algorithm, Evolutionary Optimization Techniques, Traffic Signal Timing Optimization, Traffic SimulationAbstract
As urban populations surge, traffic congestion at multi-intersection networks becomes increasingly problematic, necessitating innovative solutions for traffic signal timing optimization. This study introduces the application of the Bernstein Search Differential Evolution (BSD) Algorithm, an evolutionary approach adapted specifically for traffic signal control optimization in a multi-intersection arterial. The BSD algorithm, notable for its parameter-free and adaptive nature, was applied to a complex urban traffic network in Kayseri, Türkiye. It was benchmarked against traditional algorithms such as the Genetic Algorithm (GA), Standard Differential Evolution (sDE), and the VISSIM software's algorithms. The number of vehicles entering the network in the morning, afternoon, and evening periods, which are the rush hours of the study area, has been obtained for 12 weeks. Through extensive simulations conducted in MATLAB and VISSIM, the BSD algorithm demonstrated superior performance, particularly during peak traffic hours, by significantly reducing vehicle wait times and improving traffic flow efficiency. Results indicated that the BSD algorithm reduced average vehicle wait times by up to 20% and increased traffic throughput by 15% compared to conventional methods. This study underscores the BSD algorithm’s potential to significantly enhance traffic management in urban areas and suggests its broader application could effectively mitigate global urban traffic congestion.
References
[1] Lin, H., Han, Y., Cai, W., & Jin, B. (2023). Traffic signal optimization based on fuzzy control and differential evolution algorithm. IEEE Transactions on Intelligent Transportation Systems, 24, 8555–8566. https://doi.org/10.1109/TITS.2022.3195221
[2] Sanchez-Medina, J. J., Galan-Moreno, M. J., & 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, 132–141. https://doi.org/10.1109/TITS.2009.2034383
[3] Fu, X., Gao, H., Cai, H., Wang, Z., & Chen, W. (2021). How to improve urban intelligent traffic? A case study using traffic signal timing optimization model based on swarm intelligence algorithm. Sensors, 21, Article 2631. https://doi.org/10.3390/s21082631
[4] Zhao et al. (2021). [The bibliographic details of this source are missing in the submitted manuscript and must be completed by the authors.]
[5] Liu, Q., & Xu, J. (2012). Traffic signal timing optimization for isolated intersections based on differential evolution bacteria foraging algorithm. Procedia - Social and Behavioral Sciences, 43, 210–215. https://doi.org/10.1016/j.sbspro.2012.04.093
[6] PTV. (2018). [The bibliographic details of this source are missing in the submitted manuscript and must be completed by the authors.]
[7] Foy, M. D., Benekohal, R. F., & Goldberg, D. E. (1992). Signal timing determination using genetic algorithms. Transportation Research Record, 108–115.
[8] Zhang, B., Shang, L., & Chen, D. (2010). Traffic intersection signal-planning multi-object optimization based on genetic algorithm. In 2010 2nd International Workshop on Intelligent Systems and Applications (ISA) (pp. 1–4). IEEE. https://doi.org/10.1109/IWISA.2010.5473294
[9] Chin, Y. K., Yong, K. C., Bolong, N., Yang, S. S., & Teo, K. T. K. (2011). Multiple intersections traffic signal timing optimization with genetic algorithm. In 2011 IEEE International Conference on Control System, Computing and Engineering (ICCSCE) (pp. 454–459). IEEE. https://doi.org/10.1109/ICCSCE.2011.6190569
[10] Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6, 182–197. https://doi.org/10.1109/4235.996017
[11] Shen, Z., Wang, K., & Wang, F.-Y. (2013). GPU based non-dominated sorting genetic algorithm-II for multi-objective traffic light signaling optimization with agent based modeling. In 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013) (pp. 1840–1845). IEEE. https://doi.org/10.1109/ITSC.2013.6728496
[12] Li, Y., Yu, L., Tao, S., & Chen, K. (2013). Multi-objective optimization of traffic signal timing for oversaturated intersection. Mathematical Problems in Engineering, 2013, 1–9. https://doi.org/10.1155/2013/182643
[13] Qian, R., Lun, Z., Wenchen, Y., & Meng, Z. (2013). A traffic emission-saving signal timing model for urban isolated intersections. Procedia - Social and Behavioral Sciences, 96, 2404–2413. https://doi.org/10.1016/j.sbspro.2013.08.269
[14] Dezani, H., Marranghello, N., & Damiani, F. (2014). Genetic algorithm-based traffic lights timing optimization and routes definition using Petri net model of urban traffic flow. IFAC Proceedings Volumes, 47, 11326–11331. https://doi.org/10.3182/20140824-6-ZA-1003.01321
[15] Dezani, H., Bassi, R. D. S., Marranghello, N., Gomes, L., Damiani, F., & Nunes Da Silva, I. (2014). Optimizing urban traffic flow using genetic algorithm with Petri net analysis as fitness function. Neurocomputing, 124, 162–167. https://doi.org/10.1016/j.neucom.2013.07.015
[16] Tan, M. K., Chuo, H. S. E., Chin, R. K. Y., Yeo, K. B., & Teo, K. T. K. (2016). Optimization of urban traffic network signalization using genetic algorithm. In 2016 IEEE Conference on Open Systems (ICOS) (pp. 87–92). IEEE. https://doi.org/10.1109/ICOS.2016.7881994
[17] Yao, Z., Jiang, Y., Zhao, B., Luo, X., & Peng, B. (2020). A dynamic optimization method for adaptive signal control in a connected vehicle environment. Journal of Intelligent Transportation Systems, 24, 184–200. https://doi.org/10.1080/15472450.2019.1643723
[18] Xu, M., An, K., Vu, L. H., Ye, Z., Feng, J., & Chen, E. (2019). Optimizing multi-agent based urban traffic signal control system. Journal of Intelligent Transportation Systems, 23, 357–369. https://doi.org/10.1080/15472450.2018.1501273
[19] Yadav, A., & Nuthong, C. (2020). Traffic signal timings optimization based on genetic algorithm and gradient descent. In 2020 5th International Conference on Computer and Communication Systems (ICCCS) (pp. 670–674). IEEE. https://doi.org/10.1109/ICCCS49078.2020.9118450
[20] Liu, S., Li, Y., Qiu, Y., Zhang, B., Qiu, S., & Liu, X. (2020). Signal timing optimization algorithm for an intersection connected with an urban expressway. Arabian Journal for Science and Engineering, 45, 8667–8684. https://doi.org/10.1007/s13369-020-04835-6
[21] Wang, H., Hu, P., & Wang, H. (2021). A genetic timing scheduling model for urban traffic signal control. Information Sciences, 576, 475–483. https://doi.org/10.1016/j.ins.2021.06.082
[22] Bi, Y., Srinivasan, D., Lu, X., Sun, Z., & Zenf, W. (2014). Type-2 fuzzy multi-intersection traffic signal control with differential evolution optimization. Expert Systems with Applications, 41(16), 7338–7349. https://doi.org/10.1016/j.eswa.2014.06.022
[23] Zhang, M., Zhao, S., Lv, J., & Qian, Y. (2009). Multi-phase urban traffic signal real-time control with multi-objective discrete differential evolution. In 2009 International Conference on Electronic Computer Technology (ICECT) (pp. 296–300). IEEE. https://doi.org/10.1109/ICECT.2009.38
[24] Ceylan, H. (2013). Optimal design of signal controlled road networks using differential evolution optimization algorithm. Mathematical Problems in Engineering, 2013, 1–11. https://doi.org/10.1155/2013/696374
[25] Cakici, Z., & Murat, Y. S. (2019). A differential evolution algorithm-based traffic control model for signalized intersections. Advances in Civil Engineering, 2019, 1–16. https://doi.org/10.1155/2019/7360939
[26] Baskan, O. (2019). A multiobjective bilevel programming model for environmentally friendly traffic signal timings. Advances in Civil Engineering, 2019, 1–13. https://doi.org/10.1155/2019/1638618
[27] Civicioglu, P. (2013). Artificial cooperative search algorithm for numerical optimization problems. Information Sciences, 229, 58–76. https://doi.org/10.1016/j.ins.2012.11.013
[28] Civicioglu, P. (2013). Backtracking search optimization algorithm for numerical optimization problems. Applied Mathematics and Computation, 219, 8121–8144. https://doi.org/10.1016/j.amc.2013.02.017
[29] Civicioglu, P. (2012). Transforming geocentric cartesian coordinates to geodetic coordinates by using differential search algorithm. Computers & Geosciences, 46, 229–247. https://doi.org/10.1016/j.cageo.2011.12.011
[30] Çivicioğlu, P., & Beşdok, E. (2019). Bernstain-search differential evolution algorithm for numerical function optimization. Expert Systems with Applications, 138.
[31] Feliciani, C., Murakami, H., & Nishinari, K. (2018). A universal function for capacity of bidirectional pedestrian streams: Filling the gaps in the literature. PLOS ONE, 13, Article e0208496. https://doi.org/10.1371/journal.pone.0208496
[32] Khan, K., Lobiyal, D. K., & Kılıçman, A. (2015). Bezier curves and surfaces based on modified Bernstein polynomials (arXiv:1511.06594). arXiv.
[33] Azhari, F., Heidarpour, A., & Zhao, X.-L. (2018). On the use of Bernstain-Bézier functions for modelling the post-fire stress-strain relationship of ultra-high strength steel (Grade 1200). Engineering Structures, 175, 605–616. https://doi.org/10.1016/j.engstruct.2018.08.088
[34] Mandal, B. N., & Bhattacharya, S. (2007). Numerical solution of some classes of integral equations using Bernstein polynomials. Applied Mathematics and Computation, 190, 1707–1716. https://doi.org/10.1016/j.amc.2007.02.058
[35] Yousefi, S. A., Behroozifar, M., & Dehghan, M. (2011). The operational matrices of Bernstein polynomials for solving the parabolic equation subject to specification of the mass. Journal of Computational and Applied Mathematics, 235, 5272–5283. https://doi.org/10.1016/j.cam.2011.05.038
