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Adaptive collaborative optimization of traffic network signal timing based on immune-fireworks algorithm and hierarchical strategy

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Abstract

In contemporary urban areas, the construction speed of urban roads lags far behind the growth rate of the number of vehicles. The traffic delay caused by excessive vehicles is a major challenge for modern transportation systems. In this paper, we design an adaptive coordination traffic signal control system based on a three-level framework. In the framework, we propose a hierarchical strategy, which is helpful in avoiding possible offset conflicts and configuring the offsets reasonably. In addition, we establish a multi-intersection traffic signal control model with the goal of minimizing traffic delays. To solve the proposed model, we propose a new algorithm, called the Immune-Fireworks algorithm (IM-FWA), on the basis of the artificial immune and fireworks algorithms. Inspired by the antibody maintenance mechanism, diversity mechanism and communication mechanism of the artificial immune algorithm, IM-FWA can effectively overcome the shortcomings of the fireworks algorithm, such as its limited search range and lack of interaction among fireworks. The experiments show that the proposed model and algorithm have good practicability and that our control system can obtain a better signal timing schedule to effectively reduce traffic delays.

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References

  1. Abd Elaziz M, Heidari AA, Fujita H, Moayedi H (2020) A competitive chain-based harris hawks optimizer for global optimization and multi-level image thresholding problems. Appl Soft Comput 95:106347

    Article  Google Scholar 

  2. Aïder M, Gacem O, Hifi M (2021) Branch and solve strategies-based algorithm for the quadratic multiple knapsack problem. J Oper Res Soc 72(1):1–18

    Article  Google Scholar 

  3. Almasri EH (2014) Signal coordination for saving energy and reducing congestion using transyt-7f model and its application in gaza city. Nat Res 5(7):281–290

    Google Scholar 

  4. Aslani M, Seipel S, Wiering M (2018) Continuous residual reinforcement learning for traffic signal control optimization. Can J Civ Eng 45(8):690–702

    Article  Google Scholar 

  5. Aslani M, Mesgari MS, Seipel S, Wiering M (2019) Developing adaptive traffic signal control by actor–critic and direct exploration methods. In: Proceedings of the institution of civil engineers-transport, Thomas Telford Ltd, vol 172, pp 289–298

  6. Bing Q, Qu D, Chen X, Pan F, Wei J (2019) Arterial travel time estimation method using scats traffic data based on knn-lssvr model. Adv Mech Eng 11(5):1687814019841926

    Article  Google Scholar 

  7. Chen Y, Li L, Zhao X, Xiao J, Wu Q, Tan Y (2019) Simplified hybrid fireworks algorithm. Knowl Based Syst 173:128–139

    Article  Google Scholar 

  8. Cho HJ, Huang TJ, Huang CC (2019) Path-based maxband with green-split variables and traffic dispersion. Transportmetrica B Transp Dyn 7(1):726–740

    Article  Google Scholar 

  9. Çinar M, Kaygusuz A (2020) Artificial immunity based wound healing algorithm for power loss optimization in smart grids. Adv Electr Comput Eng 20(1):11–18

    Article  Google Scholar 

  10. Daganzo CF, Lehe LJ (2016) Traffic flow on signalized streets. Transp Res B Methodol 90:56–69

    Article  Google Scholar 

  11. Gao K, Zhang Y, Zhang Y, Su R, Suganthan PN (2018) Meta-heuristics for bi-objective urban traffic light scheduling problems. IEEE Trans Intell Transp Syst 20(7):2618–2629

    Article  Google Scholar 

  12. Hu W, Wang H, Yan L, Du B (2016) A swarm intelligent method for traffic light scheduling: application to real urban traffic networks. Appl Intell 44(1):208–231

    Article  Google Scholar 

  13. Jamal A, Rahman MT, Al-Ahmadi HM, Ullah IM, Zahid M (2020) Intelligent intersection control for delay optimization: Using meta-heuristic search algorithms. Sustainability 12(5):1896

    Article  Google Scholar 

  14. Jian L (2020) Multi-objective optimisation of traffic signal control based on particle swarm optimisation. Int J Grid and Util Comput 11(4):547–553

    Article  Google Scholar 

  15. Kühnel N, Thunig T, Nagel K (2018) Implementing an adaptive traffic signal control algorithm in an agent-based transport simulation. Procedia Comput Sci 130:894–899

    Article  Google Scholar 

  16. Kurtuluş E, Yıldız AR, Sait SM, Bureerat S (2020) A novel hybrid harris hawks-simulated annealing algorithm and rbf-based metamodel for design optimization of highway guardrails. Mater Test 62 (3):251–260

    Article  Google Scholar 

  17. Lafferriere G (2019) A decentralized network consensus control approach for urban traffic signal optimization. TREC Project Briefs 74(4)

  18. Li J, Zheng S, Tan Y (2016) The effect of information utilization: Introducing a novel guiding spark in the fireworks algorithm. IEEE Trans Evol Comput 21(1):153–166

    Article  Google Scholar 

  19. Liu J, Zheng S, Tan Y (2013) The improvement on controlling exploration and exploitation of firework algorithm. In: International conference in swarm intelligence. Springer, pp 11–23

  20. Lu Q, Kim KD (2019) Autonomous and connected intersection crossing traffic management using discrete-time occupancies trajectory. Appl Intell 49(5):1621–1635

    Article  Google Scholar 

  21. Mirjalili S (2019) Genetic algorithm. In: Evolutionary algorithms and neural networks. Springer, pp 43–55

  22. NACTO I (2013) Urban street design guide. Island Press/Center for Resource Economics, Washington, DC

    Book  Google Scholar 

  23. Patel V, Raja B, Savsani V, Yildiz AR (2020) Qualitative and quantitative performance comparison of recent optimization algorithms for economic optimization of the heat exchangers. Arch Comput Methods Eng, pp 1–16

  24. Singh A, Banda J (2017) Hybrid artificial bee colony algorithm based approaches for two ring loading problems. Appl Intell 47(4):1157–1168

    Article  Google Scholar 

  25. Sutandi AC (2020) Advanced traffic control systems: Performance evaluation in a developing country. LAP Lambert Academic Publishing Saarbrcken, Germany

  26. Tang K, Yang P, Yao X (2016) Negatively correlated search. IEEE J Sel Areas Commun 34(3):542–550

    Article  Google Scholar 

  27. Taradeh M, Mafarja M, Heidari AA, Faris H, Aljarah I, Mirjalili S, Fujita H (2019) An evolutionary gravitational search-based feature selection. Inform Sci 497:219–239

    Article  Google Scholar 

  28. Tian R, Zhang X (2017) Design and evaluation of an adaptive traffic signal control system–a case study in hefei, china. Transp Res Procedia 21:141–153

    Article  Google Scholar 

  29. Wang P, Lei Y, Agbedanu PR, Zhang Z (2020a) Makespan-driven workflow scheduling in clouds using immune-based pso algorithm. IEEE Access 8:29281–29290

    Article  Google Scholar 

  30. Wang X, Ke L, Qiao Z, Chai X (2020b) Large-scale traffic signal control using a novel multiagent reinforcement learning. IEEE Trans Cybernet 51(1):174–187

    Article  Google Scholar 

  31. Yao Z, Jiang H, Cheng Y, Jiang Y, Ran B (2020) Integrated schedule and trajectory optimization for connected automated vehicles in a conflict zone. IEEE Trans Intell Transp Syst 21(12):5334–5345

    Google Scholar 

  32. Yildiz AR (2019) A novel hybrid whale–nelder–mead algorithm for optimization of design and manufacturing problems. Int J Adv Manuf Technol 105(12):5091–5104

    Article  Google Scholar 

  33. Yıldız BS, Yıldız AR (2019) The harris hawks optimization algorithm, salp swarm algorithm, grasshopper optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components. Mater Test 61(8):744–748

    Article  Google Scholar 

  34. Yu X, Qiao Y, Li Q, Xu G, Kang C, Estevez C, Deng C, Wang S (2020) Parallelizing comprehensive learning particle swarm optimization by open computing language on an integrated graphical processing unit. Complexity 28(6):101–118

    Google Scholar 

  35. Zhang B, Zheng YJ, Zhang MX, Chen SY (2015) Fireworks algorithm with enhanced fireworks interaction. IEEE/ACM Trans Comput Biol Bioinform 14(1):42–55

    Article  Google Scholar 

  36. Zhang Y, Zhou Y (2018) Distributed coordination control of traffic network flow using adaptive genetic algorithm based on cloud computing. J Netw Comput Appl 119:110–120

    Article  Google Scholar 

  37. Zhang Y, Lei X, Tan Y (2020) Application of fireworks algorithm in bioinformatics. In: Handbook of research on fireworks algorithms and swarm intelligence. IGI Global, pp 233–262

  38. Zhao Y, Liu H, Gao K (2021) An evacuation simulation method based on an improved artificial bee colony algorithm and a social force model. Appl Intell 51(1):100–123

    Article  Google Scholar 

  39. Zheng S, Yu C, Li J, Tan Y (2015a) Exponentially decreased dimension number strategy based dynamic search fireworks algorithm for solving cec2015 competition problems. In: 2015 IEEE congress on evolutionary computation (CEC). IEEE, pp 1083–1090

  40. Zheng YJ, Song Q, Chen SY (2013) Multiobjective fireworks optimization for variable-rate fertilization in oil crop production. Appl Soft Comput 13(11):4253–4263

    Article  Google Scholar 

  41. Zheng YJ, Xu XL, Ling HF, Chen SY (2015b) A hybrid fireworks optimization method with differential evolution operators. Neurocomputing 148:75–82

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 61973244, 61573277).

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Authors and Affiliations

  1. State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an, 710049, China

    Zhimin Qiao, Liangjun Ke, Gewei Zhang & Xiaoqiang Wang

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  1. Zhimin Qiao
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  2. Liangjun Ke
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  3. Gewei Zhang
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  4. Xiaoqiang Wang
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Correspondence to Liangjun Ke.

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Appendices

Appendix A: Test functions

To test the performance of the proposed IM-FWA in this paper, eight benchmark functions are selected for testing and analysis. Table 4 shows the benchmark functions, search scope and global minimum.

Table 4 Benchmark functions
Full size table

Appendix B: Comparison results of the benchmark functions

We evaluate the performance of the proposed IM-FWA and the CLPSO, FWA-EI, GA, IM, FWA, and NCS algorithms with eight benchmark functions, and the number of dimensions is 10. The quality of the generated solution is evaluated by the absolute error of the solution (the difference between the generated solution and the global optimal solution). Because the algorithms compared are random search algorithms, we execute each algorithm 30 times and then calculate the mean and standard deviation. The results are shown in Table 5. From the data in Table 5, it can be seen that GA has the worst optimization accuracy and low robustness. FEA-EI obtains the optimal value on the Alpine, Ackley and Schwegel functions. IM-FWA obtains the optimal value on the other five benchmark functions. It can be concluded that IM-FWA is better than the other six algorithms in terms of the optimization accuracy of the extremum function. Moreover, according to the standard deviation, FEA-EI obtains the minimum standard deviation on the Ackley functions. IM-FWA obtains the minimum standard deviation on the other seven benchmark functions. The performance of IM-FWA is more stable than that of the other six algorithms.

Table 5 Comparison of the optimization results on the benchmark functions
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Qiao, Z., Ke, L., Zhang, G. et al. Adaptive collaborative optimization of traffic network signal timing based on immune-fireworks algorithm and hierarchical strategy. Appl Intell 51, 6951–6967 (2021). https://doi.org/10.1007/s10489-021-02256-y

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