Abstract
In this chapter, a novel multi-objective optimization algorithm is investigated to deal with the issue of signal timing in an isolated traffic intersection aiming at releasing traffic congestion, reducing travel delay, maximizing the traffic flow, and minimizing pollution. The throughput maximum, stop times, and delay time of motorized traffic and non-motorized traffic are selected as the objectives of the optimization problem, and quantum computing is integrated with the genetic algorithm to obtain optimized traffic signal timing plan to upgrade the performance of intersection with faster convergence and higher accuracy. A numerical simulation study is conducted on MATLAB in this research work as a case study with a Non-dominated Sorting Quantum Genetic Algorithm (NSQGA), and the simulation results show that the proposed NSQGA algorithm performed superior to the conventional NSGA-II algorithm in effectively coordinating the traffic signal timing plan for an isolated intersection to improve the traffic capacity, efficiency, and safety of traffic system.
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References
F.V. Webster, B.M. Cobbe, Traffic signals. Road Research Technical Paper No. 56, Her Majesty’s Stationery Office 4(4), 206–207 (1966)
R. Akçelik, Traffic signals: capacity and timing analysis. Research Report. Publication of Australian Road Research Board (1981)
E. Zitzler, M. Laumanns, L. Thiele, Spea2: improving the strength Pareto evolutionary algorithm (2001)
C.A.C.C. Coello, G.T. Pulido, A micro-genetic algorithm for multiobjective optimization, in Evolutionary Multi-Criterion Optimization (EMO 2001), ed. by E. Zitzler, L. Thiele, K. Deb, C.A. Coello Coello, D. Corne. Lecture Notes in Computer Science, vol. 1993 (Springer, Berlin, Heidelberg, 2001), pp. 126–140
A. Jaszkiewicz, Genetic local search for multi-objective combinatorial optimization. Eur. J. Oper. Res. 137(1), 50–71 (2002)
A. Zhou, B.Y. Qu, H. Li et al., Multiobjective evolutionary algorithms: a survey of the state of the art. Swarm Evol. Comput. 1(1), 32–49 (2011)
C.H. Wang, S.W. Tsai, Multi-objective optimization using genetic algorithm: applications to imperfect preventive maintenance model, in Proceedings of International Conference on Computer Science & Education 2011 (IEEE, Singapore, Singapore 2011), pp. 1355–1360
N. Srinivas, K. Deb, Multiobjective function optimization using nondominated sorting genetic algorithms. IEEE Trans. Evol. Comput. 2(3), 1301–1308 (1994)
K. Deb, S. Agrawal, A. Pratap et al., A fast elitist non-dominated sorting genetic algorithm for multi-objective optimization: NSGA-II, in Parallel Problem Solving from Nature PPSN VI (PPSN 2000), ed. by M. Schoenauer et al. Lecture Notes in Computer Science, vol. 1917 (Springer, Berlin, Heidelberg, 2000), pp. 849–858
H. Ishibuchi, N. Tsukamoto, Y. Hitotsuyanagi et al., Effectiveness of scalability improvement attempts on the performance of NSGA-II for many-objective problems, in Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (ACM, Atlanta, GA, USA, 2008), pp. 649–656
C.T. Cao, J.M. Xu, Multi-object traffic signal control method for single intersection. Comput. Eng. Appl. 46(16), 20–22 (2010)
H. Hu, Y. Gao, X. Yang, Multi-objective optimization method of fixed-time signal control of isolated intersections, in International Conference on Computational and Information Sciences 2010 (IEEE, Chengdu, China, 2010), pp. 1281–1284
Y. Li, L.J. Yu, S.R. Tao et al., Multi-objective optimization of traffic signal timing for oversaturated intersection. Math. Probl. Eng. 2013(1683), 1–9 (2013)
L. Du, P. Jiao, H. Wang, A multi-objective traffic signal control model for intersection based on B-P neural networks, in Proceedings of the 10th Asia Pacific Transportation Development Conference (ASCE, Beijing, China, 2014), pp. 451–458
C.L. Hwang, K.P. Yoon, Multiple attribute decision making. Methods and applications: a state-of-the-art survey (Springer, Berlin Heidelberg, 1981)
Acknowledgements
This research is partially supported by the National Natural Science Foundation of China (No. 61703288).
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Qiao, F., Sun, H., Wang, Z., Tobi, F.A. (2018). NSQGA-Based Optimization of Traffic Signal in Isolated Intersection with Multiple Objectives. In: Zhu, Q., Na, J., Wu, X. (eds) Innovative Techniques and Applications of Modelling, Identification and Control. Lecture Notes in Electrical Engineering, vol 467. Springer, Singapore. https://doi.org/10.1007/978-981-10-7212-3_18
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DOI: https://doi.org/10.1007/978-981-10-7212-3_18
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