Advertisement

Bi-Level Optimization Model of Boundary Signal Control for the Network Based on Macroscopic Fundamental Diagrams

  • Xiaowen YanEmail author
  • Jianmin Xu
  • Yingying Ma
Article
  • 11 Downloads

Abstract

This paper proposes a bi-level optimization model about boundary signal control for the network using macroscopic fundamental diagrams. Firstly, the road network is simplified as a directed graph, in which vertices and arcs respectively correspond to traffic zones and road segments connecting zones. Then a bi-level optimization model is proposed and the control objective is maximizing total output vehicles as well as keeping the existing number of vehicles optimal for each zone. The upper layer model optimizes transfer traffic flow among zones and the lower layer model optimizes the signal control scheme about boundary intersections. And a hybrid genetic simulated annealing algorithm is devised to solve the model. Finally, micro-simulation is used to test and verify the validity of proposed model. The results show that when the network congestion occurs, especially when traffic congestion is not uniform, the proposed model can improve the output vehicles for the whole network. Simultaneously, the existing number of vehicles in each zone can maintain at nearly optimal level. Thus verifying the effectiveness and feasibility of the boundary signal control model.

Keywords

Macroscopic fundamental diagram Bi-level optimization model Hybrid genetic simulated annealing algorithm Microscopic simulation 

Notes

References

  1. 1.
    Gao, Y.F., Hu, H., Han, H., Yang, X.G.: Multi-objective optimization and simulation for urban road intersection group traffic signal control. CJHT. 25, 129–135 (2012)Google Scholar
  2. 2.
    Liu, Q., Xu, J.M.: Coordinated control model of regional traffic signals. JTTE. 12, 108–112 (2012)Google Scholar
  3. 3.
    Girianna, M., Benekohal, R.F.: Using genetic algorithms to design signal coordination for oversaturated networks. ITS J. 8, 117–129 (2004)zbMATHGoogle Scholar
  4. 4.
    Wei, Y., Shao, Q.: Area traffic control model based on Q-learning and PSO. JSS. 23, 2108–2111 (2011)Google Scholar
  5. 5.
    Zhang, L.Z.: Study on Urban Area Traffic Control Strategy. Shandong University (2013)Google Scholar
  6. 6.
    Le, H.C.: Urban Traffic Signal Coordinated Control Based on Sub Region Dynamic Partition. Zhejiang University of Technology (2013)Google Scholar
  7. 7.
    Zhao, J., Ma, W.J., Wang, T., Liang, D.B.: Coordinated perimeter flow control for two subareas with macroscopic fundamental diagrams. J. Transp. Syst. Eng. Inf. Technol. 16(1), 78–84 (2016)Google Scholar
  8. 8.
    Zang, L. L. and Zhu, W. X. (2012). Study on Control Algorithm of Traffic Signals at Intersections Based on Optimizing Sub-area Traffic Flows. CJHT 25(6):136–139Google Scholar
  9. 9.
    Zhao, W.T.: Research on Several Urban Regional Traffic Control Technical. Zhejiang University (2013)Google Scholar
  10. 10.
    Ma, X.H.: Research on Signal Control for Oversaturated State of Urban Road Traffic Networks. Beijing Jiaotong University (2016)Google Scholar
  11. 11.
    C. F. Daganzo, “Urban gridlock: Macroscopic modeling and mitigation approaches., Transp. Res. B Methodol., Vol. 41 No. 1, 2007, pp. 22 49–62, Urban gridlock: Macroscopic modeling and mitigation approachesGoogle Scholar
  12. 12.
    Geroliminis, N., Sun, J.: Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transp. Res. B. 45(3), 605–617 (2011)CrossRefGoogle Scholar
  13. 13.
    Ma, Y.Y.: Study of Subnetwork-Oriented Traffic Signal Control Strategy. Tongji University, Shanghai (2010)Google Scholar
  14. 14.
    Daganzo, C.F.: Urban gridlock: macroscopic modeling and mitigation approaches. Transp. Res. B Methodol. 41(1), 49–62 (2007)CrossRefGoogle Scholar
  15. 15.
    Buisson, C., Ladier, C.: Exploring the impact of homogeneity of traffic measurements on the existence of macroscopic fundamental diagrams. Transp. Res. Rec. 38 Vol. No. 2124(1), 127–136 (2009)Google Scholar
  16. 16.
    He, Z., He, S., Guan, W.: A figure-eight hysteresis pattern in macroscopic fundamental diagrams and its microscopic causes. Transp Lett. 7(3), 133–142 (2015)CrossRefGoogle Scholar
  17. 17.
    Xie, X., Chiabaut, N., Leclercq, L.: Macroscopic fundamental diagram for urban streets and mixed traffic cross comparison of estimation methods. Transp. Res. Rec. 2390, 1–10 (2013)CrossRefGoogle Scholar
  18. 18.
    Ji, Y., Daamen, W., Hoogendoorn-Lanser, S., Qian, X.: Investigating the Shape of the Macroscopic Fundamental Diagram Using Simulation Data. Transp. Res. Rec. 2161(2010), 40–48 (2010)CrossRefGoogle Scholar
  19. 19.
    Stamos, I., Grau, J.M.S., Mitsakis, E., Mamarikas, S.: Macroscopic fundamental diagrams: simulation findings for thessaloniki’s road network. IJTTE. 5(3), 12–225 (2015)CrossRefGoogle Scholar
  20. 20.
    Aboudolas, K., Geroliminis, N.: Perimeter flow control in heterogeneous networks. In: 13th Swiss Transport Research Conference, pp. 1055–1081 (2013)Google Scholar
  21. 21.
    Aboudolas, K., Geroliminis, N.: Perimeter and boundary flow control in multi-reservoirheterogeneous networks[J]. Transp. Res. B Methodol. 55, 265–281 (2013)CrossRefGoogle Scholar
  22. 22.
    Aboudolas, K., Geroliminis, N.: Feedback perimeter control for multi-region large-scale congested networks. European Control Conference. 2013, 106–114 (2013)Google Scholar
  23. 23.
    Haddad, J., Ramezani, M., Geroliminis, N.: Model Predictive Perimeter Control for Urban Areas with Macroscopic Fundamental Diagrams, ACC, pp. 5757–5762, Montreal, June, 27–29 (2012)Google Scholar
  24. 24.
    Y. Zhang, Y. Bai, X.G. Yang, “Deadlock control strategy in urban road network. Journal of Highway in China, Nov,pp.96–102 (2010)Google Scholar
  25. 25.
    Yoshii, T.: Evaluation of an area metering control method using the macroscopic fundamental diagram. WCTR. Lisbon, Portugal. 2010, 1–12Google Scholar
  26. 26.
    Keyvan-Ekbatani, M., Kouvelas, A., Papamichail, I., Papageorgiou, M.: Exploiting the fundamental diagram of urban networks for feedback-based gating. Transp. Res. B Methodol. 46(10), 1393–1403 (2012)CrossRefGoogle Scholar
  27. 27.
    Wang, F.J., Wei, W., Wang, D.H., Qi, H.S.: Identifying and monitoring the traffic state in urban road network based on macroscopic fundamental diagram. In: Proceedings of Intelligent Transport Annual Meeting, China. , Sep, vol. 26, (2013)Google Scholar
  28. 28.
    Li, Y.S., Xu, J.M., Shen, L.: A perimeter control strategy for oversaturated network preventing queue spillback. Procedia. Soc. Behav. Sci. 43, 418–427 (2012)CrossRefGoogle Scholar
  29. 29.
    Y.Y. Ma, Y.L. Lv, J.M. Xu, X.W. Yan, “Optimizing traffic flow among traffic zones using macroscopic fundamental diagrams,” 17th CICTP, Shanghai, China, July 7–9, 2017Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of Transportation TechnologyZhejiang Institute of Mechanical and Electrical EngineeringHangzhouChina
  2. 2.School of Civil Engineering and TransportationSouth China University of TechnologyGuangzhouChina

Personalised recommendations