Skip to main content

Advertisement

Log in

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

  • Published:
International Journal of Intelligent Transportation Systems Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

References

  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. Liu, Q., Xu, J.M.: Coordinated control model of regional traffic signals. JTTE. 12, 108–112 (2012)

    Google Scholar 

  3. Girianna, M., Benekohal, R.F.: Using genetic algorithms to design signal coordination for oversaturated networks. ITS J. 8, 117–129 (2004)

    MATH  Google Scholar 

  4. Wei, Y., Shao, Q.: Area traffic control model based on Q-learning and PSO. JSS. 23, 2108–2111 (2011)

    Google Scholar 

  5. Zhang, L.Z.: Study on Urban Area Traffic Control Strategy. Shandong University (2013)

  6. Le, H.C.: Urban Traffic Signal Coordinated Control Based on Sub Region Dynamic Partition. Zhejiang University of Technology (2013)

  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. 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):136–139

  9. Zhao, W.T.: Research on Several Urban Regional Traffic Control Technical. Zhejiang University (2013)

  10. Ma, X.H.: Research on Signal Control for Oversaturated State of Urban Road Traffic Networks. Beijing Jiaotong University (2016)

  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 approaches

  12. Geroliminis, N., Sun, J.: Properties of a well-defined macroscopic fundamental diagram for urban traffic. Transp. Res. B. 45(3), 605–617 (2011)

    Article  Google Scholar 

  13. Ma, Y.Y.: Study of Subnetwork-Oriented Traffic Signal Control Strategy. Tongji University, Shanghai (2010)

    Google Scholar 

  14. Daganzo, C.F.: Urban gridlock: macroscopic modeling and mitigation approaches. Transp. Res. B Methodol. 41(1), 49–62 (2007)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  20. Aboudolas, K., Geroliminis, N.: Perimeter flow control in heterogeneous networks. In: 13th Swiss Transport Research Conference, pp. 1055–1081 (2013)

    Google Scholar 

  21. Aboudolas, K., Geroliminis, N.: Perimeter and boundary flow control in multi-reservoirheterogeneous networks[J]. Transp. Res. B Methodol. 55, 265–281 (2013)

    Article  Google Scholar 

  22. Aboudolas, K., Geroliminis, N.: Feedback perimeter control for multi-region large-scale congested networks. European Control Conference. 2013, 106–114 (2013)

  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)

  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)

  25. Yoshii, T.: Evaluation of an area metering control method using the macroscopic fundamental diagram. WCTR. Lisbon, Portugal. 2010, 1–12

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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, 2017

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Xiaowen Yan.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yan, X., Xu, J. & Ma, Y. Bi-Level Optimization Model of Boundary Signal Control for the Network Based on Macroscopic Fundamental Diagrams. Int. J. ITS Res. 18, 113–121 (2020). https://doi.org/10.1007/s13177-019-00183-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13177-019-00183-4

Keywords

Navigation