The Impact of Increasing Minor Arterial Flow on Arterial Coordination: An Analysis Based on MAXBAND Model

  • Liang XuEmail author
  • Lixiao Shen
  • Xiaobo Qu
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 185)


With the progress of urbanization, car ownership is experiencing explosive growth in China, which leads to heavy pressure on the urban road network. Arterial coordination strategy has been proved an effective method to avoid or alleviate traffic congestion. However, with the increasing proportion of flow on the minor arterial, arterial coordination efficiency might be affected. To figure out the problem, a numerical test is conducted by designing eight scenarios with different proportion of through movement and left turn flow on the minor arterials. MAXBAND model is applied for optimizing signal plans. The results show that average delay for vehicles on the arterials increases with the increasing of proportion of through movement flow, as well as the entire average delay. Average delay for vehicles on the minor arterials and two-way bandwidth decreases at same time. In other words, when the proportion of minor arterial flow increases, the arterial coordination efficiency would be reduced, especially for increasing left turn flow. This work reveals the improvement direction for arterial coordination.


Arterial coordination MAXBAND model Coordination efficiency 


  1. 1.
    Weng, J., Xue, S., Yang, Y., Yan, X., Qu, X.: In-depth analysis of drivers’ merging behaviour and rear-end crash risks in work zone merging areas. Accid. Anal. Prev. 77, 51–61 (2015)CrossRefGoogle Scholar
  2. 2.
    Xu, C., Yang, Y., Jin, S., Qu, Z., Hou, L.: Potential risk and its influencing factors for separated bicycle paths. Accid. Anal. Prev. 87, 59–67 (2016)CrossRefGoogle Scholar
  3. 3.
    Qu, X., Yu, Y., Zhou, M., Lin, C.T., Wang, X.: Jointly dampening traffic oscillations and improving energy consumption with electric, connected and automated vehicles: A reinforcement learning based approach. Appl. Energy 257, 114030 (2020)CrossRefGoogle Scholar
  4. 4.
    Zhou, M., Yu, Y., Qu, X.: Development of an efficient driving strategy for connected and automated vehicles at signalized intersections: A reinforcement learning approach. IEEE Trans. Intell. Transp. Syst. 21(1), 433–443 (2020)CrossRefGoogle Scholar
  5. 5.
    Easa, S.M., Qu, X., Dabbour, E.: Improved pedestrian sight distance needs at railroad-highway grade crossings. J. Transp. Eng. Part A: Syst. 143(7), 04017027 (2017)CrossRefGoogle Scholar
  6. 6.
    Bie, Y., Cheng, S., Easa, S., Qu, X.: Stop line set back at a signalized roundabout: A new concept for traffic operations. J. Transp. Eng. ASCE 142(3), 05016001 (2016)CrossRefGoogle Scholar
  7. 7.
    Jin, S., Qu, X., Zhou, D., Xu, C., Ma, D., Wang, D.: Estimating cycleway capacity and bicycle equivalent unit for electric bicycles. Transp. Res. Part A: Policy Pract. 77, 225–248 (2015)Google Scholar
  8. 8.
    Xu, J.: Traffic management and control, 1st edn. China Communications Press, Beijing (2007)Google Scholar
  9. 9.
    Morgan, J., Little, J.: Synchronizing traffic signals for maximal bandwidth. Oper. Res. 12(6), 896–912 (1964)CrossRefGoogle Scholar
  10. 10.
    Little, J., Kelson, M., Gartner, N.: MAXBAND: A program for setting signals on arteries and triangular networks. Transp. Res. Rec. 795, 40–46 (1981)Google Scholar
  11. 11.
    Chang, E., Cohen, S., Liu, C., Chaudhary, N., Messer, C.: MAXBAND-86: Program for optimizing left-turn phase sequence in multiarterial closed networks. Transp. Res. Rec. 1181, 61–67 (1988)Google Scholar
  12. 12.
    Gartner, N., Assmann, S., Lasaga, F., Hous, D.: MULTIBAND-a variable-bandwidth arterial progression scheme. Transp. Res. Rec. 1287, 212–222 (1990)Google Scholar
  13. 13.
    Stamatiadis, C., Gartner, N.: MULTIBAND-96: a program for variable-bandwidth progression optimization of multiarterial traffic networks. Transp. Res. Rec. 1554(1), 9–17 (1996)CrossRefGoogle Scholar
  14. 14.
    Lin, L., Tung, L., Ku, H.: Synchronized signal control model for maximizing progression along an arterial. J. Transp. Eng. 136(8), 727–735 (2009)CrossRefGoogle Scholar
  15. 15.
    Zhang, C., Xie, Y., Gartner, N., Stamatiadis, C., Arsava, T.: AM-band: an asymmetrical multi-band model for arterial traffic signal coordination. Transpo. Res. Part C: Emerg. Technol 58, 515–531 (2015)CrossRefGoogle Scholar
  16. 16.
    Yang, X., Cheng, Y., Chang, G.: A multi-path progression model for synchronization of arterial traffic signals. Transp. Res. part C: Emerg. Technol 53, 93–111 (2015)CrossRefGoogle Scholar
  17. 17.
    Zhang, L., Song, Z., Tang, X., Wang, D.: Signal coordination models for long arterials and grid networks. Transp. Res. Part C: Emerg. Technol. 71, 215–230 (2016)CrossRefGoogle Scholar
  18. 18.
    Cho, H., Huang, T., Huang, C.: Path-based MAXBAND with green-split variables and traffic dispersion. Transp. B: Transp. Dyn. 7(1), 726–740 (2019)Google Scholar
  19. 19.
    Ma, W., Zou, L., An, K., Gartner, N., Wang, M.: A partition-enabled multi-mode band approach to arterial traffic signal optimization. IEEE Trans. Intell. Transp. Syst. 20(1), 313–322 (2018)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.College of Civil Engineering and ArchitectureZhejiang UniversityHangzhou, ZhejiangChina
  2. 2.The Architectural Design and Research Institute of Zhejiang University Co. LtdHangzhou, ZhejiangChina
  3. 3.Department of Architecture and Civil EngineeringChalmers University of TechnologyGothenburgSweden

Personalised recommendations