Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Development of platoon-based actuated signal control systems to coordinated intersections: application in corridors in Houston

© The Institution of Engineering and Technology
Received 17/05/2019, Accepted 10/10/2019, Revised 11/09/2019, Published 11/10/2019

A signal control method considering platoon-based actuation is presented with a focus on its application to two arterial corridors in the Houston Metropolitan Area, where the signal coordination is required for adjacent intersections. For the purpose of immediate implementation, the system was developed based on existing conventional loop detectors and control technologies used by Texas Department of Transportation. Simply based on volume and occupancy – the only traffic information reported by a conventional loop detector, it is impossible to precisely detect a platoon of vehicle approaching an intersection. As a compromise way, two specific logics were proposed to switch the system between the original coordinated control and the new platoon-based actuated control systems. At one testbed suffering from large left-turn traffic, the results revealed by the detector data before and after the implementation show that the model works well to relieve the delay for both left-turn traffic and through traffic on the major road. On another testbed where the platoon-based control is activated only when the through traffic is not high, some improvements were also observed. This method opens the door of wide application of the platoon-based signal control system (simply based on conventional loop detectors) to arterial corridors.

Inspec keywords: traffic information systems; transportation; traffic control; road traffic; traffic engineering computing; road traffic control; actuators; road vehicles

Other keywords: arterial corridors; platoon-based actuation; conventional loop detector; platoon-based actuated control systems; original coordinated control; platoon-based control; detector data; traffic information; Houston Metropolitan Area; coordinated intersections; left-turn traffic; signal coordination; platoon-based actuated signal control systems; platoon-based signal control system; control technologies; adjacent intersections; existing conventional loop detectors; signal control method

Subjects: Traffic engineering computing; Road-traffic system control; Systems theory applications in transportation; Computer communications

References

    1. 1)
      • 19. Lomax, T., Lasley, P., Ellis, D., et al: ‘Mobility investment priorities project’. Tech. Report, 2013.
    2. 2)
      • 13. Zhang, L., Li, H., Prevedouros, P.: ‘Signal control for oversaturated intersections using fuzzy logic’, Transp. Dev. Innov. Best Pract., 2008, 00, pp. 179–184.
    3. 3)
      • 16. Ma, S., Ying, L., Bao, L.: ‘Agent-based learning control method for urban traffic signal of single intersection’, J. Syst. Eng., 2002, 17, pp. 526–530.
    4. 4)
      • 20. Hadi, M., Wallae, C.: ‘Hybrid genetic algorithm to optimize signal phasing and timing’, Transp. Res. Rec., 1993, 1421, pp. 104–112.
    5. 5)
      • 1. Abbas, M., Bullock, D., Head, L.: ‘Real-time offset transitioning algorithm for coordinating traffic signals’, Transp. Res. Rec., 2001, 1748, pp. 26–39.
    6. 6)
      • 7. He, Q., Head, K., Ding, J.: ‘Pamscod: platoon-based arterial multimodal signal control with online data’, Transp. Res. C, 2012, 20, pp. 164–184.
    7. 7)
      • 2. Chaudhary, N., Messer, C., Gartner, N., et al: ‘Passer iv: a program for optimizing signal timing in grid networks. Discussion. Authors’ closure’, Transp. Res. Rec., 1993, 1421, pp. 82–93.
    8. 8)
      • 4. Yin, Y., Li, M., Skabardonis, A.: ‘Offline offset refiner for coordinated actuated signal control systems’, J. Transp. Eng., 2007, 133, pp. 423–432.
    9. 9)
      • 17. Ma, C., He, R.: ‘Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm’, Neural Comput. Appl., 2019, 31, (7), pp. 2073–2083.
    10. 10)
      • 14. Srinivasan, D., Choy, M., Cheu, R.: ‘Neural networks for realtime traffic signal control’, IEEE Trans. Intell. Transp., 2006, 7, pp. 261–272.
    11. 11)
      • 6. Lee, J.: ‘Assessing the potential benefits of intellidrive-based intersection control algorithms’. PhD dissertation, University of Virginia, Charlottesville, VA, 2015.
    12. 12)
      • 11. Khalid, M., Liang, S., Yusof, R.: ‘Control of a complex traffic junction using fuzzy inference’. 5th Asian Control Conf., Melbourne, Victoria, Australia, July 2004, pp. 1544–1551.
    13. 13)
      • 27. ASC: ‘Advanced system controller, ASC/3 programming manual’, 2019, Available at: https://webpages.uidaho.edu/signalperformance/Product_review/ASC3_Programming_Manual.pdf.
    14. 14)
      • 10. Ma, C., Hao, W., Wang, A., et al: ‘Developing a coordinated signal control system for urban ring road under the vehicle-infrastructure connected environment’, IEEE. Access., 2018, 6, pp. 52471–52478.
    15. 15)
      • 5. Koonce, P., Rodegerdts, L., Lee, K., et al: ‘Traffic Signal Timing Manual’, Final Report, FHWA-HOP-08-024, U.S. Department of Transportation, Washington, DC, USA, 2008Available at: http://ops.fhwa.dot.gov/publications/fhwahop08024/chapter5.htm.
    16. 16)
      • 21. SCATS: ‘Sydney coordinated adaptive traffic systems’, Available at: http://www.scats.com.au/.
    17. 17)
      • 28. ECONOLITE: ‘User manual’, 2018, Available at: https://www.econolite.com/products/controllers/.
    18. 18)
      • 3. Qi, L., Wu, A., Yang, X.: ‘A strategy on coordination control under saturated condition’, Proc. Soc. Behav. Sci., 2013, 96, pp. 1880–1889.
    19. 19)
      • 8. Goodall, N., Smith, B., Park, B.: ‘Traffic signal control with connected vehicles’, Transp. Res. Rec., 2013, 2381, pp. 65–72.
    20. 20)
      • 26. Wu, X., Yang, H., Adhikari, B., et al: ‘Implementation of proactive signal control system at multiple intersections at the greater Houston area’. Final Report of Project 5-6920, 2019, submitted to Texas Department of Transportation.
    21. 21)
      • 23. Yang, H., Haque, M., Wu, X.: ‘Connected vehicle-enabled proactive signal control for congestion mitigation on arterial corridors’. Proc. Transpt Res Board 97th Annual Meeting, Washington, DC, USA, January 2018, pp. 1–23.
    22. 22)
      • 12. Pranevicius, H., Kraujalis, T.: ‘Knowledge based traffic signal control model for signalized intersection’, Transport, 2012, 27, pp. 263–267.
    23. 23)
      • 22. Wu, X., Yang, H., Haque, M.: ‘Proactive traffic signal timing and coordination for congestion mitigation on arterial roads’. Final Report of Project 0-6920, 2017, submitted to Texas Department of Transportation.
    24. 24)
      • 18. Hao, W., Ma, C., Moghimi, B., et al: ‘Robust optimization of signal control parameters for unsaturated intersection based on tabu search-artificial bee colony algorithm’, IEEE. Access., 2018, 6, pp. 32015–32022.
    25. 25)
      • 9. Feng, Y., Head, K.L., Khoshmagham, S., et al: ‘A real-time adaptive signal control in a connected vehicle environment’, Transp. Res. C, 2015, 55, pp. 460–473.
    26. 26)
      • 25. Xie, X., van Lint, H., Verbraeck, A.: ‘A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters’, Transp. Res. C, 2018, 92, pp. 364–391.
    27. 27)
      • 24. Coifman, B.: ‘Estimating travel times and vehicle trajectories on freeways using dual loop detectors’, Transp. Res. A, 2002, 36, pp. 351–364.
    28. 28)
      • 15. Arel, I., Liu, C., Urbanik, T., et al: ‘Reinforcement learning-based multi-agent system for network traffic signal control’, IET Intell. Transp. Syst., 2010, 4, pp. 128–135.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-its.2019.0289
Loading

Related content

content/journals/10.1049/iet-its.2019.0289
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address