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Volume-Occupancy-Based Actuated Signal Control System: Design and Implementation to Diamond Interchanges in Houston

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Abstract

This paper presents a new volume-occupancy-based actuated signal control system for a diamond interchange, where a two-way arterial street intersects two one-way streets on each side of a freeway, aiming to approximately capture vehicle platoons and thus to improve the traffic by adaptively adjusting the plan. As a sponsored project aiming at immediate implementation, this system had to be designed based on conventional loop detectors and Econolite ASC/3-2100 Controller, which are currently used by the Texas Department of Transportation (TxDOT). Loop detectors placed in the pavement at each approach were used to collect real-time traffic volume and occupancy every 1 min. These data were then aggregated into 15-min flow and 15-min average occupancy, used in the proposed control logic to help vehicle platoons more smoothly passing through a diamond interchange. Based on onsite observations and data from loop detectors, it shows that the proposed system works well when the traffic flow is higher than 400 veh/h/ln. For arterial roads in the morning peak hours, the occupancy can be reduced by over 10% with a similar or even higher flow rate. The findings exhibit a low-cost method to improve traffic flow at a diamond interchange by using widely used conventional loop detectors.

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Availability of data and material

The real-traffic data used in this paper were collected by TxDOT Houston District Office for use in this project. The data are not shared publicly.

Code availability

PTV VISSIM was used in this project. This software has been licensed to Lamar University since 2013. The codes of data cleaning were prepared in PgSQL.

References

  1. Lee J (2000) Assessing the potential benefits of intellidrive-based intersection control algorithms, Ph.D. dissertation, University of Virginia, Charlottesville, VA. https://books.google.com/books?id=McjFZwEACAAJ. Accessed 9 July 2021

  2. He Q, Head LK, Ding J (2012) PAMSCOD: platoon-based arterial multimodal signal control with online data. Transp Res Part C 20(1):164–184

    Article  Google Scholar 

  3. Goodall N, Smith B, Park B (2013) Traffic signal control with connected vehicles. Transp Res Rec 2381:65–72

    Article  Google Scholar 

  4. Feng Y, Head K, Khoshmagham S, Zamanipour M (2015) A real-time adaptive signal control in a connected vehicle environment. Transp Res Part C 55:460–473

    Article  Google Scholar 

  5. Khalid M, Liang S, Yusof R (2004) Control of a complex traffic junction using fuzzy inference. In: 2004 5th Asian control conference (IEEE Cat. No.04EX904), vol 3, pp 1544–1551

  6. Pranevicius H, Kraujalis T (2012) Knowledge based traffic signal control model for signalized intersection. Transportation 27(3):263–267

    Article  Google Scholar 

  7. Zhang L, Li H, Prevedouros PD (2008) Signal control for over-saturated intersections using fuzzy logic. In: First international symposium on transportation and development innovative best practices, Beijing, China, 24–26 April 2008

  8. Srinivasan D, Choy M, Cheu R (2006) Neural networks for real-time traffic signal control. IEEE Trans Intell Transp 7(3):261–272

    Article  Google Scholar 

  9. Arel I, Liu C, Urbanik T, Kohls A (2010) Reinforcement learning-based multi-agent system for network traffic signal control. IET Intell Transport Syst 4(2):128–135

    Article  Google Scholar 

  10. Ma S, Ying L, Bao L (2002) Agent-based learning control method for urban traffic signal of single intersection. J Syst Eng 17:526–530

    Google Scholar 

  11. Ma C, He R (2015) Green wave traffic control system optimization based on adaptive genetic-artificial fish swarm algorithm. Neural Comput Appl 31:2073–2083

    Article  Google Scholar 

  12. Hao W, Ma C, Moghimi B, Fan Y (2018) Robust optimization of signal control parameters for unsaturated intersection based on Tabu search-artificial bee colony algorithm. IEEE Access 6:32015–32022

    Article  Google Scholar 

  13. Hadi M, Wallae C (1993) Hybrid genetic algorithm to optimize signal phasing and timing. Transp Res Rec 1421:104–112

    Google Scholar 

  14. Engelbrecht R, Barnes K (2003) Advanced traffic signal control for diamond interchanges. Transp Res Rec 1856(1):231–238

    Article  Google Scholar 

  15. Messer CJ, Chang M (1987) Traffic operations of basic traffic-actuated control systems at diamond interchanges. Transp Res Rec 1114:54–62

    Google Scholar 

  16. Nelson E, Bukkock D, Urbanik T (2000) Implementing actuated control of diamond interchanges. J Transp Eng 126(5):390–395

    Article  Google Scholar 

  17. Head L, Mirchandani P (1997) RHODES–ITMS. Report for Arizona Department of Transportation. Report #: AZ-SP-9701. http://apps.azdot.gov/adotlibrary/publications/SP/PDF/SP9701.pdf. Accessed 9 July 2021

  18. Wu X, Yang H, Haque M (2017) Proactive traffic signal timing and coordination for congestion mitigation on arterial roads. Final Report of Project 0-6920, Texas Department of Transportation. https://library.ctr.utexas.edu/hostedpdfs/lamar/0-6920-s.pdf. Accessed 9 July 2021

  19. Wu X, Yang H, Mainali B, Pokharel P, Chiu S (2019) Development of platoon-based actuated signal control systems to coordinated intersections: application in corridors in Houston. IET Intell Transp Syst 14(2):127–137

    Article  Google Scholar 

  20. Coifman B (2002) Estimating travel times and vehicle trajectories on freeways using dual loop detectors. Transp Res Part A 36(4):351–364

    Google Scholar 

  21. Xie X, van Lint H, Verbraeck A (2018) A generic data assimilation framework for vehicle trajectory reconstruction on signalized urban arterials using particle filters. Transp Res Part C 92:364–391

    Article  Google Scholar 

  22. Wu X, Yang H, Adhikari B, Mainali B, Pokharel P (2019) Implementation of proactive signal control system at multiple intersections at the Greater Houston Area. Final Report of Project 5-6920-01, Texas Department of Transportation. https://library.ctr.utexas.edu/hostedpdfs/lamar/5-6920-01-1.pdf. Accessed 9 July 2021

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Acknowledgements

This research was sponsored by the Texas Department of Transportation (TxDOT) (Project no. 5-6920). The authors would like to appreciate Darrin Jensen, the Project Manager, for his great support and coordination of this project. The authors resume sole responsibilities for the content expressed. The authors also would like to thank the editor and two anonymous reviewers for their valuable comments and suggestions.

Funding

This research was sponsored by TxDOT (Project NO. 5-6920).

Author information

Authors and Affiliations

  1. Department of Civil and Environmental Engineering, Lamar University, PO BOX 10024, Beaumont, TX, USA

    Xing Wu, Binod Adhikari & Shahin Sajjadi

  2. Texas Department of Transportation, Houston District Office, 7600 Washington Ave, Houston, TX, 77007, USA

    Steve Chiu

  3. Department of Civil Engineering, McMaster University, Hamilton, ON, Canada

    Hao Yang

  4. Wyoming Technology Transfer Center, University of Wyoming, Laramie, WY, USA

    Uttara Roy

Authors
  1. Xing Wu
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  2. Binod Adhikari
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  3. Steve Chiu
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  4. Hao Yang
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  5. Shahin Sajjadi
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  6. Uttara Roy
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Contributions

XW is the principal investigator (PI) of this project. He organized the whole research team, provided the research direction, developed the programming of data cleaning, and drafted this paper; BA conducted the modelling set-up, simulation, and data analysis; SC collected all traffic data; HY and SS analyzed the data to set up the travel demand matrices, which was used in traffic simulation; and UR helped cleaned the data.

Corresponding author

Correspondence to Xing Wu.

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Conflict of interest

To the best knowledge of the authors, no conflict of interest is found in this paper.

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This paper does not have any human or animal subjects involved. The involved research is NOT sensitive or classified.

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Wu, X., Adhikari, B., Chiu, S. et al. Volume-Occupancy-Based Actuated Signal Control System: Design and Implementation to Diamond Interchanges in Houston. Int J Civ Eng 20, 337–348 (2022). https://doi.org/10.1007/s40999-021-00666-0

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  • DOI: https://doi.org/10.1007/s40999-021-00666-0

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