IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v15y2023i1p808-d1022721.html
   My bibliography  Save this article

LED Traffic Signal Repair and Replacement Practices

Author

Listed:
  • Morgan Westbrook

    (Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27606, USA)

  • William Rasdorf

    (Department of Civil, Construction, and Environmental Engineering, North Carolina State University, Raleigh, NC 27606, USA)

Abstract

Upgrading traffic signal systems from incandescent bulbs to LED modules over the last two decades has vastly improved the sustainability of this ubiquitous transportation asset. Recent technological upgrades have extended the warrantied life of these assets from 5 years to 15 years. With these advancements, it is vital that prioritization be given to sustainable operations and maintenance strategies which take advantage of the extended lifespan and continued reduction in energy consumption of LED modules. One major limiting factor in determining these strategies is that the service life of new 15-year-warrantied LED modules is currently unknown. Through available literature, this paper identifies the expected service life of 5-year-warrantied LED modules, commonly used from the early 2000s to 2022, as a baseline for comparison. Literature also provides insight into current Inspection, Repair, and Replacement practices. Interviews with manufacturers provide insight into current and future lifespan expectations. Finally, feedback from active transportation agencies provides examples of current practices in the absence of official national guidance, of which there is little. Understanding the current state of practice and expectations for the future will allow for the development of a repair and replacement guideline, ultimately taking maximum advantage of these advancements in sustainable technology.

Suggested Citation

  • Morgan Westbrook & William Rasdorf, 2023. "LED Traffic Signal Repair and Replacement Practices," Sustainability, MDPI, vol. 15(1), pages 1-18, January.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:1:p:808-:d:1022721
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/15/1/808/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/15/1/808/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Eva M. Urbano & Victor Martinez-Viol & Konstantinos Kampouropoulos & Luis Romeral, 2021. "Energy-Investment Decision-Making for Industry: Quantitative and Qualitative Risks Integrated Analysis," Sustainability, MDPI, vol. 13(12), pages 1-30, June.
    2. Maria Rella Riccardi & Francesco Galante & Antonella Scarano & Alfonso Montella, 2022. "Econometric and Machine Learning Methods to Identify Pedestrian Crash Patterns," Sustainability, MDPI, vol. 14(22), pages 1-19, November.
    3. Alaa H. Elwany & Nagi Z. Gebraeel & Lisa M. Maillart, 2011. "Structured Replacement Policies for Components with Complex Degradation Processes and Dedicated Sensors," Operations Research, INFORMS, vol. 59(3), pages 684-695, June.
    4. Alfonso Montella & Salvatore Chiaradonna & Alessandro Claudi de Saint Mihiel & Gord Lovegrove & Pietro Nunziante & Maria Rella Riccardi, 2022. "Sustainable Complete Streets Design Criteria and Case Study in Naples, Italy," Sustainability, MDPI, vol. 14(20), pages 1-27, October.
    5. Mohamed Gaha & Bilal Chabane & Dragan Komljenovic & Alain Côté & Claude Hébert & Olivier Blancke & Atieh Delavari & Georges Abdul-Nour, 2021. "Global Methodology for Electrical Utilities Maintenance Assessment Based on Risk-Informed Decision Making," Sustainability, MDPI, vol. 13(16), pages 1-23, August.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Natalia Distefano & Salvatore Leonardi & Nilda Georgina Liotta, 2023. "Walking for Sustainable Cities: Factors Affecting Users’ Willingness to Walk," Sustainability, MDPI, vol. 15(7), pages 1-18, March.
    2. Paulo Infante & Gonçalo Jacinto & Anabela Afonso & Leonor Rego & Pedro Nogueira & Marcelo Silva & Vitor Nogueira & José Saias & Paulo Quaresma & Daniel Santos & Patrícia Góis & Paulo Rebelo Manuel, 2023. "Factors That Influence the Type of Road Traffic Accidents: A Case Study in a District of Portugal," Sustainability, MDPI, vol. 15(3), pages 1-16, January.
    3. de Jonge, Bram & Teunter, Ruud & Tinga, Tiedo, 2017. "The influence of practical factors on the benefits of condition-based maintenance over time-based maintenance," Reliability Engineering and System Safety, Elsevier, vol. 158(C), pages 21-30.
    4. Qiu, Qingan & Cui, Lirong, 2019. "Gamma process based optimal mission abort policy," Reliability Engineering and System Safety, Elsevier, vol. 190(C), pages 1-1.
    5. Alaswad, Suzan & Xiang, Yisha, 2017. "A review on condition-based maintenance optimization models for stochastically deteriorating system," Reliability Engineering and System Safety, Elsevier, vol. 157(C), pages 54-63.
    6. Liu, Bin & Liang, Zhenglin & Parlikad, Ajith Kumar & Xie, Min & Kuo, Way, 2017. "Condition-based maintenance for systems with aging and cumulative damage based on proportional hazards model," Reliability Engineering and System Safety, Elsevier, vol. 168(C), pages 200-209.
    7. Liu, Bin & Wu, Shaomin & Xie, Min & Kuo, Way, 2017. "A condition-based maintenance policy for degrading systems with age- and state-dependent operating cost," European Journal of Operational Research, Elsevier, vol. 263(3), pages 879-887.
    8. Tiedo Tinga & Rene Janssen, 2013. "The interplay between deployment and optimal maintenance intervals for complex multi-component systems," Journal of Risk and Reliability, , vol. 227(3), pages 227-240, June.
    9. Najafi, Seyedvahid & Zheng, Rui & Lee, Chi-Guhn, 2021. "An optimal opportunistic maintenance policy for a two-unit series system with general repair using proportional hazards models," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
    10. Deep, Akash & Zhou, Shiyu & Veeramani, Dharmaraj & Chen, Yong, 2023. "Partially observable Markov decision process-based optimal maintenance planning with time-dependent observations," European Journal of Operational Research, Elsevier, vol. 311(2), pages 533-544.
    11. Wang, Xiaolin & Balakrishnan, Narayanaswamy & Guo, Bo, 2014. "Residual life estimation based on a generalized Wiener degradation process," Reliability Engineering and System Safety, Elsevier, vol. 124(C), pages 13-23.
    12. Michael Jong Kim, 2016. "Robust Control of Partially Observable Failing Systems," Operations Research, INFORMS, vol. 64(4), pages 999-1014, August.
    13. Zhu, Zhicheng & Xiang, Yisha & Zhao, Ming & Shi, Yue, 2023. "Data-driven remanufacturing planning with parameter uncertainty," European Journal of Operational Research, Elsevier, vol. 309(1), pages 102-116.
    14. Liu, Bin & Pandey, Mahesh D. & Wang, Xiaolin & Zhao, Xiujie, 2021. "A finite-horizon condition-based maintenance policy for a two-unit system with dependent degradation processes," European Journal of Operational Research, Elsevier, vol. 295(2), pages 705-717.
    15. Zhang, Zhengxin & Si, Xiaosheng & Hu, Changhua & Lei, Yaguo, 2018. "Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods," European Journal of Operational Research, Elsevier, vol. 271(3), pages 775-796.
    16. Lu Jin & Undarmaa Bayarsaikhan & Kazuyuki Suzuki, 2016. "Optimal control limit policy for age-dependent deteriorating systems under incomplete observations," Journal of Risk and Reliability, , vol. 230(1), pages 34-43, February.
    17. Mosayebi Omshi, E. & Grall, A. & Shemehsavar, S., 2020. "A dynamic auto-adaptive predictive maintenance policy for degradation with unknown parameters," European Journal of Operational Research, Elsevier, vol. 282(1), pages 81-92.
    18. Zhaoming Chen & Wenyuan Xu & Youyang Qu, 2023. "Joint Analysis of Crash Frequency by Severity Based on a Random Parameters Approach," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
    19. Andersen, Jesper Fink & Andersen, Anders Reenberg & Kulahci, Murat & Nielsen, Bo Friis, 2022. "A numerical study of Markov decision process algorithms for multi-component replacement problems," European Journal of Operational Research, Elsevier, vol. 299(3), pages 898-909.
    20. de Pater, Ingeborg & Mitici, Mihaela, 2021. "Predictive maintenance for multi-component systems of repairables with Remaining-Useful-Life prognostics and a limited stock of spare components," Reliability Engineering and System Safety, Elsevier, vol. 214(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:15:y:2023:i:1:p:808-:d:1022721. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.