IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v17y2025i3p826-d1572460.html
   My bibliography  Save this article

Weigh-In-Motion Placement for Overloaded Truck Enforcement Considering Traffic Loadings and Disruptions

Author

Listed:
  • Yunkyeong Jung

    (Graduate School of Green Growth and Sustainability, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea)

  • Daijiro Mizutani

    (Department of Civil and Environmental Engineering, Tohoku University, Aoba, Sendai 980-8579, Miyagi, Japan)

  • Jinwoo Lee

    (Department of Civil and Environmental Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, Republic of Korea)

Abstract

Overloaded trucks directly contribute to road infrastructure deterioration and undermine safety, posing significant challenges to sustainability. This makes enforcement to reduce their numbers and impacts essential. Weigh-in-motion (WIM) systems use road-embedded sensors to measure truck weights and enforce regulations. However, WIM cannot be installed on all routes, and some overloaded truck drivers can detour to avoid them instead of giving up overloading if the detour penalty is still lower than the extra profit from overloading. This paper focuses on optimal WIM location planning for overloaded truck management, incorporating a demand shift and user equilibrium model based on the utility functions of overloaded and non-overloaded trucks. The presented framework includes an upper-level problem for WIM placement and a lower-level problem for demand shifts and traffic assignments among overloaded trucks, non-overloaded trucks, and light-duty vehicles for a given WIM placement. Particularly, at the upper level, the primary objective is to minimize the traffic loadings, i.e., the expected equivalent single-axle load–kilometers per unit time, with the secondary objective of minimizing the total traffic disruptions over the target network. Simulations and sensitivity analyses are conducted through a numerical example. Consequently, this study proposes an optimal WIM placement framework that considers drivers’ utility-based route choice and social costs such as ESAL and traffic congestion.

Suggested Citation

  • Yunkyeong Jung & Daijiro Mizutani & Jinwoo Lee, 2025. "Weigh-In-Motion Placement for Overloaded Truck Enforcement Considering Traffic Loadings and Disruptions," Sustainability, MDPI, vol. 17(3), pages 1-14, January.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:3:p:826-:d:1572460
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/17/3/826/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/17/3/826/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Michel Gendreau & Gilbert Laporte & Isabelle Parent, 2000. "Heuristics for the location of inspection stations on a network," Naval Research Logistics (NRL), John Wiley & Sons, vol. 47(4), pages 287-303, June.
    2. Bogyrbayeva, Aigerim & Kwon, Changhyun, 2021. "Pessimistic evasive flow capturing problems," European Journal of Operational Research, Elsevier, vol. 293(1), pages 133-148.
    3. Marković, Nikola & Ryzhov, Ilya O. & Schonfeld, Paul, 2017. "Evasive flow capture: A multi-period stochastic facility location problem with independent demand," European Journal of Operational Research, Elsevier, vol. 257(2), pages 687-703.
    4. F. Hooshmand & S. A. MirHassani, 2018. "Reduction of nonanticipativity constraints in multistage stochastic programming problems with endogenous and exogenous uncertainty," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 87(1), pages 1-18, February.
    5. Lee, Jinwoo & Madanat, Samer, 2015. "A joint bottom-up solution methodology for system-level pavement rehabilitation and reconstruction," Transportation Research Part B: Methodological, Elsevier, vol. 78(C), pages 106-122.
    6. Oded Berman & Richard C. Larson & Nikoletta Fouska, 1992. "Optimal Location of Discretionary Service Facilities," Transportation Science, INFORMS, vol. 26(3), pages 201-211, August.
    7. Marguerite Frank & Philip Wolfe, 1956. "An algorithm for quadratic programming," Naval Research Logistics Quarterly, John Wiley & Sons, vol. 3(1‐2), pages 95-110, March.
    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. Bogyrbayeva, Aigerim & Kwon, Changhyun, 2021. "Pessimistic evasive flow capturing problems," European Journal of Operational Research, Elsevier, vol. 293(1), pages 133-148.
    2. Guillaume Sagnol & Edouard Pauwels, 2019. "An unexpected connection between Bayes A-optimal designs and the group lasso," Statistical Papers, Springer, vol. 60(2), pages 565-584, April.
    3. Abdelfettah Laouzai & Rachid Ouafi, 2022. "A prediction model for atmospheric pollution reduction from urban traffic," Environment and Planning B, , vol. 49(2), pages 566-584, February.
    4. Chung, Sung Hoon & Kwon, Changhyun, 2015. "Multi-period planning for electric car charging station locations: A case of Korean Expressways," European Journal of Operational Research, Elsevier, vol. 242(2), pages 677-687.
    5. Chou, Chang-Chi & Chiang, Wen-Chu & Chen, Albert Y., 2022. "Emergency medical response in mass casualty incidents considering the traffic congestions in proximity on-site and hospital delays," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 158(C).
    6. Schulz, Arne & Boysen, Nils & Briskorn, Dirk, 2024. "Centrally-chosen versus user-selected swaps: How the selection of swapping stations impacts standby battery inventories," European Journal of Operational Research, Elsevier, vol. 319(3), pages 726-738.
    7. Francesco Rinaldi & Damiano Zeffiro, 2023. "Avoiding bad steps in Frank-Wolfe variants," Computational Optimization and Applications, Springer, vol. 84(1), pages 225-264, January.
    8. Beck, Yasmine & Ljubić, Ivana & Schmidt, Martin, 2023. "A survey on bilevel optimization under uncertainty," European Journal of Operational Research, Elsevier, vol. 311(2), pages 401-426.
    9. Tiến-Sơn Phạm, 2019. "Optimality Conditions for Minimizers at Infinity in Polynomial Programming," Management Science, INFORMS, vol. 44(4), pages 1381-1395, November.
    10. Correia, Isabel & Melo, Teresa, 2019. "Dynamic facility location problem with modular capacity adjustments under uncertainty," Technical Reports on Logistics of the Saarland Business School 17, Saarland University of Applied Sciences (htw saar), Saarland Business School.
    11. Filippozzi, Rafaela & Gonçalves, Douglas S. & Santos, Luiz-Rafael, 2023. "First-order methods for the convex hull membership problem," European Journal of Operational Research, Elsevier, vol. 306(1), pages 17-33.
    12. Ke, Ginger Y. & Zhang, Huiwen & Bookbinder, James H., 2020. "A dual toll policy for maintaining risk equity in hazardous materials transportation with fuzzy incident rate," International Journal of Production Economics, Elsevier, vol. 227(C).
    13. Friesz, Terry L. & Tourreilles, Francisco A. & Han, Anthony Fu-Wha, 1979. "Multi-Criteria Optimization Methods in Transport Project Evaluation: The Case of Rural Roads in Developing Countries," Transportation Research Forum Proceedings 1970s 318817, Transportation Research Forum.
    14. Giovanni Pantuso, 2021. "A node formulation for multistage stochastic programs with endogenous uncertainty," Computational Management Science, Springer, vol. 18(3), pages 325-354, July.
    15. Damian Clarke & Daniel Paila~nir & Susan Athey & Guido Imbens, 2023. "Synthetic Difference In Differences Estimation," Papers 2301.11859, arXiv.org, revised Feb 2023.
    16. Fabiana R. Oliveira & Orizon P. Ferreira & Gilson N. Silva, 2019. "Newton’s method with feasible inexact projections for solving constrained generalized equations," Computational Optimization and Applications, Springer, vol. 72(1), pages 159-177, January.
    17. Ali Fattahi & Sriram Dasu & Reza Ahmadi, 2019. "Mass Customization and “Forecasting Options’ Penetration Rates Problem”," Operations Research, INFORMS, vol. 67(4), pages 1120-1134, July.
    18. Pokojovy, Michael & Jobe, J. Marcus, 2022. "A robust deterministic affine-equivariant algorithm for multivariate location and scatter," Computational Statistics & Data Analysis, Elsevier, vol. 172(C).
    19. Wei-jie Cong & Le Wang & Hui Sun, 2020. "Rank-two update algorithm versus Frank–Wolfe algorithm with away steps for the weighted Euclidean one-center problem," Computational Optimization and Applications, Springer, vol. 75(1), pages 237-262, January.
    20. Kuby, Michael & Lim, Seow, 2005. "The flow-refueling location problem for alternative-fuel vehicles," Socio-Economic Planning Sciences, Elsevier, vol. 39(2), pages 125-145, June.

    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:17:y:2025:i:3:p:826-:d:1572460. 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.