IDEAS home Printed from https://ideas.repec.org/a/spr/annopr/v320y2023i2d10.1007_s10479-022-04779-0.html
   My bibliography  Save this article

Designing higher value roads to preserve species at risk by optimally controlling traffic flow

Author

Listed:
  • Nicholas Davey

    (The University of Melbourne)

  • Nicolas Langrené

    (BNU-HKBU United International College)

  • Wen Chen

    (CSIRO’s Data61)

  • Jonathan R. Rhodes

    (The University of Queensland)

  • Simon Dunstall

    (CSIRO’s Data61)

  • Saman Halgamuge

    (The University of Melbourne)

Abstract

The construction and operation of linear infrastructure has major impacts on biodiversity through loss of habitat, increased mortality and loss of connectivity. In particular, minimising the impact of roads which pass through ecologically sensitive areas on surrounding species at the construction and operational phases is critical for conservation. However, potential impacts are rarely known perfectly at the construction phase and early in the operational phase. To address this problem, a company could build flexibility into road operation so that it can respond rapidly to future ecological impacts if necessary. In this paper we analyse the value of this flexibility using stochastic dynamic programming and use the results to guide a global search algorithm to find high value roads in the region. We consider flexibility in terms of the proportion of traffic volume routed along the road, with the remainder passing along an existing higher-cost, lower-impact road. We applied this to an example scenario where a road must be routed through a region with a vulnerable species present. By incorporating flexibility, the proposed model was able to find a road that met a desired ending population of animals and was more valuable than roads found under existing design alternatives.

Suggested Citation

  • Nicholas Davey & Nicolas Langrené & Wen Chen & Jonathan R. Rhodes & Simon Dunstall & Saman Halgamuge, 2023. "Designing higher value roads to preserve species at risk by optimally controlling traffic flow," Annals of Operations Research, Springer, vol. 320(2), pages 663-693, January.
  • Handle: RePEc:spr:annopr:v:320:y:2023:i:2:d:10.1007_s10479-022-04779-0
    DOI: 10.1007/s10479-022-04779-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10479-022-04779-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s10479-022-04779-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," The Review of Financial Studies, Society for Financial Studies, vol. 14(1), pages 113-147.
    2. Carriere, Jacques F., 1996. "Valuation of the early-exercise price for options using simulations and nonparametric regression," Insurance: Mathematics and Economics, Elsevier, vol. 19(1), pages 19-30, December.
    3. Kitapbayev, Yerkin & Moriarty, John & Mancarella, Pierluigi, 2015. "Stochastic control and real options valuation of thermal storage-enabled demand response from flexible district energy systems," Applied Energy, Elsevier, vol. 137(C), pages 823-831.
    4. Jonathan R Rhodes & Daniel Lunney & John Callaghan & Clive A McAlpine, 2014. "A Few Large Roads or Many Small Ones? How to Accommodate Growth in Vehicle Numbers to Minimise Impacts on Wildlife," PLOS ONE, Public Library of Science, vol. 9(3), pages 1-10, March.
    5. Lenos Trigeorgis, 1993. "Real Options and Interactions With Financial Flexibility," Financial Management, Financial Management Association, vol. 22(3), Fall.
    6. repec:dau:papers:123456789/12195 is not listed on IDEAS
    7. Zhiyi Shen & Chengguo Weng, 2019. "A Backward Simulation Method for Stochastic Optimal Control Problems," Papers 1901.06715, arXiv.org.
    8. Nadarajah, Selvaprabu & Margot, François & Secomandi, Nicola, 2017. "Comparison of least squares Monte Carlo methods with applications to energy real options," European Journal of Operational Research, Elsevier, vol. 256(1), pages 196-204.
    9. Schwartz, Eduardo S, 1997. "The Stochastic Behavior of Commodity Prices: Implications for Valuation and Hedging," Journal of Finance, American Finance Association, vol. 52(3), pages 923-973, July.
    10. Crombecq, K. & Laermans, E. & Dhaene, T., 2011. "Efficient space-filling and non-collapsing sequential design strategies for simulation-based modeling," European Journal of Operational Research, Elsevier, vol. 214(3), pages 683-696, November.
    11. Longstaff, Francis A & Schwartz, Eduardo S, 2001. "Valuing American Options by Simulation: A Simple Least-Squares Approach," University of California at Los Angeles, Anderson Graduate School of Management qt43n1k4jb, Anderson Graduate School of Management, UCLA.
    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. Juri Hinz & Tanya Tarnopolskaya & Jeremy Yee, 2020. "Efficient algorithms of pathwise dynamic programming for decision optimization in mining operations," Annals of Operations Research, Springer, vol. 286(1), pages 583-615, March.
    2. Masci, Martín Ezequiel, 2012. "Irreversibilidad e incertidumbre de las decisiones financieras en i&d [Irreversibility and uncertainty of the financial investments on r&d]," MPRA Paper 40970, University Library of Munich, Germany.
    3. Lee, Sangmin & Boomsma, Trine Krogh, 2022. "An approximate dynamic programming algorithm for short-term electric vehicle fleet operation under uncertainty," Applied Energy, Elsevier, vol. 325(C).
    4. Armstrong, Margaret & Langrené, Nicolas & Petter, Renato & Chen, Wen & Petter, Carlos, 2019. "Accounting for tailings dam failures in the valuation of mining projects," Resources Policy, Elsevier, vol. 63(C), pages 1-1.
    5. Wei, Wei & Zhu, Dan, 2022. "Generic improvements to least squares monte carlo methods with applications to optimal stopping problems," European Journal of Operational Research, Elsevier, vol. 298(3), pages 1132-1144.
    6. Leonardo Kanashiro Felizardo & Elia Matsumoto & Emilio Del-Moral-Hernandez, 2022. "Solving the optimal stopping problem with reinforcement learning: an application in financial option exercise," Papers 2208.00765, arXiv.org.
    7. Sebastian Maier, 2021. "Re-evaluating natural resource investments under uncertainty: An alternative to limited traditional approaches," Annals of Operations Research, Springer, vol. 299(1), pages 907-937, April.
    8. Ivan Guo & Nicolas Langrené & Gregoire Loeper & Wei Ning, 2020. "Robust utility maximization under model uncertainty via a penalization approach," Working Papers hal-02910261, HAL.
    9. Work, James & Hauer, Grant & Luckert, M.K. (Marty), 2018. "What ethanol prices would induce growers to switch from agriculture to poplar in Alberta? A multiple options approach," Journal of Forest Economics, Elsevier, vol. 33(C), pages 51-62.
    10. Stentoft, Lars, 2005. "Pricing American options when the underlying asset follows GARCH processes," Journal of Empirical Finance, Elsevier, vol. 12(4), pages 576-611, September.
    11. Marcelo G. Figueroa, 2006. "Pricing Multiple Interruptible-Swing Contracts," Birkbeck Working Papers in Economics and Finance 0606, Birkbeck, Department of Economics, Mathematics & Statistics.
    12. Abdullah Almansour and Margaret Insley, 2016. "The Impact of Stochastic Extraction Cost on the Value of an Exhaustible Resource: An Application to the Alberta Oil Sands," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2).
    13. Jungmin An & Dong-Kwan Kim & Jinyeong Lee & Sung-Kwan Joo, 2021. "Least Squares Monte Carlo Simulation-Based Decision-Making Method for Photovoltaic Investment in Korea," Sustainability, MDPI, vol. 13(19), pages 1-14, September.
    14. Denis Belomestny & Grigori Milstein & Vladimir Spokoiny, 2009. "Regression methods in pricing American and Bermudan options using consumption processes," Quantitative Finance, Taylor & Francis Journals, vol. 9(3), pages 315-327.
    15. Seiji Harikae & James S. Dyer & Tianyang Wang, 2021. "Valuing Real Options in the Volatile Real World," Production and Operations Management, Production and Operations Management Society, vol. 30(1), pages 171-189, January.
    16. Calypso Herrera & Florian Krach & Pierre Ruyssen & Josef Teichmann, 2021. "Optimal Stopping via Randomized Neural Networks," Papers 2104.13669, arXiv.org, revised Dec 2023.
    17. Yuval Arbel & Danny Ben-Shahar & Eyal Sulganik, 2009. "Mean Reversion and Momentum: Another Look at the Price-Volume Correlation in the Real Estate Market," The Journal of Real Estate Finance and Economics, Springer, vol. 39(3), pages 316-335, October.
    18. Rongju Zhang & Nicolas Langren'e & Yu Tian & Zili Zhu & Fima Klebaner & Kais Hamza, 2018. "Local Control Regression: Improving the Least Squares Monte Carlo Method for Portfolio Optimization," Papers 1803.11467, arXiv.org, revised Sep 2018.
    19. Luis M. Abadie & José M. Chamorro, 2009. "Monte Carlo valuation of natural gas investments," Review of Financial Economics, John Wiley & Sons, vol. 18(1), pages 10-22, January.
    20. Lim, Terence & Lo, Andrew W. & Merton, Robert C. & Scholes, Myron S., 2006. "The Derivatives Sourcebook," Foundations and Trends(R) in Finance, now publishers, vol. 1(5–6), pages 365-572, April.

    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:spr:annopr:v:320:y:2023:i:2:d:10.1007_s10479-022-04779-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.