IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0288969.html
   My bibliography  Save this article

Monitoring the elasticity of travel demand with respect to changes in the transport network for better policy decisions during disasters

Author

Listed:
  • Nur Diana Safitri
  • Makoto Chikaraishi

Abstract

When a disaster occurs, disaster management goes through a number of phases, namely normal, emergency response, adaptation, and recovery. Being able to identify the transition between these phases would be useful for policymakers, for example, in order to shift their focus from meeting the travel needs of affected people during the emergency response phase, to meeting travel needs for adaptation and recovery activities. This study proposes a data-driven method which may be useful for assessing phase transitions for transport management during a disaster. Specifically, we argue that changes in elasticities of travel demand with respect to changes in the transport network can be a useful indicator of phase transition, since they depict changes in consumers’ tastes, i.e., changes in the degree of travel necessity during disaster. Two hypotheses are formulated to investigate the changes in elasticity during a disaster: 1) the elasticity of travel demand is more elastic soon after a disaster as travel becomes a luxury good, and 2) it becomes less elastic afterwards as travel goes back to being a necessity good. To empirically confirm the hypotheses, we develop a multilevel log-log linear model, where the transport network service level information varying over time during a disaster is used as an explanatory variable, and tested mobile phone location and transport network data captured during the heavy rain disaster in Japan in July 2018. We also utilized a change point detection algorithm to identify a structural change that occurred in these elasticities. We confirm that our empirical results support our hypotheses, i.e., in the affected areas, the elasticity was more elastic soon after the disaster, while the elasticity tended to go back to normal around one month later. These results suggest that the proposed method can be useful to judge the phase transition for disaster management.

Suggested Citation

  • Nur Diana Safitri & Makoto Chikaraishi, 2023. "Monitoring the elasticity of travel demand with respect to changes in the transport network for better policy decisions during disasters," PLOS ONE, Public Library of Science, vol. 18(7), pages 1-18, July.
    • Handle: RePEc:plo:pone00:0288969
    DOI: 10.1371/journal.pone.0288969
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0288969
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0288969&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0288969?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
    ---><---

    References listed on IDEAS

    as
    1. Mai, Tien & Fosgerau, Mogens & Frejinger, Emma, 2015. "A nested recursive logit model for route choice analysis," Transportation Research Part B: Methodological, Elsevier, vol. 75(C), pages 100-112.
    2. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    3. Khan, Muhammad Tariq Iqbal & Anwar, Sofia, 2022. "Natural disasters and foreign exchange reserves: The role of renewable energy and human capital," Renewable Energy, Elsevier, vol. 192(C), pages 838-848.
    4. Hossain, Eklas & Roy, Shidhartho & Mohammad, Naeem & Nawar, Nafiu & Dipta, Debopriya Roy, 2021. "Metrics and enhancement strategies for grid resilience and reliability during natural disasters," Applied Energy, Elsevier, vol. 290(C).
    5. Fosgerau, Mogens & Frejinger, Emma & Karlstrom, Anders, 2013. "A link based network route choice model with unrestricted choice set," Transportation Research Part B: Methodological, Elsevier, vol. 56(C), pages 70-80.
    6. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Cools, Mario, 2018. "Investigating the impact of river floods on travel demand based on an agent-based modeling approach: The case of Liège, Belgium," Transport Policy, Elsevier, vol. 67(C), pages 102-110.
    7. Nicholson, Alan & Du, Zhen-Ping, 1997. "Degradable transportation systems: An integrated equilibrium model," Transportation Research Part B: Methodological, Elsevier, vol. 31(3), pages 209-223, June.
    8. Daniel Seaberg & Laura Devine & Jun Zhuang, 2017. "A review of game theory applications in natural disaster management research," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 89(3), pages 1461-1483, December.
    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. Mogens Fosgerau & Mads Paulsen & Thomas Kj{ae}r Rasmussen, 2021. "A perturbed utility route choice model," Papers 2103.13784, arXiv.org, revised Sep 2021.
    2. Tien Mai & The Viet Bui & Quoc Phong Nguyen & Tho V. Le, 2022. "Estimation of Recursive Route Choice Models with Incomplete Trip Observations," Papers 2204.12992, arXiv.org.
    3. Meyer de Freitas, Lucas & Becker, Henrik & Zimmermann, Maëlle & Axhausen, Kay W., 2019. "Modelling intermodal travel in Switzerland: A recursive logit approach," Transportation Research Part A: Policy and Practice, Elsevier, vol. 119(C), pages 200-213.
    4. Yao, Rui & Bekhor, Shlomo, 2022. "A variational autoencoder approach for choice set generation and implicit perception of alternatives in choice modeling," Transportation Research Part B: Methodological, Elsevier, vol. 158(C), pages 273-294.
    5. Cortés, Cristián E. & Donoso, Pedro & Gutiérrez, Leonel & Herl, Daniel & Muñoz, Diego, 2023. "A recursive stochastic transit equilibrium model estimated using passive data from Santiago, Chile," Transportation Research Part B: Methodological, Elsevier, vol. 174(C).
    6. Evanthia Kazagli & Michel Bierlaire & Matthieu de Lapparent, 2020. "Operational route choice methodologies for practical applications," Transportation, Springer, vol. 47(1), pages 43-74, February.
    7. Kazagli, Evanthia & Bierlaire, Michel & Flötteröd, Gunnar, 2016. "Revisiting the route choice problem: A modeling framework based on mental representations," Journal of choice modelling, Elsevier, vol. 19(C), pages 1-23.
    8. Mai, Tien & Yu, Xinlian & Gao, Song & Frejinger, Emma, 2021. "Routing policy choice prediction in a stochastic network: Recursive model and solution algorithm," Transportation Research Part B: Methodological, Elsevier, vol. 151(C), pages 42-58.
    9. Hung Tran & Tien Mai, 2023. "Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis," Papers 2306.04606, arXiv.org.
    10. Nassir, Neema & Hickman, Mark & Ma, Zhen-Liang, 2019. "A strategy-based recursive path choice model for public transit smart card data," Transportation Research Part B: Methodological, Elsevier, vol. 126(C), pages 528-548.
    11. Austin Knies & Jorge Lorca & Emerson Melo, 2020. "A Recursive Logit Model with Choice Aversion and Its Application to Transportation Networks," Papers 2010.02398, arXiv.org, revised Oct 2021.
    12. Yuki Oyama, 2023. "Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of locally perceived attributes," Papers 2307.08646, arXiv.org.
    13. van Oijen, Tim P. & Daamen, Winnie & Hoogendoorn, Serge P., 2020. "Estimation of a recursive link-based logit model and link flows in a sensor equipped network," Transportation Research Part B: Methodological, Elsevier, vol. 140(C), pages 262-281.
    14. Oyama, Yuki & Hato, Eiji, 2019. "Prism-based path set restriction for solving Markovian traffic assignment problem," Transportation Research Part B: Methodological, Elsevier, vol. 122(C), pages 528-546.
    15. Liu, Shan & Jiang, Hai, 2022. "Personalized route recommendation for ride-hailing with deep inverse reinforcement learning and real-time traffic conditions," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    16. Mai, Tien, 2016. "A method of integrating correlation structures for a generalized recursive route choice model," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 146-161.
    17. Oyama, Yuki, 2024. "Global path preference and local response: A reward decomposition approach for network path choice analysis in the presence of visually perceived attributes," Transportation Research Part A: Policy and Practice, Elsevier, vol. 181(C).
    18. Zhou, Bo & Liu, Ronghui, 2024. "A generalized rationally inattentive route choice model with non-uniform marginal information costs," Transportation Research Part B: Methodological, Elsevier, vol. 189(C).
    19. Oskar Blom Västberg & Anders Karlström & Daniel Jonsson & Marcus Sundberg, 2020. "A Dynamic Discrete Choice Activity-Based Travel Demand Model," Transportation Science, INFORMS, vol. 54(1), pages 21-41, January.
    20. Jiang, Gege & Fosgerau, Mogens & Lo, Hong K., 2020. "Route choice, travel time variability, and rational inattention," Transportation Research Part B: Methodological, Elsevier, vol. 132(C), pages 188-207.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0288969. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.