IDEAS home Printed from https://ideas.repec.org/a/eee/trapol/v67y2018icp102-110.html
   My bibliography  Save this article

Investigating the impact of river floods on travel demand based on an agent-based modeling approach: The case of Liège, Belgium

Author

Listed:
  • Saadi, Ismaïl
  • Mustafa, Ahmed
  • Teller, Jacques
  • Cools, Mario

Abstract

In Belgium, river floods are among the most frequent natural disasters and they may have important consequences on travel demand. In order to better understand how the travel patterns vary, we propose to set up a large scale scenario based on MATSim for guarantying an accurate assessment of the impact of river floods on the transportation system. As inputs, the current agent-based model requires a base year population. A synthetic population with respective set of attributes is generated as a key input. Afterwards, agents are assigned activity chains through an activity-based generation process. Finally, the synthetic population and the transportation network are integrated into MATSim. Regarding data, households travel surveys, OD matrix of Belgium have been used to set up the demand. For simulating river floods effects, a steady-state inundation map has been integrated within MATSim. In the current study, five scenarios have been tested where critical links are associated various levels of service, i.e. 10%, 25%, 50%, 75% and 100% (base case scenario). They are systematically compared to the standard scenario to estimate the deviations in terms of traffic patterns and travel times. The results suggest that compared to the standard scenario, the average trip travel time increased by 16.36%, 44.44%, 126.77% and 144.44% with respect to scenarios 75%, 50%, 25% and 10% respectively. Also, the traffic flows have been re-distributed more uniformly across the transportation network. Roads with important traffic volumes are subjected to a decrease of activity on the contrary of roads with low traffic volumes. A very few studies have focused on how river floods affect transportation systems, this paper provided new insights in term of methodology and traffic patterns analysis under disruptions.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:trapol:v:67:y:2018:i:c:p:102-110
    DOI: 10.1016/j.tranpol.2017.09.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0967070X16304425
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.tranpol.2017.09.009?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. Saadi, Ismaïl & Mustafa, Ahmed & Teller, Jacques & Farooq, Bilal & Cools, Mario, 2016. "Hidden Markov Model-based population synthesis," Transportation Research Part B: Methodological, Elsevier, vol. 90(C), pages 1-21.
    2. Novosel, T. & Perković, L. & Ban, M. & Keko, H. & Pukšec, T. & Krajačić, G. & Duić, N., 2015. "Agent based modelling and energy planning – Utilization of MATSim for transport energy demand modelling," Energy, Elsevier, vol. 92(P3), pages 466-475.
    3. David Charypar & Kai Nagel, 2005. "Generating complete all-day activity plans with genetic algorithms," Transportation, Springer, vol. 32(4), pages 369-397, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yu Han & Kevin Ash & Liang Mao & Zhong-Ren Peng, 2020. "An agent-based model for community flood adaptation under uncertain sea-level rise," Climatic Change, Springer, vol. 162(4), pages 2257-2276, October.

    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. Østergaard, P.A. & Lund, H. & Thellufsen, J.Z. & Sorknæs, P. & Mathiesen, B.V., 2022. "Review and validation of EnergyPLAN," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    2. Theo Arentze & Pauline van den Berg & Harry Timmermans, 2012. "Modeling Social Networks in Geographic Space: Approach and Empirical Application," Environment and Planning A, , vol. 44(5), pages 1101-1120, May.
    3. Martin Johnsen & Oliver Brandt & Sergio Garrido & Francisco C. Pereira, 2020. "Population synthesis for urban resident modeling using deep generative models," Papers 2011.06851, arXiv.org.
    4. Lucio Ciabattoni & Stefano Cardarelli & Marialaura Di Somma & Giorgio Graditi & Gabriele Comodi, 2021. "A Novel Open-Source Simulator Of Electric Vehicles in a Demand-Side Management Scenario," Energies, MDPI, vol. 14(6), pages 1-16, March.
    5. Gurumurthy, Krishna Murthy & Kockelman, Kara M., 2021. "Impacts of shared automated vehicles on airport access and operations, with opportunities for revenue recovery: Case Study of Austin, Texas," Research in Transportation Economics, Elsevier, vol. 90(C).
    6. Graabak, Ingeborg & Wu, Qiuwei & Warland, Leif & Liu, Zhaoxi, 2016. "Optimal planning of the Nordic transmission system with 100% electric vehicle penetration of passenger cars by 2050," Energy, Elsevier, vol. 107(C), pages 648-660.
    7. Jian Liu & Xiaosu Ma & Yi Zhu & Jing Li & Zong He & Sheng Ye, 2021. "Generating and Visualizing Spatially Disaggregated Synthetic Population Using a Web-Based Geospatial Service," Sustainability, MDPI, vol. 13(3), pages 1-16, February.
    8. Herc, Luka & Pfeifer, Antun & Duić, Neven & Wang, Fei, 2022. "Economic viability of flexibility options for smart energy systems with high penetration of renewable energy," Energy, Elsevier, vol. 252(C).
    9. Schwarz, Gregor & Bichler, Martin, 2022. "How to trade thirty thousand products: A wholesale market design for road capacity," Transportation Research Part A: Policy and Practice, Elsevier, vol. 164(C), pages 167-185.
    10. Zahra Navidi & Nicole Ronald & Stephan Winter, 2018. "Comparison between ad-hoc demand responsive and conventional transit: a simulation study," Public Transport, Springer, vol. 10(1), pages 147-167, May.
    11. Amit Agarwal & Benjamin Kickhöfer, 2018. "The correlation of externalities in marginal cost pricing: lessons learned from a real-world case study," Transportation, Springer, vol. 45(3), pages 849-873, May.
    12. Chang, Miguel & Lund, Henrik & Thellufsen, Jakob Zinck & Østergaard, Poul Alberg, 2023. "Perspectives on purpose-driven coupling of energy system models," Energy, Elsevier, vol. 265(C).
    13. Yazdanie, M. & Orehounig, K., 2021. "Advancing urban energy system planning and modeling approaches: Gaps and solutions in perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    14. Bellocchi, Sara & Manno, Michele & Noussan, Michel & Prina, Matteo Giacomo & Vellini, Michela, 2020. "Electrification of transport and residential heating sectors in support of renewable penetration: Scenarios for the Italian energy system," Energy, Elsevier, vol. 196(C).
    15. Hackney, Jeremy & Marchal, Fabrice, 2011. "A coupled multi-agent microsimulation of social interactions and transportation behavior," Transportation Research Part A: Policy and Practice, Elsevier, vol. 45(4), pages 296-309, May.
    16. Gunnar Flötteröd & Yu Chen & Kai Nagel, 2012. "Behavioral Calibration and Analysis of a Large-Scale Travel Microsimulation," Networks and Spatial Economics, Springer, vol. 12(4), pages 481-502, December.
    17. Seddig, Katrin & Jochem, Patrick & Fichtner, Wolf, 2017. "Integrating renewable energy sources by electric vehicle fleets under uncertainty," Energy, Elsevier, vol. 141(C), pages 2145-2153.
    18. Diego Viesi & Gregorio Borelli & Silvia Ricciuti & Giovanni Pernigotto & Md Shahriar Mahbub, 2024. "Modeling the Optimal Transition of an Urban Neighborhood towards an Energy Community and a Positive Energy District," Energies, MDPI, vol. 17(16), pages 1-30, August.
    19. Tan, Kang Miao & Ramachandaramurthy, Vigna K. & Yong, Jia Ying, 2016. "Optimal vehicle to grid planning and scheduling using double layer multi-objective algorithm," Energy, Elsevier, vol. 112(C), pages 1060-1073.
    20. Francesco Ciari & Milos Balac & Michael Balmer, 2015. "Modelling the effect of different pricing schemes on free-floating carsharing travel demand: a test case for Zurich, Switzerland," Transportation, Springer, vol. 42(3), pages 413-433, May.

    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:eee:trapol:v:67:y:2018:i:c:p:102-110. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/30473/description#description .

    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.