IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2310.14380.html
   My bibliography  Save this paper

Modeling Link-level Road Traffic Resilience to Extreme Weather Events Using Crowdsourced Data

Author

Listed:
  • Songhua Hu

    (Jack)

  • Kailai Wang

    (Jack)

  • Lingyao Li

    (Jack)

  • Yingrui Zhao

    (Jack)

  • Zhenbing He

    (Jack)

  • Yunpeng

    (Jack)

  • Zhang

Abstract

Climate changes lead to more frequent and intense weather events, posing escalating risks to road traffic. Crowdsourced data offer new opportunities to monitor and investigate changes in road traffic flow during extreme weather. This study utilizes diverse crowdsourced data from mobile devices and the community-driven navigation app, Waze, to examine the impact of three weather events (i.e., floods, winter storms, and fog) on road traffic. Three metrics, speed change, event duration, and area under the curve (AUC), are employed to assess link-level traffic change and recovery. In addition, a user's perceived severity is computed to evaluate link-level weather impact based on crowdsourced reports. This study evaluates a range of new data sources, and provides insights into the resilience of road traffic to extreme weather, which are crucial for disaster preparedness, response, and recovery in road transportation systems.

Suggested Citation

  • Songhua Hu & Kailai Wang & Lingyao Li & Yingrui Zhao & Zhenbing He & Yunpeng & Zhang, 2023. "Modeling Link-level Road Traffic Resilience to Extreme Weather Events Using Crowdsourced Data," Papers 2310.14380, arXiv.org.
    • Handle: RePEc:arx:papers:2310.14380
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2310.14380
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pan, Shouzheng & Yan, Hai & He, Jia & He, Zhengbing, 2021. "Vulnerability and resilience of transportation systems: A recent literature review," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    2. Li, Lingyao & Bensi, Michelle & Cui, Qingbin & Baecher, Gregory B. & Huang, You, 2021. "Social media crowdsourcing for rapid damage assessment following a sudden-onset natural hazard event," International Journal of Information Management, Elsevier, vol. 60(C).
    3. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    4. Datla, Sandeep & Sharma, Satish, 2008. "Impact of cold and snow on temporal and spatial variations of highway traffic volumes," Journal of Transport Geography, Elsevier, vol. 16(5), pages 358-372.
    5. Boyeong Hong & Bartosz J. Bonczak & Arpit Gupta & Constantine E. Kontokosta, 2021. "Measuring inequality in community resilience to natural disasters using large-scale mobility data," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
    6. Xing, Jiping & Wu, Wei & Cheng, Qixiu & Liu, Ronghui, 2022. "Traffic state estimation of urban road networks by multi-source data fusion: Review and new insights," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 595(C).
    7. Markolf, Samuel A. & Hoehne, Christopher & Fraser, Andrew & Chester, Mikhail V. & Underwood, B. Shane, 2019. "Transportation resilience to climate change and extreme weather events – Beyond risk and robustness," Transport Policy, Elsevier, vol. 74(C), pages 174-186.
    8. Shraddha Praharaj & T. Donna Chen & Faria T. Zahura & Madhur Behl & Jonathan L. Goodall, 2021. "Estimating impacts of recurring flooding on roadway networks: a Norfolk, Virginia case study," 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. 107(3), pages 2363-2387, July.
    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. Peng, Yisheng & Liu, Jiahui & Li, Fangyou & Cui, Jianqiang & Lu, Yi & Yang, Linchuan, 2024. "Resilience of ride-hailing services in response to air pollution and its association with built-environment and socioeconomic characteristics," Journal of Transport Geography, Elsevier, vol. 120(C).
    2. Amoozad Mahdiraji, Hannan & Yaftiyan, Fatemeh & Abbasi-Kamardi, Aliasghar & Vrontis, Demetris & Gong, Yu, 2024. "Disentangling the resiliency of international transportation systems under uncertainty by a novel multi-layer spherical fuzzy decision-making framework: Evidence from an emerging economy," Transportation Research Part A: Policy and Practice, Elsevier, vol. 186(C).
    3. Zhou, Mingzhi & Zhou, Jiangping, 2024. "Multiscalar trip resilience and metro station-area characteristics: A case study of Hong Kong amid the pandemic," Journal of Transport Geography, Elsevier, vol. 116(C).
    4. Zizhen Xu & Shauhrat S. Chopra, 2023. "Interconnectedness enhances network resilience of multimodal public transportation systems for Safe-to-Fail urban mobility," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    5. Dong, Shangjia & Gao, Xinyu & Mostafavi, Ali & Gao, Jianxi & Gangwal, Utkarsh, 2023. "Characterizing resilience of flood-disrupted dynamic transportation network through the lens of link reliability and stability," Reliability Engineering and System Safety, Elsevier, vol. 232(C).
    6. Wen, Huiying & Ye, Yichen & Zhang, Lin, 2024. "Optimizing road networks in underdeveloped regions for improving comprehensive efficiency integrated by accessibility, vulnerability and socioeconomic interaction," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    7. M.L. Nores & M.P. Díaz, 2016. "Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 810-826, April.
    8. Georgios Gioldasis & Antonio Musolesi & Michel Simioni, 2020. "Model uncertainty, nonlinearities and out-of-sample comparison: evidence from international technology diffusion," Working Papers hal-02790523, HAL.
    9. Dong, Kangyin & Zhao, Congyu & Nepal, Rabindra & Zander, Kerstin K., 2025. "Are natural disasters stumbling blocks to carbon inequality mitigation? A global perspective," Ecological Economics, Elsevier, vol. 227(C).
    10. Fang, Lei & Härdle, Wolfgang Karl, 2015. "Stochastic population analysis: A functional data approach," SFB 649 Discussion Papers 2015-007, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk.
    11. Sean Fox & Felix Agyemang & Laurence Hawker & Jeffrey Neal, 2024. "Integrating social vulnerability into high-resolution global flood risk mapping," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    12. Iñaki Galán & Lorena Simón & Elena Boldo & Cristina Ortiz & Rafael Fernández-Cuenca & Cristina Linares & María José Medrano & Roberto Pastor-Barriuso, 2017. "Changes in hospitalizations for chronic respiratory diseases after two successive smoking bans in Spain," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    13. Yangyang Meng & Xiaofei Zhao & Jianzhong Liu & Qingjie Qi, 2023. "Dynamic Influence Analysis of the Important Station Evolution on the Resilience of Complex Metro Network," Sustainability, MDPI, vol. 15(12), pages 1-15, June.
    14. Kurmankhojayev, Daniyar & Li, Guoyuan & Chen, Anthony, 2024. "Link criticality index: Refinement, framework extension, and a case study," Reliability Engineering and System Safety, Elsevier, vol. 243(C).
    15. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    16. Strasak, Alexander M. & Umlauf, Nikolaus & Pfeiffer, Ruth M. & Lang, Stefan, 2011. "Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1540-1551, April.
    17. Roberto Basile & Luigi Benfratello & Davide Castellani, 2012. "Geoadditive models for regional count data: an application to industrial location," ERSA conference papers ersa12p83, European Regional Science Association.
    18. Paolo Veneri, 2018. "Urban spatial structure in OECD cities: Is urban population decentralising or clustering?," Papers in Regional Science, Wiley Blackwell, vol. 97(4), pages 1355-1374, November.
    19. Schwemmer, Philipp & Güpner, Franziska & Adler, Sven & Klingbeil, Knut & Garthe, Stefan, 2016. "Modelling small-scale foraging habitat use in breeding Eurasian oystercatchers (Haematopus ostralegus) in relation to prey distribution and environmental predictors," Ecological Modelling, Elsevier, vol. 320(C), pages 322-333.
    20. E. Zanini & E. Eastoe & M. J. Jones & D. Randell & P. Jonathan, 2020. "Flexible covariate representations for extremes," Environmetrics, John Wiley & Sons, Ltd., vol. 31(5), August.

    More about this item

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:arx:papers:2310.14380. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.