Abstract
Extreme weather poses significant threats to air-transportation systems, causing flight rerouting and cancelations, as well as passenger travel delays. With the growing frequency of extreme weather hazards, it is essential to understand the extent to which disruptions in flights and subsequent cancelations impact passenger delays. This study focuses on quantifying the impacts of a recent extreme weather event (2022 Winter Storm Elliott) on the U.S. air-transportation system by investigating passenger delays measured based on dwell time at airports using privacy-preserving location-based datasets. The study determines total dwell time and dwell time per anonymized user at airports during the extreme weather event and computes the impact based on changes in values compared to the same period in the previous year. The results show that the storm event caused passengers significant delays, as characterized by a substantial increase in airport dwell time. Factor analysis shows that airports with a greater passenger flow and a greater portion of flights from decentralized airlines aggravated passengers delays during the winter storm. The vulnerability of airports was mainly due to the direct storm exposure, and the influence of network cascading impacts were limited. The findings of this study provide novel insights and quantification of the extent of extreme weather impacts on air transportation at individual airports and national levels. These outcomes could inform airport owners and operators, as well as airlines, about the extent of vulnerability and provide useful information for weather-related risk assessment of air-transportation systems.
This is a preview of subscription content, log in via an institution to check access.
(Source: National Weather Service)
Similar content being viewed by others
Availability of Data and Materials
All data were collected through a CCPA- and GDPR-compliant framework and utilized for research purposes. The data that support the findings of this study are available from Spectus, but restrictions apply to the availability of these data, which were used under license for the current study. The data can be accessed upon request submitted on Spectus.ai. Other data we used in this study are publicly available.
References
Environment Agency (2016) The costs and impacts of the winter 2013 to 2014 floods. Environment Agency, Bristol
Heathrow Airport Ltd (2011) Climate change adaptation report. https://www.heathrow.com/content/dam/heathrow/web/common/documents/company/heathrow-2-0-sustainability/futher-reading/Heathrow%20Airport%20CCAR%202021%20FINAL.pdf
Hsu CW, Liu C, Nguyen KM, Chien YH, Mostafavi A (2024) Do human mobility network analyses produced from different location-based data sources yield similar results across scales? Comput Environ Urban Syst 107:102052
Baglin C (2012) Airport climate adaptation and resilience, vol 33. Transportation Research Board
Borsky S, Unterberger C (2019) Bad weather and flight delays: The impact of sudden and slow onset weather events. Econ Transp 18:10–26
Bureau of Transportation Statistics. https://www.bts.gov/
Chen Z, Wang Y (2019) Impacts of severe weather events on high-speed rail and aviation delays. Transp Res Part D Transp Environ 69:168–183
Coleman N, Gao X, DeLeon J, Mostafavi A (2022) Human activity and mobility data reveal disparities in exposure risk reduction indicators among socially vulnerable populations during COVID-19 for five US metropolitan cities. Sci Rep 12(1):15814
De Vivo C et al (2021) Risk assessment framework for Mediterranean airports: a focus on extreme temperatures and precipitations and sea level rise. Nat Hazards 111:547–566
December 2022 North American Winter Storm (2023). In Wikipedia. https://en.wikipedia.org/wiki/December_2022_North_American_winter_storm
Doll C et al (2014) Large and small numbers: options for quantifying the costs of extremes on transport now and in 40 years. Nat Hazards 72:211–239
Haraguchi M et al (2022) Human mobility data and analysis for urban resilience: a systematic review. Environ Plan B Urban Anal City Sci 49(5):1507–1535
Hsu CW, Ho MA, Mostafavi A (2023) Human mobility networks manifest dissimilar resilience characteristics at macroscopic, substructure, and microscopic scales. Sci Rep 13(1):17327
ICAO (2021) Climate resilient airports. https://www.icao.int/environmental-protection/Documents/Climate%20resilient%20airports.pdf
IPCC (2018) IPCC special report on impacts of 1.5C warming. Summary for policymakers. https://www.ipcc.ch/sr15/chapter/spm/
Lee CC et al (2022a) Quantitative measures for integrating resilience into transportation planning practice: study in Texas. Transp Res Part D Transp Environ 113:103496
Lee CC, Chou C, Mostafavi A (2022b) Specifying evacuation return and home-switch stability during short-term disaster recovery using location-based data. Sci Rep 12(1):15987
Lee CC, Maron M, Mostafavi A (2022c) Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage. Human Soc Sci Commun 9(1):1–12
Liu Z et al (2019) Recommending attractive thematic regions by semantic community detection with multi-sourced VGI data. Int J Geogr Inf Sci 33(8):1520–1544
Liu Z et al (2021) Analysis of the performance and robustness of methods to detect base locations of individuals with geo-tagged social media data. Int J Geogr Inf Sci 35(3):609–627
Liu Z et al (2022) Categorisation of cultural tourism attractions by tourist preference using location-based social network data: the case of Central, Hong Kong. Tour Manag 90:104488
Nuijten AD (2016) Runway temperature prediction, a case study for Oslo Airport, Norway. Cold Reg Sci Technol 125:72–84
OurAirports Website. https://ourairports.com/data/
Rajput AA, Mostafavi A (2023) Latent sub-structural resilience mechanisms in temporal human mobility networks during urban flooding. Sci Rep 13(1):10953
Rajput AA, Nayak S, Dong S, Mostafavi A (2023) Anatomy of perturbed traffic networks during urban flooding. Sustain Cities Soc 97:104693
Rajput AA, Li Q, Gao X, Mostafavi A (2022b) Revealing critical characteristics of mobility patterns in New York City during the onset of COVID-19 pandemic. Frontiers in Built Environment 7:180
Spectus. https://spectus.ai/
Vogiatzis K et al (2021) Climate Change Adaptation Studies as a tool to ensure airport’s sustainability: the case of Athens International Airport (AIA). Sci Total Environ 754:142153
Voskaki A et al (2023) The impact of climate hazards to airport systems: a synthesis of the implications and risk mitigation trends. Transp Rev. https://doi.org/10.1080/01441647.2022.2163319
Wang Q, Taylor JE (2016) Patterns and limitations of urban human mobility resilience under the influence of multiple types of natural disaster. PLoS ONE 11(1):e0147299
Wang A et al (2020) A review of human mobility research based on big data and its implication for smart city development. ISPRS Int J Geo Inf 10(1):13
White V (2016) Revised departmental guidance on valuation of travel time in economic analysis. Office of the Secretary of Transportation, US Department of Transportation, transportation. gov/sites/dot.gov/files/docs/2016%20Revised% 20Value%20of%20Travel%20Time%20Guidance.pdf.
Zhang X, Li N (2022) Characterizing individual mobility perturbations in cities during extreme weather events. Int J Disaster Risk Reduct 72:102849
Zhao Y (2020) The impact of climate change on aircraft takeoff performance for canadian airports. McGill University, Canada
Zhou L, Chen Z (2020) Measuring the performance of airport resilience to severe weather events. Transp Res Part D Transp Environ 83:102362
Funding
No funding was obtained for this study.
Author information
Authors and Affiliations
Contributions
C.W and C.L collected the data, carried out the experiment, and wrote the paper. Z.L collected the data and wrote the paper. A.M conceptualized the study.
Corresponding author
Ethics declarations
Conflict of interest
No competing interests to declare.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Hsu, CW., Liu, C., Liu, Z. et al. Unraveling Extreme Weather Impacts on Air Transportation and Passenger Delays Using Location-Based Data. Data Sci. Transp. 6, 9 (2024). https://doi.org/10.1007/s42421-024-00094-1
Received:
Revised:
Accepted:
Published:
DOI: https://doi.org/10.1007/s42421-024-00094-1