Toward the Development of an Impact-Based Decision Support Tool for Surface-Transportation Hazards. Part I: Tying Weather Variables to Road Hazards and Quantifying Impacts

Dana M. Tobin aCooperative Institute for Research in Environmental Sciences, Boulder, Colorado
bWeather Prediction Center, College Park, Maryland

Search for other papers by Dana M. Tobin in
Current site
Google Scholar
PubMed
Close
,
Joshua S. Kastman bWeather Prediction Center, College Park, Maryland

Search for other papers by Joshua S. Kastman in
Current site
Google Scholar
PubMed
Close
,
James A. Nelson bWeather Prediction Center, College Park, Maryland

Search for other papers by James A. Nelson in
Current site
Google Scholar
PubMed
Close
, and
Heather D. Reeves cCooperative Institute for Severe and High-Impact Weather Research and Operations, University of Oklahoma, Norman, Oklahoma
dNOAA/OAR/National Severe Storms Laboratory, Norman, Oklahoma

Search for other papers by Heather D. Reeves in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Development of an impact-based decision support forecasting tool for surface-transportation hazards requires consideration for what impacts the product is intended to capture and how to scale forecast information to impacts to then categorize impact severity. In this first part of the series, we discuss the motivation and intent of such a product, in addition to outlining the approach we take to leverage existing and new research to develop the product. Traffic disruptions (e.g., crashes, increased travel times, roadway restrictions, or closures) are the intended impacts, where impact severity levels are intended to scale to reflect the increasing severity of adverse driving conditions that can correlate with a need for enhanced mitigation efforts by motorists and/or transportation agencies (e.g., slowing down, avoiding travel, and imposing roadway restrictions or closures). Previous research on how weather and road conditions impact transportation and novel research herein to create a metric for crash impact based on precipitation type and local hour of the day are both intended to help scale weather forecasts to impacts. Impact severity classifications can ultimately be determined through consideration of any thresholds used by transportation agencies, in conjunction with the scaling metrics.

Significance Statement

Weather can profoundly impact surface transportation and motorist safety. Because of this and because there are no explicit tools available to forecasters to identify and communicate potential impacts to surface transportation, there is a desire for the development of such a forecast product. However, doing so requires careful consideration for what impacts are intended to be included, how weather corresponds to impacts, and how thresholds for impact severity should be defined. In this first part of the paper series, we outline each of these aspects and present novel research and approaches for the development of an impact-based forecast product specifically tailored to surface-transportation hazards. The product is ultimately intended to improve motorist safety and mobility on roads.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dana M. Tobin, dana.tobin@noaa.gov

Abstract

Development of an impact-based decision support forecasting tool for surface-transportation hazards requires consideration for what impacts the product is intended to capture and how to scale forecast information to impacts to then categorize impact severity. In this first part of the series, we discuss the motivation and intent of such a product, in addition to outlining the approach we take to leverage existing and new research to develop the product. Traffic disruptions (e.g., crashes, increased travel times, roadway restrictions, or closures) are the intended impacts, where impact severity levels are intended to scale to reflect the increasing severity of adverse driving conditions that can correlate with a need for enhanced mitigation efforts by motorists and/or transportation agencies (e.g., slowing down, avoiding travel, and imposing roadway restrictions or closures). Previous research on how weather and road conditions impact transportation and novel research herein to create a metric for crash impact based on precipitation type and local hour of the day are both intended to help scale weather forecasts to impacts. Impact severity classifications can ultimately be determined through consideration of any thresholds used by transportation agencies, in conjunction with the scaling metrics.

Significance Statement

Weather can profoundly impact surface transportation and motorist safety. Because of this and because there are no explicit tools available to forecasters to identify and communicate potential impacts to surface transportation, there is a desire for the development of such a forecast product. However, doing so requires careful consideration for what impacts are intended to be included, how weather corresponds to impacts, and how thresholds for impact severity should be defined. In this first part of the paper series, we outline each of these aspects and present novel research and approaches for the development of an impact-based forecast product specifically tailored to surface-transportation hazards. The product is ultimately intended to improve motorist safety and mobility on roads.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Dana M. Tobin, dana.tobin@noaa.gov
Save
  • Agarwal, M., T. H. Maze, and R. Souleyrette, 2005: Impacts of weather on urban freeway traffic flow characteristics and facility capacity. Proc. 2005 Mid-Continent Transportation Research Symp., Ames, IA, Iowa State University, 14 pp., https://cdn-wordpress.webspec.cloud/intrans.iastate.edu/uploads/2018/03/weather_impacts.pdf.

  • Andrey, J., 2010: Long-term trends in weather-related crash risks. J. Transp. Geogr., 18, 247258, https://doi.org/10.1016/j.jtrangeo.2009.05.002.

    • Search Google Scholar
    • Export Citation
  • Andrey, J., B. Mills, M. Leahy, and J. Suggett, 2003: Weather as a chronic hazard for road transportation in Canadian cities. Nat. Hazards, 28, 319343, https://doi.org/10.1023/A:1022934225431.

    • Search Google Scholar
    • Export Citation
  • Ashley, W. S., S. Strader, D. C. Dziubla, and A. Haberlie, 2015: Driving blind: Weather-related vision hazards and fatal motor vehicle crashes. Bull. Amer. Meteor. Soc., 96, 755778, https://doi.org/10.1175/BAMS-D-14-00026.1.

    • Search Google Scholar
    • Export Citation
  • Barjenbruch, K., and Coauthors, 2016: Drivers’ awareness of and response to two significant winter storms impacting a metropolitan area in the intermountain west: Implications for improving traffic flow in inclement weather. Wea. Climate Soc., 8, 475491, https://doi.org/10.1175/WCAS-D-16-0017.1.

    • Search Google Scholar
    • Export Citation
  • Bhagat-Conway, M. W., and S. Zhang, 2023: Rush hour-and-a-half: Traffic is spreading out post-lockdown. PLOS ONE, 18, e0290534, https://doi.org/10.1371/journal.pone.0290534.

    • Search Google Scholar
    • Export Citation
  • Black, A. W., and T. L. Mote, 2015a: Characteristics of winter precipitation-related transportation fatalities in the United States. Wea. Climate Soc., 7, 133145, https://doi.org/10.1175/WCAS-D-14-00011.1.

    • Search Google Scholar
    • Export Citation
  • Black, A. W., and T. L. Mote, 2015b: Effects of winter precipitation on automobile collisions, injuries, and fatalities in the United States. J. Transp. Geogr., 48, 165175, https://doi.org/10.1016/j.jtrangeo.2015.09.007.

    • Search Google Scholar
    • Export Citation
  • Black, A. W., and G. Villarini, 2019: Effects of methodological decisions on rainfall-related crash relative risk estimates. Accid. Anal. Prev., 130, 2229, https://doi.org/10.1016/j.aap.2018.01.023.

    • Search Google Scholar
    • Export Citation
  • Black, A. W., G. Villarini, and T. L. Mote, 2017: Effects of rainfall on vehicle crashes in six U.S. states. Wea. Climate Soc., 9, 5370, https://doi.org/10.1175/WCAS-D-16-0035.1.

    • Search Google Scholar
    • Export Citation
  • Blincoe, L., and Coauthors, 2023: The economic and societal impact of motor vehicle crashes, 2019 (revised). National Highway Traffic Safety Administration Rep. DOT HS 813 403, 297 pp., https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/813403.

  • Böcker, L., M. Dijst, and J. Prillwitz, 2013: Impact of everyday weather on individual daily travel behaviours in perspective: A literature review. Transp. Rev., 33, 7191, https://doi.org/10.1080/01441647.2012.747114.

    • Search Google Scholar
    • Export Citation
  • Chin, S. M., O. Franzese, D. L. Greene, H. L. Hwang, and R. C. Gibson, 2004: Temporary losses of highway capacity and impacts on performance: Phase 2. Tech. Rep. ORNL/TM-2004/209, 131 pp., https://rosap.ntl.bts.gov/view/dot/37083/dot_37083_DS1.pdf.

  • Codling, P., 1974: Weather and road accidents. Climatic Resources and Economic Activity, David and Charles, 205–222.

  • Commonwealth of Pennsylvania, 2022: Inclement winter weather travel restriction and ban framework. 31 pp., https://www.511pa.com/pdfs/Travel_Restriction_and_Ban_Framework_October_2022.pdf.

  • Doherty, S. T., J. C. Andrey, and J. C. Marquis, 1993: Driver adjustments to wet weather hazards. Climatol. Bull., 27, 154164.

  • Fay, L., N. Villwock-Witte, D. Veneziano, and K. Clouser, 2020: Severe weather index. Tech. Rep. MD-20-SP809B4G, 248 pp., https://www.roads.maryland.gov/OPR_Research/MD-20-SP809B4G_SevereWeatherIndex_Report.pdf.

  • FHWA, 2018: Weather-savvy roads: What is pathfinder? FHWA Doc. FHWA-HOP-18-034, 2 pp., https://ops.fhwa.dot.gov/publications/fhwahop18034/fhwahop18034.pdf.

  • Hanbali, R. M., and D. A. Kuemmel, 1993: Traffic volume reductions due to winter storm conditions. Transp. Res. Rec., 1387, 159164.

  • Javadinasr, M., and Coauthors, 2022: The long-term effects of COVID-19 on travel behavior in the United States: A panel study on work from home, mode choice, online shopping, and air travel. Transp. Res., 90F, 466484, https://doi.org/10.1016/j.trf.2022.09.019.

    • Search Google Scholar
    • Export Citation
  • Keay, K., and I. Simmonds, 2005: The association of rainfall and other weather variables with road traffic volume in Melbourne, Australia. Accid. Anal. Prev., 37, 109124, https://doi.org/10.1016/j.aap.2004.07.005.

    • Search Google Scholar
    • Export Citation
  • Knapp, K. K., and L. D. Smithson, 2003: Winter storm event volume impact analysis using multiple-source archived monitoring data. Transp. Res. Rec., 1700, 1016, https://doi.org/10.3141/1700-03.

    • Search Google Scholar
    • Export Citation
  • Kyte, M., Z. Khatib, P. Shannon, and F. Kitchener, 2001: The effect of weather on free flow speed. Transp. Res. Rec., 1776, 6068, https://doi.org/10.3141/1776-08.

    • Search Google Scholar
    • Export Citation
  • Landolt, S. D., J. S. Lave, D. Jacobson, A. Gaydos, S. DiVito, and D. Porter, 2019: The Impacts of automation on present weather–type observing capabilities across the conterminous United States. J. Appl. Meteor. Climatol., 58, 26992715, https://doi.org/10.1175/JAMC-D-19-0170.1.

    • Search Google Scholar
    • Export Citation
  • Maze, T. H., M. Agarwal, and G. Burchett, 2006: Whether weather matters to traffic demand, traffic safety, and traffic operations and flow. Transp. Res. Rec., 1948, 170176, https://doi.org/10.1177/0361198106194800119.

    • Search Google Scholar
    • Export Citation
  • NOAA, 1998: Automated Surface Observing System (ASOS) user’s guide. National Weather Service Doc., 74 pp., https://www.weather.gov/media/asos/aum-toc.pdf.

  • Qiu, L., and W. Nixon, 2008: Effects of adverse weather on traffic crashes: Systematic review and meta-analysis. Transp. Res. Rec., 2055, 139146, https://doi.org/10.3141/2055-16.

    • Search Google Scholar
    • Export Citation
  • Reeves, H. D., 2016: The uncertainty of precipitation-type observations and its effect on the validation of forecast precipitation type. Wea. Forecasting, 31, 19611971, https://doi.org/10.1175/WAF-D-16-0068.1.

    • Search Google Scholar
    • Export Citation
  • Sturges, L., L. Fay, K. Clouser, and N. Villwock-Witte, 2020: Evaluation of SSI and WSI variables. Tech. Rep. CR 18-03, 148 pp., https://westerntransportationinstitute.org/wp-content/uploads/2021/05/4W7797_FinalReport_ClearRpads.18-03.pdf.

  • Tobin, D. M., M. R. Kumjian, and A. W. Black, 2019: Characteristics of recent vehicle-related fatalities during active precipitation in the United States. Wea. Climate Soc., 11, 935952, https://doi.org/10.1175/WCAS-D-18-0110.1.

    • Search Google Scholar
    • Export Citation
  • Tobin, D. M., M. R. Kumjian, and A. W. Black, 2021: Effects of precipitation type on crash relative risk estimates in Kansas. Accid. Anal. Prev., 151, 105946, https://doi.org/10.1016/j.aap.2020.105946.

    • Search Google Scholar
    • Export Citation
  • Tobin, D. M., H. D. Reeves, M. N. Gibson, and A. A. Rosenow, 2022: Weather conditions and messaging associated with fatal winter-weather-related motor-vehicle crashes. Wea. Climate Soc., 14, 835848, https://doi.org/10.1175/WCAS-D-21-0112.1.

    • Search Google Scholar
    • Export Citation
  • Tobin, D. M., J. S. Kastman, J. A. Nelson, and H. D. Reeves, 2024: Toward the development of an impact-based decision support tool for surface-transportation hazards. Part II: An hourly winter storm severity index. Wea. Forecasting, 39, 11291142, https://doi.org/10.1175/WAF-D-23-0220.1.

    • Search Google Scholar
    • Export Citation
  • Transportation Research Board, 2000: Highway Capacity Manual. National Academy of Sciences, 1207 pp.

  • Villwock-Witte, N., C. Walker, L. Fay, S. Landolt, G. Wiener, and K. Clouser, 2021: Weather Severity Indices—Key issues and potential paths forward. Tech. Rep. Aurora Project 2020-03, 28 pp., https://intrans.iastate.edu/app/uploads/2021/01/weather_severity_indices_key_issues_and_paths_white_paper_w_cvr.pdf.

  • Walker, C. L., D. Steinkruger, P. Gholizadeh, S. Hasanzedah, M. R. Anderson, and B. Esmaeili, 2019: Developing a department of transportation winter severity index. J. Appl. Meteor. Climatol., 58, 17791798, https://doi.org/10.1175/JAMC-D-18-0240.1.

    • Search Google Scholar
    • Export Citation
  • Ye, Z., C. Strong, X. Shi, and S. Conger, 2009: Analysis of Maintenance Decision Support System (MDSS) benefits & costs. Tech. Rep. SD2006-10-F, 5 pp., https://westerntransportationinstitute.org/wp-content/uploads/2016/08/4W1408_Executive_Summary.pdf.

All Time Past Year Past 30 Days
Abstract Views 260 260 87
Full Text Views 154 154 79
PDF Downloads 161 161 79