Article

Correlating Machine Learning Classification of Traffic Camera Images with Snow-Related Vehicular Crashes in New York State

Downloads

Chang, J., & Walker, C. L. (2022). Correlating Machine Learning Classification of Traffic Camera Images with Snow-Related Vehicular Crashes in New York State. Prevention and Treatment of Natural Disasters, 1(2). https://doi.org/10.54963/ptnd.v1i2.65

Authors

Millions of motor vehicle crashes and tens of thousands of resulting deaths occur each year in the United States. While it is well known that wintry conditions make driving more difficult and dangerous, it is difficult to quantify and communicate the threat to motorists, especially in real time. This proof-of-concept research uses machine learning (ML) to approach this problem in a new way by creating a ML model that can identify snow on the road from a traffic camera image. This information is coupled with the number of coincident vehicular crashes to provide detailed consideration of the impact of snow on the road to motorists and transportation agency decision-makers. It was found that, during meteorological winter, when the ML model determined there to be snow on the road in a traffic camera image, the chance of a vehicular crash pairing with that traffic camera increased by 61%. The systems developed as part of this research have potential to assist roadway officials in assessing risk in real time and making informed decisions about snow removal and road closures. Moreover, the implementation of in-vehicle weather hazard information could promote driver safety and allow motorists to adjust their driving behavior and travel decision making as well.

Keywords:

Image classification Image recognition Machine learning Road condition Road weather Weather-related crashes

Author Biography

Research Applications Laboratory

References

  1. Steiner, M., Anderson, A., Landolt, S., et al., 2015. Coping with adverse winter weather: Emerging capabilities in support of airport and airline operations. Journal of Air Traffic Control. 57, 36-45.
  2. Lazo, J.K., Hosterman, H.R., Sprague-Hilderbrand, J.M., et al., 2020. Impact-Based Decision Support Services and the Socioeconomic Impacts of Winter Storms. Bulletin of the American Meteorological Society. 101(5), E626-E639. DOI: https://doi.org/10.1175/bams-d-18-0153.1
  3. Blincoe, L.J., Miller, T.R., Zaloshnja, E., et al., 2015. The economic and societal impact of motor vehicle crashes, 2010. National Highway Traffic Safety Administration Rep. DOT HS 812 013, pp. 304.
  4. American Highway Users Alliance, 2014. The economic costs of disruption from a snowstorm and HIS global insight. Retrieved February 16, 2022 from
  5. www.highways.org/wp-content/uploads/2014/02/ economic-costs-of-snowstorms.pdf.
  6. FHWA, 2021. How Do Weather Events Impact Roads? Retrieved December 17, 2021 from https://ops.fhwa.dot.gov/weather/q1_roadimpact.htm.
  7. Boatman, J.F., Reinking, R.F., 1984. Synoptic and Mesoscale Circulations and Precipitation Mechanisms in Shallow Upslope Storms over the Western High Plains. Monthly Weather Review. 112(9), 1725-1744. DOI: https://doi.org/10.1175/1520-0493(1984)1122.0.co;2
  8. Dunn, L., 1987. Cold Air Damming by the Front Range of the Colorado Rockies and its Relationship to Locally Heavy Snows. Weather and Forecasting. 2(3), 177-189. DOI: https://doi.org/10.1175/1520-0434(1987)0022.0.co;2
  9. Bannon, P.R., 1992. A Model of Rocky Mountain Lee Cyclogenesis. Journal of the Atmospheric Sciences. 49(16), 1510-1522. DOI: https://doi.org/10.1175/1520-0469(1992)0492.0.co;2
  10. Horel, J.D., Gibson, C.V., 1994. Analysis and Simulation of a Winter Storm over Utah. Weather and Forecasting. 9(4), 479-494. DOI: https://doi.org/10.1175/1520-0434(1994)0092.0.co;2
  11. Mahoney, J.L., Brown, J.M., Tollerud, E.I., 1995. Contrasting Meteorological Conditions Associated with Winter Storms at Denver and Colorado Springs. Weather and Forecasting. 10(2), 245-260. DOI: https://doi.org/10.1175/1520-0434(1995)0102.0.co;2
  12. Davis, C.A., 1997. The Modification of Baroclinic Waves by the Rocky Mountains. Journal of the Atmospheric Sciences. 54(7), 848-868. DOI: https://doi.org/10.1175/1520-0469(1997)0542.0.co;2
  13. Hirsch, M.E., Degaetano, A.T., Colucci, S.J., 2001. An East Coast Winter Storm Climatology. Journal of Climate. 14(5), 882-899. DOI: https://doi.org/10.1175/1520-0442(2001)0142.0.co;2
  14. Hoskins, B.J., Hodges, K.I., 2002. New Perspectives on the Northern Hemisphere Winter Storm Tracks. Journal of the Atmospheric Sciences. 59(6), 1041-1061. DOI: https://doi.org/10.1175/1520-0469(2002)0592.0.co;2
  15. Bentley, A.M., Bosart, L.F., Keyser, D., 2019. A Climatology of Extratropical Cyclones Leading to Extreme Weather Events over Central and Eastern North America. Monthly Weather Review. 147(5), 1471-1490. DOI: https://doi.org/10.1175/mwr-d-18-0453.1
  16. Feser, F., Krueger, O., Woth, K., et al., 2021. North Atlantic Winter Storm Activity in Modern Reanalyses and Pressure-Based Observations. Journal of
  17. Climate. 34(7), 2411-2428. DOI: https://doi.org/10.1175/jcli-d-20-0529.1
  18. Carmichael, C.G., Gallus, W.A., Temeyer, B.R., et al., 2004. A Winter Weather Index for Estimating Winter Roadway Maintenance Costs in the Midwest. Journal of Applied Meteorology. 43(11), 1783-1790. DOI: https://doi.org/10.1175/jam2167.1
  19. Kocin, P.J., Uccellini, L.W., 2004. Supplement to a Snowfall Impact Scale Derived from Northeast Storm Snowfall Distributions. Bulletin of the American Meteorological Society. 85(2), 194-194. DOI: https://doi.org/10.1175/bams-85-2-kocin
  20. Nixon, W.A., Qiu, L., 2005. Developing a Storm Severity Index. Transportation Research Record: Journal of the Transportation Research Board. 1911(1), 143-148.
  21. DOI: https://doi.org/10.1177/0361198105191100114
  22. Cerruti, B.J., Decker, S.G., 2011. The Local Winter Storm Scale: A Measure of the Intrinsic Ability of Winter Storms to Disrupt Society. Bulletin of the
  23. American Meteorological Society. 92(6), 721-737. DOI: https://doi.org/10.1175/2010bams3191.1
  24. Boustead, B.E., Hilberg, S.D., Shulski, M.D., et al.,2015. The Accumulated Winter Season Severity Index (AWSSI). Journal of Applied Meteorology and
  25. Climatology. 54(8), 1693-1712. DOI: https://doi.org/10.1175/jamc-d-14-0217.1
  26. Walker, C.L., Hasanzadeh, S., Esmaeili, B., et al., 2019. Developing a winter severity index: A critical review. Cold Regions Science and Technology. 160,
  27. -149. DOI: https://doi.org/10.1016/j.coldregions.2019.02.005
  28. Walker, C.L., Steinkruger, D., Gholizadeh, P., et al., 2019. Developing a Department of Transportation Winter Severity Index. Journal of Applied Meteorology and Climatology. 58(8), 1779-1798. DOI: https://doi.org/10.1175/jamc-d-18-0240.1
  29. NWS, 2021. Winter Storm Severity Index. Retrieved December 17, 2021 from https://www.wpc.ncep.noaa.gov/wwd/wssi/wssi.php.
  30. Matthews, L., Andrey, J., Hambly, D., et al., 2017. Development of a Flexible Winter Severity Index for Snow and Ice Control. Journal of Cold Regions Engineering. 31(3), 04017005. DOI: https://doi.org/10.1061/(asce)cr.1943-5495.0000130
  31. Dao, B., Hasanzadeh, S., Walker, C.L., et al., 2019. Current Practices of Winter Maintenance Operations and Perceptions of Winter Weather Conditions. Journal of Cold Regions Engineering. 33(3), 04019008. DOI: https://doi.org/10.1061/(asce)cr.1943-5495.0000191
  32. Gholizadeh, P., Walker, C.L., Anderson, M., et al., 2019. Application of Unsupervised Machine Learning to Increase Safety and Mobility on Roadways
  33. after Snowstorms. Computing in Civil Engineering. DOI: https://doi.org/10.1061/9780784482445.045
  34. Pisano, P.A., Goodwin, L.C., Rosetti, M.A., 2008. U.S. Highway Crashes in Adverse Road Weather Conditions. Proceedings of the 88th Annual American Meteorological Society Meeting, 20-24 January, New Orleans, LA.
  35. Black, A.W., Mote, T.L., 2015. Characteristics of Winter-Precipitation-Related Transportation Fatalities in the United States. Weather, Climate, and Society. 7(2), 133-145. DOI: https://doi.org/10.1175/wcas-d-14-00011.1
  36. Black, A.W., Mote, T.L., 2015. Effects of winter precipitation on automobile collisions, injuries, and fatalities in the United States. Journal of Transport
  37. Geography. 48, 165-175. DOI: https://doi.org/10.1016/j.jtrangeo.2015.09.007
  38. Tobin, D.M., Kumjian, M.R., Black, A.W., 2019. Characteristics of Recent Vehicle-Related Fatalities during Active Precipitation in the United States.
  39. Weather, Climate, and Society. 11(4), 935-952. DOI: https://doi.org/10.1175/wcas-d-18-0110.1
  40. Tobin, D.M., Kumjian, M.R., Black, A.W., 2021. Effects of precipitation type on crash relative risk estimates in Kansas. Accident Analysis & Prevention.
  41. , 105946. DOI: https://doi.org/10.1016/j.aap.2020.105946
  42. Khan, M.N., Ahmed, M.M., 2019. Snow Detection using In-Vehicle Video Camera with Texture-Based Image Features Utilizing K-Nearest Neighbor, Support Vector Machine, and Random Forest. Transportation Research Record: Journal of the Transportation Research Board. 2673(8), 221-232. DOI: https://doi.org/10.1177/0361198119842105
  43. Khan, M.N., Das, A., Ahmed, M.M., et al., 2021. Multilevel weather detection based on images: A machine learning approach with histogram of oriented gradient and local binary pattern-based features. Journal of Intelligent Transportation Systems. 25(5),513-532. DOI: https://doi.org/10.1080/15472450.2021.1944860
  44. Haralick, R.M., Shanmugam, K., Dinstein, I., 1973. Textural Features for Image Classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC. 3(6), 610-621. DOI: https://doi.org/10.1109/tsmc.1973.4309314
  45. Roser, M., Moosmann, F., 2008. Classification of weather situations on single color images. 2008 IEEE Intelligent Vehicles Symposium. DOI: https://doi.org/10.1109/ivs.2008.4621205
  46. Yan, X., Luo, Y., Zheng, X., 2009. Weather Recognition Based on Images Captured by Vision System in Vehicle. Advances in Neural Networks - ISNN 2009 Lecture Notes in Computer Science. 390-398. DOI: https://doi.org/10.1007/978-3-642-01513-7_42
  47. Bosch, A., Zisserman, A., Munoz, X., 2007. Image Classification using Random Forests and Ferns. 2007 IEEE 11th International Conference on Computer Vision. DOI: https://doi.org/10.1109/iccv.2007.4409066
  48. Google, 2020. Teachable Machine. Retrieved December 17, 2020 from https://teachablemachine.withgoogle.com/.
  49. 511 New York, 2020. 511 NY Get Connected To Go. Traffic, Travel, and Transit Information. Retrieved December 17, 2020 from https://511ny.org/#:Alerts.