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Comparison of Factors Associated with Animal–Vehicle Crashes and Non-Animal–Vehicle Crashes in Wyoming

Abstract

This study investigated and compared animal–vehicle crashes (AVCs) with non-animal–vehicle crashes (non-AVCs) using police-reported crash data collected from the Wyoming Department of Transportation (WYDOT). Different driver, vehicle, roadway, and environment characteristics between AVCs and non-AVCs were compared. Statistical tests were conducted to see whether the factors prevalent in AVCs were statistically significant. When different environmental characteristics were examined, important distinctions between AVCs and non-AVCs were seen. AVCs were found to be higher from the month of June to November with the highest (14.26%) being observed in the month of November. In addition, a vast number of AVCs (60.41%) occurred during dawn and dusk. The speed limit was also found to have a significant impact on AVCs. More AVCs (76.41%) occurred when the speed limit was higher than 60 mph. In addition, dry road surface (90.02% AVCs), dark and unlit condition (60.41% AVCs), and clear weather (89.53% AVCs) were associated with AVCs when compared to non-AVCs. It was found that drivers between 25 and 64 years of age were more likely to be involved in AVCs (75.10%), whereas younger (between 16 and 24 years) and older drivers (65 years or older) were more likely to be involved in non-AVCs. The proportion of non-AVCs was 28.12% when drivers were between 16 and 24 years of age and 14.51% when drivers were 65 years or older. The results of Mantel–Haenszel estimation showed that drivers who were involved in AVCs had about 1.65 times the odds of being 35 years of age or more and had about 4.37 times the odds of using safety equipment than drivers who were involved in non-AVCs. The results also showed that dawn (odds ratio of 2.001) and night-time (odds ratio of 3.54) tended to have more association with AVCs when compared with non-AVCs. Among different vehicular factors being considered, it was found that AVCs were more common when the vehicles had straight maneuvers (odds ratio of 10.52). In addition, vehicles involved in AVCs had 9.14 times the odds of traveling above 45 mph than vehicles involved in non-AVCs. The factors found to be prevalent in AVCs when compared with non-AVCs will be beneficial to reduce AVCs by helping to identify effective countermeasures.

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References

  1. Huijser M, McGowen P, Fuller J, Hardy A, Kociolek A, Clevenger A, Smith D, Ament R (2007) Wildlife-vehicle collision reduction study. Report to Congress. U.S. Department of Transportation, Federal Highway Administration, Washington D.C.

  2. Wyoming Department of Transportation (2019) 2019 Report on traffic crashes

  3. Riginos C, Fairbank E, Hansen E, Kolek J, Huijser MP (2019) Effectiveness of night-time speed limit reduction in reducing wildlife-vehicle collisions. Report No. FHWA-WY-1904F. Wyoming Department of Transportation

  4. Beckmann JP, Hilty JA (2010) Connecting wildlife populations in fractured landscapes. In: Beckmann JP, Clevenger AP, Huijser MP, Hilty JA (eds) Safe passages: highways, wildlife and habitat connectivity. Island Press, Washington, D.C., pp 3–16

    Google Scholar 

  5. Huijser MP, Fuller J, Wagner ME, Hardy A, Clevenger AP (2007) Animal vehicle collision data collection: a synthesis of highway practice. National Cooperative Highway Research Program Synthesis 370. Transportation Research Board, Washington, D.C.

  6. Evink G (2002) Interaction between roadways and wildlife ecology: a synthesis of highway practice. National Cooperative Highway Research Program Synthesis 305. Transportation Research Board of the National Academies. Washington D.C.

  7. Bissonette JA, Cramer PC (2008) Evaluation of the use and effectiveness of wildlife crossings. National Cooperative Highway Research Program Report 615. Transportation Research Board, Washington, D.C.

  8. Wilkins DC, Kockelman KM, Jiang N (2019) Animal-vehicle collisions in Texas: how to protect travelers and animals on roadways. Accid Anal Prev 131:157–170. https://doi.org/10.1016/j.aap.2019.05.030

    Article  Google Scholar 

  9. Khattak AJ (2003) Human fatalities in animal-related highway crashes. Transportation Research Record, No. 1840, pp 158–165. https://doi.org/10.3141/1840-18

  10. Sullivan JM (2011) Trends and characteristics of animal-vehicle collisions in the United States. J Safety Res 42(1):9–16. https://doi.org/10.1016/j.jsr.2010.11.002

    Article  Google Scholar 

  11. Cherry CC, Dietz S, Sauber-Schatz E, Russell S, Proctor J, Buttke D (2019) Characteristics of animal-related motor vehicle crashes in select national park service units—United States, 1990–2013. Traffic Inj Prev 20(1):58–63. https://doi.org/10.1080/15389588.2018.1508835

    Article  Google Scholar 

  12. Zeller KA, Wattles DW, Destefano S (2020) Evaluating methods for identifying large mammal road crossing locations: black bears as a case study. Landscape Ecol 35(8):1799–1808. https://doi.org/10.1007/s10980-020-01057-x

    Article  Google Scholar 

  13. Conn JM, Annest JL, Dellinger A (2004) Nonfatal motor-vehicle animal crash-related injuries—United States, 2001–2002. J Safety Res 35(5):571–574. https://doi.org/10.1016/j.jsr.2004.10.002

    Article  Google Scholar 

  14. Chen X, Wu S (2014) Examining patterns of animal-vehicle collisions in Alabama, USA. Hum Wildl Interact 8(2):235–244. https://doi.org/10.26077/p18k-1089

    Article  Google Scholar 

  15. Lao Y, Zhang G, Wu YJ, Wang Y (2011) Modeling animal-vehicle collisions considering animal-vehicle interactions. Accid Anal Prev 43(6):1991–1998. https://doi.org/10.1016/j.aap.2011.05.017

    Article  Google Scholar 

  16. Jensen RR, Gonser RA, Joyner C (2014) Landscape factors that contribute to animal-vehicle collisions in two northern Utah Canyons. Appl Geogr 50:74–79. https://doi.org/10.1016/j.apgeog.2014.02.007

    Article  Google Scholar 

  17. Marcoux A, Riley SJ (2010) Driver knowledge, beliefs, and attitudes about deer-vehicle collisions in southern Michigan. Hum Wildl Interact 4(1):47–55

    Google Scholar 

  18. Rowden P, Steinhardt D, Sheehan M (2008) Road crashes involving animals in Australia. Accid Anal Prev 40(6):1865–1871. https://doi.org/10.1016/j.aap.2008.08.002

    Article  Google Scholar 

  19. Wyoming Department of Transportation (2020) Wyoming Crash Reporting System database

  20. Langley RL, Higgins SA, Herrin KB (2006) Risk factors associated with fatal animal-vehicle collisions in the United States, 1995–2004. Wilderness Environ Med 17(4):229–239. https://doi.org/10.1580/06-WEME-OR-001R1.1

    Article  Google Scholar 

  21. SAS Institute Inc (2015) SAS/IML® 14.1 User's Guide. SAS Institute Inc., Cary

  22. Hezaveh AM, Cherry CR (2018) Walking under the influence of the alcohol: a case study of pedestrian crashes in Tennessee. Accid Anal Prev 121(August):64–70. https://doi.org/10.1016/j.aap.2018.09.002

    Article  Google Scholar 

  23. Anderson RWG, McLean AJ, Farmer MJB, Lee BH, Brooks CG (1997) Vehicle travel speeds and the incidence of fatal pedestrian crashes. Accid Anal Prev 29(5):667–674. https://doi.org/10.1016/S0001-4575(97)00036-5

    Article  Google Scholar 

  24. Riginos C, Copeland H, Smith C, Sawyer H, Krasnow K, Hart T (2016) Planning-support for mitigation of wildlife-vehicle collisions and highway impacts on migration routes in Wyoming. FHWA-WY-16/10F. https://rosap.ntl.bts.gov/view/dot/34185

  25. Grace MK, Smith DJ, Noss RF (2017) Reducing the threat of wildlife-vehicle collisions during peak tourism periods using a roadside animal detection system. Accid Anal Prev 109:55–61. https://doi.org/10.1016/j.aap.2017.10.003

    Article  Google Scholar 

  26. Hardy A, Lee S, Al-Kaisy AF (2006) Effectiveness of animal advisory messages on dynamic message signs as a speed reduction tool: case study in rural Montana. Transportation Research Record, No. 1973, pp 64–72. https://doi.org/10.3141/1973-10

  27. Stewart KM (2015) Effectiveness of wildlife crossing structures to minimize traffic collisions with mule deer and other wildlife in Nevada. Report No. 101-10-803. Nevada Department of Transportation, Carson City, pp 1–34

  28. Bell M, Ament R, Fick D, Huijser MP (2020) Improving connectivity: innovative fiber-reinforced polymer structures for wildlife, bicyclists, and/or pedestrians a report for tasks 1–4. No. P701-18-803 TO 2. Nevada Department of Transportation

  29. Hedlund JH, Curtis PD, Curtis G, Williams AF (2004) Methods to reduce traffic crashes involving deer: what works and what does not. Traffic Inj Prev 5(2):122–131. https://doi.org/10.1080/15389580490435079

    Article  Google Scholar 

  30. Putman RJ (1997) Deer and road traffic accidents: options for management. J Environ Manage 51(1):43–57. https://doi.org/10.1006/jema.1997.0135

    Article  Google Scholar 

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Funding was provided by Wyoming Technology Transfer Center.

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Correspondence to Uttara Roy.

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Roy, U., Ksaibati, K. Comparison of Factors Associated with Animal–Vehicle Crashes and Non-Animal–Vehicle Crashes in Wyoming. Int J Civ Eng (2022). https://doi.org/10.1007/s40999-022-00730-3

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  • DOI: https://doi.org/10.1007/s40999-022-00730-3

Keywords

  • Animal–vehicle crash
  • Non-animal–vehicle crash
  • Crash characteristics
  • Mantel–Haenszel statistics