Predicting severe wildlife vehicle crashes (WVCs) on New Hampshire roads using a hybrid generalized additive model

• Autorzy: Laflamme Eric M. , Villamagna Amy , Kim Hyun Joong

Identyfikatory

DOI

10.61089/aot2024.15w9vq26

• Języki publikacji: EN

Abstrakty

EN

Across the United States, wildlife vehicle crashes (WVCs) are increasing and remain consistently deadly to drivers, despite a downward trend in fatal automobile accidents overall. That said, the factors related to severe WVCs are unclear. With this in mind, we pursued a statistical model to reveal factors associated with WVCs that result in severe injury or death to drivers. We hypothesize that there are statistically significant interactions and non-linear relation-ships between these factors and severity occurrence. We developed a generalized additive model (GAM) with linear terms, additive terms, and a binary response for severity. We surmise that our fitted model results will quantify the relationship between significant variables and severity occurrence, and ultimately help to develop countermeasures to mitigate serious injury. The model was fitted to WVC records occurring between 2002 and 2019 in the state of New Hampshire. Fitted linear terms revealed: 1) in inclement weather, there is about a 22% increase in the odds of severity for slick surface conditions compared to dry surface conditions; 2) for the warmer months (spring/summer), there is a 42% decrease in the odds of severity for straight roads compared to those with curvature/incline; 3) for highways, the odds of severity decreases by 48% for accidents occurring on NH’s two major intestates highways, and 4) for spring/summer (as compared to the fall/winter), there is more than a 3-fold increase in the odds of severity for two-way traffic. Fitted additive terms revealed: 1) the odds of severity increased in the early hours, between midnight and 6AM, and after 5PM; 2) speeds between 45 and 60 mph are associated with an increase in the odds of a severe accident, while both lower and higher speeds (those below 45 and above 60 mph) are associated with a decrease in the odds of a severe accident; and 3) low, mid-range, and high human population densities are associated with decreases, increases, and decreases in odds of severity, respectively. Cross validation and resulting ROC curves gave evidence that our model is well specified and an effective predictor. Results could be used to inform drivers of potentially dangerous roadways/conditions/times.

Słowa kluczowe

EN

vehicle crash; generalized additive model; logistic regression; wildlife vehicle crash; accident severity; interaction

PL

wypadki drogowe; model addytywny; regresja logistyczna; wypadek z dzikimi zwierzętami

• Wydawca: Warsaw University of Technology, Faculty of Transport
• Czasopismo: Archives of Transport
• Rocznik: 2024
• Tom: Vol. 69, iss. 1
• Strony: 39--57
• Opis fizyczny: Bibliogr. 58 poz., tab., wykr.

Twórcy

autor

Laflamme Eric M.

• Plymouth State University, Department of Mathematics, Plymouth, NH

• https://orcid.org/0009-0006-2588-2259

autor

Villamagna Amy

• Plymouth State University, Department of Environmental Science and Policy, Plymouth, NH

• https://orcid.org/0000-0003-4670-644X

autor

Kim Hyun Joong

• Plymouth State University, Social Science Program, Plymouth, NH

• https://orcid.org/0000-0002-7839-2468

Bibliografia

• [1] Al-Bdairi, N.S.S, A. Behnood, and S. Hernandez (2020). Temporal stability of driver injury severities in animal-vehicle collisions: A random parameters with heterogeneity in means (and variances) approach, Analytic Methods in Accident Research. 26, https://doi.org/10.1016/j.amar.
• [2] Ascensao, F., D. Yogui, M. Alves, E. Medici, and A. Desbiez (2019). Predicting spatiotemporal patterns of road mortality for medium-large mammals, Journal of Environmental Management. 248. https://doi.org/10.1016/j.jenvman.2019.109320.
• [3] Bartonička, T., R. Andrášik, M. Duľa, J. Sedoník, and M. Bíl (2018). Identification of Local Factors Causing Clustering of Animal-Vehicle Collisions. J. Wildl. Manag. 82, 940-947. https://doi.org/10.1002/jwmg.21467.
• [4] Beben, D. (2012). Crossings for animals – an effective method of wild fauna conservation. J. Environ. Eng. Landsc. 20, 86-96. https://doi.org/10.3846/16486897.2012.662753.
• [5] Benten, A., T. Hothorn, T. Vor, C. Ammer (2018). Wildlife warning reflectors do not mitigate wildlife–vehicle collisions on roads, Accident Analysis & Prevention. 120, 64-73. https://doi.org/10.1016/j.aap.2018.08.003.
• [6] Bíl, M., J. Kubeček, J. Sedoník, and R. Andrášik (2017). A system for evidence of animal-vehicle collisions along transportation networks, Biol. Conserv. 213, 167-174. https://doi.org/10.1016/j.jenvman.2019.02.076.
• [7] Bíl, M., R. Andrášik, M. Duľa, J. Sedoník (2019). On Reliable Identification of Factors Influencing Wildlife-Vehicle Collisions along Roads, J. Environ. Manag. 237, 297-304. https://doi.org/10.1016/j.jenvman.2019.02.076.
• [8] Cherry, C.C., S. Dietz, E. Sauber-Schatz, S. Russell, J. Proctor, and D. Buttke (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.
• [9] Eloff, and A. van Niekerk (2008). Temporal patterns of animal-related traffic accidents in the Eastern Cape, South Africa, African Journal of Wildlife Research. 38(2), 153-162. https://doi.org/10.1080/15389588.2018.1508835.
• [10] Eluru N., Paleti R, Pendyala RM, Bhat CR. Modeling Injury Severity of Multiple Occupants of Vehicles: Copula-Based Multivariate Approach. Transportation Research Record. 2010, 2165(1), 1-11, https://doi.org/10.3141/2165-01.
• [11] Etter, D.R., K.M. Hollis, T.R. Van Deelen, D.R. Ludwig, J.E. Chelsvig, C.L. Anchor, and R.E. Warner (2002). Survival and movements of white-tailed deer in suburban Chicago, Illinois, Journal of Wildlife Management. 66, 500- 510. https://doi.org/10.3141/2165-01.
• [12] Gharraie, I. and E. Sacchi (2020). Severity Analysis of Wildlife–Vehicle Crashes using Generalized Structural Equation Modeling, Transportation Research Record. 2675(2), 53-64. https://doi.org/10.1177/0361198120961088.
• [13] Gkritza, K., M. Baird, and Z. N. Hans (2010). Deer-vehicle collisions, deer density, and land use in Iowa’s urban deer herd management zones, Accid. Anal. Prev. 42(6), 1916-1925. https://doi.org/10.1016/j.aap.2010.05.013.
• [14] Ha, H. and F. Shilling (2018). Modelling potential wildlife-vehicle collisions (WVC) locations using environmental factors and human population density: A case-study from 3 state highways in Central California, Ecological Informatics. 43, 212-221. https://doi.org/10.1016/j.ecoinf.2017.10.005.
• [15] Haikonen, H. and H. Summala (2001). Deer-vehicle crashes: extensive peak at 1 hour after sunset., Am. J. Prev. Med. 21(3), 209-13. https://doi.org/10.1016/s0749-3797(01)00352-x.
• [16] Hosmer D. and S. Lemeshow (2000). Applied Logistic Regression. New Jersey: Wiley.
• [17] Hothorn, T., J. Müller, L. Held, L. Möst, and A. Mysterud (2015). Temporal patterns of deer–vehicle collisions consistent with deer activity pattern and density increase but not general accident risk, Accident Analysis & Prevention. 81, 143-152. https://doi.org/10.1016/j.aap.2015.04.037.
• [18] Huang, H., H.C. Chin, M.M. Haque (2008). Severity of driver injury and vehicle damage in traffic crashes at intersections: a Bayesian hierarchical analysis. Accid Anal Prev. 40(1), 45-54. https://doi.org/10.1016/j.aa 2007.04.002.
• [19] Hughes, W.E., R.A. Reza, and J.F. Paniati (1996). Vehicle-animal crashes: An increasing safety problem. Institute of Transportation Engineers Journal. 66, 24-28. https://doi.org/10.1016/j.aap.2007.04.002.
• [20] Huijser, M., McGowen, J. Fuller, A. Hardy, A. Kociolek, A. Clevenger, D. Smith, and R. Ament (2008). Wildlife-Vehicle Collision Reduction Study: Report to Congress, Federal Highway Administration, Office of Safety Research and Development.
• [21] Ialongo, C. (2016). Understanding the effect size and its measures, Biochem Med (Zagreb), 26(2), 150-163. https://doi.org/10.11613/BM.2016.015.
• [22] IIHS. https://www.iihs.org/topics/fatality-statistics/detail/collisions-with-fixed-objects-and-animals.
• [23] Imprialou, M-I.M., M. Quddus, D.E. Pitfield, D. Lord (2016). Re-visiting crash–speed relationships: A new perspective in crash modelling, Accident Analysis & Prevention. 86, 173-185. https://doi.org/10.1016/j.aap.2015.10.001.
• [24] Islam, M.S., J. N. Ivan, N. E. Lownes, R. A. Ammar, and S. Rajasekaran (2014). Developing safety performance function for freeways by considering interactions between speed limit and geometric variables, Trans Res. Rec. 2435(1), 72-81. https://doi.org/10.3141/2435-09.
• [25] Jakubas, D., M. Rys, and M. Lazarus (2018). Factors affecting wildlife-vehicle collision on the express-way in a suburban area in norther Poland, North-Western Journal of Zoology. 14 (1), 107-116. [26] Joyce, T.L. and S. Mahoney (2001). Spatial and temporal distributions of moose vehicle collisions in Newfoundland, Wildlife Society Bulletin. 29(1), 281-291. https://www.jstor.org/stable/3784010.
• [27] Krauze-Gryz, D., M. Żmihorski, K. Jasińska, L. Kwaśny, and J. Werka (2017). Temporal pattern of wildlife‐train collisions in Poland, The Journal of Wildlife Management. 81, 1513-1519. https://doi.org/10.1002/jwmg.21311.
• [28] Kučas A. and L. Balčiauskas (2020). Temporal patterns of ungulate-vehicle collisions in Lithuania, Journal of Environmental Management Nov 1, 273. https://doi.org/0.1016/j.jenvman.2020.111172.
• [29] Lave, C. and Elias (1994). Did the 65 mph speed limit save lives?, Accident Analysis and Prevention. 26(1), 49-62. https://doi.org/10.1016/0001-4575(94)90068-x.
• [30] Liu, Y., M. Nieuwenhuis, and A. McCullagh (2018). The effect of roadside land-use on the occurrence of deer vehicle collisions, Irish Forestry. 75, 1&2, 8-25.
• [31] Machado S., M.A.L. Fontes, B. Mendes, A.S. Moura, and B.d.S Romão (2015). Roadkill on vertebrates in Brazil: Seasonal variation and road type comparison, North-Western Journal of Zoology. 11, 247-252.
• [32] Malin, F., I. Norros, and S. Innamaa (2019). Accident risk of road and weather conditions on different road types, Accid. Anal. Prev. 122, 181-188. https://doi.org/10.1016/j.aap.2018.10.014.
• [33] Malyshkina, N., F. Mannering, (2009). Markov switching multinomial logit model: An application to accident-injury severities. Accident Analysis and Prevention. 41(4), 829-838. https://doi.org/10.1016/j.aap.2009.04.006.
• [34] Mannering, F. (2007). The effects of interstate speed limits on driving speeds: Some new evidence, Transportation Research Board 86th Annual Meeting, Washington DC, Jan. 21-25. https://doi.org/10.1016/j.trc.2003.09.001.
• [35] McCarthy, (2001). Effect of speed limits on speed distributions and highway safety: A survey of recent literature, Transport Reviews. 21(1), 31-50. https://doi.org/10.1080/014416400750059275.
• [36] McShea, W.J.; C.M. Stewart, L.J. Kearns, S. Liccioli, and D. Kocka (2008). Factors affecting autumn deer–vehicle collisions in a rural Virginia county. Human–Wildlife Interactions. 76. https://doi.org/10.1002/wsb.244.
• [37] Murphy, A. and J. Xia (2016). Risk analysis of animal–vehicle crashes: a hierarchical Bayesian approach to spatial modelling, International Journal of Crashworthiness, 21:6, 614-626, https://doi.org/10.1080/13588265.2016.1209823.
• [38] Neumann, W., G. Ericsson, H. Dettki, N. Bunnefeld, N.S. Keuler, D. Helmers, V.C. Radeloff (2012). Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions, Biological Conservation, 145(1), 70-78. https://doi.org/10.1016/j.biocon.2011.10.011.
• [39] Olson, D.D., J.A. Bissonette, C. Cramer, K.D. Bunnell, D.C. Coster, and J. Jackson (2015). How does variation in winter weather affect deer-vehicle collision rates?. Wildlife Biology, 21, 80-87. wlb.00855. https://doi.org/10.2981/wlb.00043.
• [40] Ortega, J. (2022). Drivers struck more than 2 million animals during the pandemic. https://news-room.statefarm.com/november-the-most-dangerous-month-for-animal-collisions/.
• [41] Pagany, R. (2020). Wildlife-vehicle collisions - Influencing factors, data collection and research methods, Biological Conservation. 251. https://doi.org/10.1016/j.biocon.2020.108758.
• [42] Prabasaj, , M.L. Pennell, and S. Lemeshow (2013). Standardizing The Power of The Hosmer-Lemeshow Goodness of Fit Test In Large Data Sets, Statist. Med. 32(1), 67-80. https://doi.org/10.1002/sim.5525.
• [43] R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/.
• [44] Radimsky, M., R. Matuszkova, and O. Budik (2016). Relationship between Horizontal Curves Design and Accident Rate, Jurnal Teknologi 78, 75-78. https://doi.org/10.11113/jt.v78.8493.
• [45] Robin, X., N. Turck, A. Hainard, N. Tiberti, F. Lisacek, J. Sanchez, M. Mueller (2011). pROC: an open source package for R and S+ to analyze and compare ROC curves, BMC Bioinformatics. 12, 77. https://doi.org/10.1186/1471-2105-12-77.
• [46] Russo, B.J. (2017). Analysis of Factors Affecting Injury Severity in Arizona Animal-Vehicle Crashes Considering Animal Type, Transportation Research Board 96th Annual Meeting, Washington DC, United States, 2017-1-8 to 2017-1-12. https://doi.org/10.1177/0361198119847613.
• [47] Savolainen, T., F.L. Mannering, D. Lord, and M.A. Quddus (2011). The statistical analysis of highway crash-injury severities: A review and assessment of methodological alternatives, Accident Analysis & Prevention. 43, Issue 5, 1666-1676. https://doi.org/10.1016/j.aap.2011.03.025.
• [48] Skroch, M. and T. St. Hilaire (2021). Wildlife-Vehicle Collisions Are a Big and Costly Problem and Congress Can Help, https://www.pewtrusts.org/en/research-and-analysis/articles/2021/05/10/wildlife-vehicle-collisions-are-a-big-and-costly-problem-and-congress-can-help.
• [49] Snow, N. , W.F. Porter, and D.M. Williams (2015). Underreporting of wildlife-vehicle collisions does not hinder predictive models for large ungulates, Biological Conservation, 181. https://doi.org/10.1016/j.biocon.2014.10.030.
• [50] Steiner W., E.M. Schöll, F. Leisch, and K. Hackländer (2021). Temporal patterns of roe deer traffic accidents: Effects of season, daytime and lunar phase. PLoS One 16(3). https://doi.org/10.1371/journal.pone.0249082.
• [51] Steiner, W., F. Leisch, and K. Hackländer (2014). A review on the temporal pattern of deer-vehicle accidents: impact of seasonal, diurnal and lunar effects in cervids, Accid Anal Prev. 66, 168-81. https://doi.org/10.1016/j.aa 2014.01.020.
• [52] Valerio, F., M. Basile, and R. Balestrieri (2021). The identification of wildlife-vehicle collision hotspots: Citizen science reveals spatial and temporal patterns, Ecol Process. 10, 6. https://doi.org/10.1186/s13717-020-00271-4.
• [53] Wali, B., A.J. Khattak, and T. Karnowski (2020). The relationship between driving volatility in time to collision and crash-injury severity in a naturalistic driving environment, Analytic Methods in Accident Research. 28, 100136, https://doi.org/10.1016/j.amar.2020.100136.
• [54] Waring, G.H., J.L. Griffis, and M.E. Vaughn (1991). White-tailed deer roadside behavior, wildlife warning reflectors, and highway mortality, Applied Animal Behaviour Science. 29(1-4), 215-223. https://doi.org/10.1016/0168-1591(91)90249-W.
• [55] Wood, S. N. (2011). Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models, Journal of the Royal Statistical Society (B). 73(1), 3-36. https://doi.org/10.1111/j.1467-9868.2010.00749.x.
• [56] Yamamoto, T., J. Hashiji, V.N. Shankar (2008). Underreporting in traffic accident data, bias in parameters and the structure of injury severity models. Accid Anal Prev. 40(4), 1320-9. https://doi.org/10.1016/j.aa 2007.10.016.
• [57] Zhang Z, Yang R, Yuan Y, Blackwelder G, Yang XT (2021). Examining driver injury severity in left-turn crashes using hierarchical ordered probit models. Traffic Inj Prev. 2021, 22(1):57-62. https://doi.org/10.1080/15389 588.2020.1841899.
• [58] Zuberogoitia, I., J. del Real, J.J. Torres, L. Rodríguez, M. Alonso, and J. Zabala (2014). Ungulate vehicle collisions in a peri-urban environment: consequences of transportation infrastructures planned assuming the absence of ungulates, PLoS One, 9(9). https://doi.org/10.1371/journal.pone.0107713.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024)