A copula-based approach for accommodating the underreporting effect in wildlife-vehicle crash analysis

Sustainability (Switzerland)

Volumn 11, Issue 2, 2019, Pages

  Zou, Yajie ^a   Zhong, Xinzhi ^a   Tang, Jinjun ^b   Ye, Xin ^a   Wu, Lingtao ^c   Ijaz, Muhammad ^a   Wang, Yinhai ^d  

^a Tongji University   (China)

^b CENTRAL SOUTH UNIVERSITY   (China)

^c TEXAS A AND M UNIVERSITY SYSTEM   (United States)

^d UNIVERSITY OF WASHINGTON   (United States)

Author keywords

Mathematical and statistical techniques; Maximum likelihood estimation; Statistical methods; Transportation; Wildlife vehicle collisions

Indexed keywords

COLLISION; DATABASE; GAUSSIAN METHOD; NATURE-SOCIETY RELATIONS; NUMERICAL METHOD; REGRESSION ANALYSIS; STATISTICAL ANALYSIS; TRANSPORT VEHICLE; TRANSPORTATION; WILDLIFE MANAGEMENT;

UNITED STATES; WASHINGTON [UNITED STATES];

EID: 85059986669     PISSN: None     EISSN: 20711050     Source Type: Journal    
DOI: 10.3390/su11020418     Document Type: Article

References (58)

1. Hughes, W.E.; Saremi, A.R.; Paniati, J.F. Vehicle-animal crashes: An increasing safety problem. ITE J. 1996, 66, 24-29
2. Michael, C. Resolving Human-Wildlife Conflict: The Science of Wildlife Damage Management; CRC Press: Boca Raton, FL, USA, 2001
3. Huijser, M.P.; Bergers, P.J.M. The effect of roads and traffic on hedgehog (Erinaceus europaeus) populations. Biol. Conserv. 2000, 95, 111-116
4. Proctor, M.F. Genetic analysis of movement, dispersal and population fragmentation of grizzly bears in southwestern Canada. In Proceedings of the International Conference on Ecology & Transportation, Lake Placid, NY, USA, 24-29 August 2003; pp. 186-193
5. Zee, F.F.V.D.;Wiertz, J.; Braak, C.J.F.T.; Apeldoorn, R.C.V.; Vink, J. Landscape change as a possible cause of the badger Meles meles L. decline in The Netherlands. Biol. Conserv. 1992, 61, 17-22
6. Russo, F.; Comi, A. From the analysis of European accident data to safety assessment for planning: The role of good vehicles in urban area. Eur. Transport Res. Rev. 2017, 9, 9
7. Huijser, M.P.; Mcgowen, P.T.; Fuller, J.; Hardy, A.; Kociolek, A.Wildlife-Vehicle Collision Reduction Study: Report to Congress. 2007. Available online: https://www.fhwa.dot.gov/publications/research/safety/08034/(accessed on 12 January 2019)
8. Tamas, C.; Janos, F. Annual trends in the number of wildlife-vehicle collisions on the main linear transport corridors (highway and railway) of Hungary. N.-West. J. Zool. 2015, 11, 41-50
9. Huijser, M.P.; Wagner, M.E.; Hardy, A.; Clevenger, A.P.; Fuller, J.A. Animal-Vehicle Collision Data Collection Throughout the United States and Canada. Road Ecology Center, 2007. Available online: https://escholarship. org/uc/item/573094wr (accessed on 12 January 2019)
10. Gunson, K.E.; Mountrakis, G.; Quackenbush, L.J. Spatial wildlife-vehicle collision models: A review of current work and its application to transportation mitigation projects. J. Environ. Manag. 2011, 92, 1074-1082
11. Lord, D.; Mannering, F. The statistical analysis of crash-frequency data: A review and assessment of methodological alternatives. Transp. Res. Part A Policy Pract. 2010, 44, 291-305
12. Mannering, F.L.; Bhat, C.R. Analytic methods in accident research: Methodological frontier and future directions. Anal. Methods Accid. Res. 2014, 1, 1-22
13. Jovanis, P.P.; Chang, H.L. Modeling the relationship of accidents to miles traveled. Transport. Res. Rec. 1986, 1068, 42-51
14. Miaou, S.P.; Lum, H. Modeling vehicle accidents and highway geometric design relationships. Accid. Anal. Prev. 1993, 25, 689-709
15. Miaou, S.P. The relationship between truck accidents and geometric design of road sections: Poisson versus negative binomial regressions. Accid. Anal. Prev. 1994, 26, 471-482
16. El-Basyouny, K.; Sayed, T. Comparison of Two Negative Binomial Regression Techniques in Developing Accident Prediction Models. Transp. Res. Rec. J. Transp. Res. Board 2006, 1950
17. Malyshkina, N.V.; Mannering, F.L. Empirical assessment of the impact of highway design exceptions on the frequency and severity of vehicle accidents. Accid. Anal. Prev. 2010, 42, 131-139
18. Lord, D.; Miranda-Moreno, L.F. Effects of low sample mean values and small sample size on the estimation of the fixed dispersion parameter of Poisson-gamma models for modeling motor vehicle crashes: A Bayesian perspective. Saf. Sci. 2008, 46, 751-770
19. Oh, J.;Washington, S.P.; Nam, D. Accident prediction model for railway-highway interfaces. Accid. Anal. Prev. 2006, 38, 346-356
20. Winkelmann, R.; Zimmermann, K.F. Recent developments in count data modelling: Theory and application. J. Econ. Surv. 2010, 9, 1-24
21. Ye, X.; Wang, K.; Zou, Y.; Lord, D. A semi-nonparametric Poisson regression model for analyzing motor vehicle crash data. PLoS ONE 2018, 13, e0197338
22. Gkritza, K.; Baird, M.; Hans, Z.N. Deer-vehicle collisions, deer density, and land use in Iowa's urban deer herd management zones. Accid. Anal. Prev. 2010, 42, 1916-1925
23. Malo, J.E.; Suarez, F.; Diez, A. Can we mitigate animal-vehicle accidents using predictive models? J. Appl. Ecol. 2004, 41, 701-710
24. Hubbard, M.W.; Danielson, B.J.; Schmitz, R.A. Factors influencing the location of deer-vehicle accidents in Iowa. J. Wildl. Manag. 2000, 64, 707-713
25. Rodriguez, K.E. Modeling Black Bear-Vehicle Collision Zones in Yosemite National Park. Master's Thesis, San Jose State University, San Jose, CA, USA, 2015
26. Seiler, A. Predicting locations of moose-vehicle collisions in Sweden. J. Appl. Ecol. 2005, 42, 371-382
27. Tappe, P.A. County-Level Factors Contributing to Deer-Vehicle Collisions in Arkansas. J. Wildl. Manag. 2011, 71, 2727-2731
28. Lao, Y.; Wu, Y.J.; Corey, J.; Wang, Y. Modeling animal-vehicle collisions using diagonal inflated bivariate Poisson regression. Accid. Anal. Prev. 2011, 43, 220-227
29. Neumann, W.; Ericsson, G.; Dettki, H.; Bunnefeld, N.; Keuler, N.S.; Helmers, D.P.; Radeloff, V.C. Difference in spatiotemporal patterns of wildlife road-crossings and wildlife-vehicle collisions. Biol. Conserv. 2012, 145, 70-78
30. Murphy, A.; Xia, J. Risk analysis of animal-vehicle crashes: A hierarchical Bayesian approach to spatial modelling. Int. J. Crashworthiness 2016, 21, 1-13
31. Donaldson, B.; Lafon, N. Personal Digital Assistants to Collect Data on Animal Carcass Removal from Roadways. Transp. Res. Rec. J. Transp. Res. Board 2010, 2147, 18-24
32. Romin, L.A.; Bissonette, J.A. Deer: Vehicle Collisions: Status of State Monitoring Activities and Mitigation Efforts. Wildl. Soc. Bull. 1996, 24, 276-283
33. Gunson, K.E.; Chruszcz, B.; Clevenger, A.P. Large animal-vehicle collisions in the Central Canadian Rocky Mountains: Patterns and characteristics. BMC Health Serv. Res. 2003, 10, 1-8
34. Rowden, P.; Steinhardt, D.; Sheehan, M. Road crashes involving animals in Australia. Accid. Anal. Prev. 2008, 40, 1865-1871
35. Yannis, G.; Papadimitriou, E.; Chaziris, A.; Broughton, J. Modeling road accident injury under-reporting in Europe. Eur. Transport Res. Rev. 2014, 6, 425-438
36. Lao, Y.; Zhang, G.; Wu, Y.-J.; Wang, Y. Modeling animal-vehicle collisions considering animal-vehicle interactions. Accid. Anal. Prev. 2011, 43, 1991-1998
37. Snow, N.P.; Andelt, W.F.; Stanley, T.R.; Resnik, J.R.; Munson, L. Effects of roads on survival of San Clemente Island foxes. J. Wildl. Manag. 2012, 76, 243-252
38. Huijser, M.P.; Mcgowen, P.T.; Clevenger, A.P.; Ament, R. Wildlife-Vehicle Collision Reduction Study: Best Practices Manual; The U.S. Federal Highway Administration (FHWA): Washington, DC, USA, 2008
39. Alsop, J.; Langley, J. Under-reporting of motor vehicle traffic crash victims in New Zealand. Accid. Anal. Prev. 2001, 33, 353-359
40. Yamamoto, T.; Hashiji, J.; Shankar, V.N. Underreporting in traffic accident data, bias in parameters and the structure of injury severity models. Accid. Anal. Prev. 2008, 40, 1320-1329
41. Patil, S.; Geedipally, S.R.; Lord, D. Analysis of crash severities using nested logit model-Accounting for the underreporting of crashes. Accid. Anal. Prev. 2012, 45, 646-653
42. Snow, N.P.; Porter, W.F.; Williams, D.M. Underreporting of wildlife-vehicle collisions does not hinder predictive models for large ungulates. Biol. Conserv. 2015, 181, 44-53
43. Tavasszy, L.; de Jong, G. Data availability and model form. In Modelling Freight Transport; Elsevier: New York, NY, USA, 2014; pp. 229-244
44. American Association of State Highway and Transportation Officials. Policy on Geometric Design of Highways and Streets; American Association of State Highway and Transportation Officials: Washington, DC, USA, 2001
45. Knapp, K. Crash Reduction Factors for Deer-Vehicle Crash Countermeasures: State of the Knowledge and Suggested Safety Research Needs. Transp. Res. Rec. J. Transp. Res. Board 2005, 1908, 172-179
46. Genest, C.; Favre, A.-C. Everything you always wanted to know about copula modeling but were afraid to ask. J. Hydrol. Eng. 2007, 12, 347-368
47. Bhat, C.R.; Eluru, N. A copula-based approach to accommodate residential self-selection effects in travel behavior modeling. Transp. Res. Part B Methodol. 2009, 43, 749-765
48. Zou, Y.; Zhang, Y. A copula-based approach to accommodate the dependence among microscopic traffic variables. Transp. Res. Part C Emerg. Technol. 2016, 70, 53-68
49. Zou, Y.; Ye, X.; Henrickson, K.; Tang, J.; Wang, Y. Jointly analyzing freeway traffic incident clearance and response time using a copula-based approach. Transp. Res. Part C Emerg. Technol. 2018, 86, 171-182
50. Wang, K.; Yasmin, S.; Konduri, K.C.; Eluru, N.; Ivan, J.N. Copula-based joint model of injury severity and vehicle damage in two-vehicle crashes. Transp. Res. Rec. J. Transp. Res. Board 2015, 158-166
51. Rana, T.; Sikder, S.; Pinjari, A. Copula-based method for addressing endogeneity in models of severity of traffic crash injuries: Application to two-vehicle crashes. Transp. Res. Rec. J. Transp. Res. Board 2010, 75-87
52. Yasmin, S.; Eluru, N.; Pinjari, A.R.; Tay, R. Examining driver injury severity in two vehicle crashes-A copula based approach. Accid. Anal. Prev. 2014, 66, 120-135
53. Nashad, T.; Yasmin, S.; Eluru, N.; Lee, J.; Abdel-Aty, M.A. Joint modeling of pedestrian and bicycle crashes: Copula-based approach. Transp. Res. Rec. J. Transp. Res. Board 2016, 119-127
54. Marra, G.; Wyszynski, K. Semi-parametric copula sample selection models for count responses. Comput. Stat. Data Anal. 2016, 104, 110-129
55. Hauer, E.; Harwood, D.W.; Griffith, M.S. Estimating Safety by the Empirical Bayes Method: A Tutorial. Transp. Res. Rec. 2002, 1784, 126-131
56. Zou, Y.; Ash, J.E.; Park, B.-J.; Lord, D.;Wu, L. Empirical Bayes estimates of finite mixture of negative binomial regression models and its application to highway safety. J. Appl. Stat. 2018, 45, 1652-1669
57. Cheng, W.;Washington, S. New Criteria for Evaluating Methods of Identifying Hot Spots. Transp. Res. Rec. J. Transp. Res. Board 2008, 2083, 76-85
58. Wu, L.; Zou, Y.; Lord, D. Comparison of Sichel and Negative Binomial Models in Hot Spot Identification. Transp. Res. Rec. J. Transp. Res. Board 2014, 2460, 107-116