Volume List  / Volume 11 (3)

Article

EVALUATION OF MULTI-CLASS MULTI-LABEL MACHINE LEARNING METHODS TO IDENTIFY THE CONTRIBUTING FACTORS TO THE SEVERITY OF ANIMAL-VEHICLE COLLISIONS

DOI: 10.7708/ijtte2021.11(3).01


11 / 3 / 341 - 358 Pages

Author(s)

Kian Moghaddam - Department of Civil Engineering and Construction Engineering Management, California State University, Long Beach, CA 90840, USA -

Vahid Balali - Department of Civil Engineering and Construction Engineering Management, California State University, Long Beach, CA 90840, USA -

Prateechi Singh - Department of Computer Engineering and Computer Science, California State University, Long Beach, CA 90840, USA -

Majid Khalilikhah - C&M Associates, Dallas, TX 75248, USA -


Abstract

Transportation is a fundamental tool to develop communities, cities, and countries on a larger scale, and more extensive transportation networks have developed ubiquitously. However, it is needed to consider the fact that animals also live in the same environment without using the same means, and there is always a chance of colliding with them while driving vehicles. Animal-Vehicle Collision (AVC) is a principal concern for transportation agencies and roadway hazards that influences human safety, property, and wildlife. State of Tennessee animal crash data has been collected for 23 years containing different types of information for each collision. This paper presents and evaluates the performance of five machine learning-based prediction models for animal collisions in the presence of both categorical and non-categorical features. These five models are developed using Logistic Regression, Random Forest, CatBoost, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LGBM). The CatBoost model has the highest accuracy level at 78.52%. Therefore, it seems to be the most suitable model to predict animal collisions based on 23-year data from Tennessee. The experimental results demonstrate the potential of leveraging categorical data with CatBoost as a viable solution for creating up-to-date and complete analysis for animal-vehicle collision data.


Download Article

Number of downloads: 599


References:

Bartonička, T.; Andrášik, R.; Duľa, M.; Sedoník, J.; Bíl, M. 2018. Identification of local factors causing clustering of animal‐vehicle collisions, The Journal of Wildlife Management 82(5): 940-947.

 

Bhala, D. 2015. Weight of Evidence (WOE) and Information Value (IV) Explained. Available from Internet: https://www.listendata.com.

 

Breiman, L. 1996. Bagging Predictors, Machine learning 24(2): 123-140. Breiman, L. 2001. Random Forests, Machine learning 45(1): 5-32.

 

Chen, T.; Guestrin, C. 2016, August. Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.

 

Clevenger, A.P.; Chruszcz, B.; Gunson, K.E. 2001. Highway Mitigation Fencing Reduces Wildlife-Vehicle Collisions, Wildlife Society Bulletin (1973-2006) 29(2): 646-653.

 

Conn, J.M.;Annest, J.L.; Dellinger, A. 2004. Nonfatal Motor-Vehicle Animal Crash-Related Injuries-United States, 2001-2002, Journal of Safety Research 35(5): 571-574.

 

Found, R.; Boyce, M.S. 2011. Predicting Deer–Vehicle Collisions in an Urban Area, Journal of environmental Management 92(10): 2486-2493.

 

Grace, M.K.; Smith, D.J.; Noss, R.F. 2017. Reducing the Threat of Wildlife-Vehicle Collisions during Peak Tourism Periods using a Roadside Animal Detection System, Accident Analysis & Prevention 109: 55-61.

 

Ha, H.; Shilling, F. 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.

 

Haikonen, H.; Summala, H. 2001. Deer-Vehicle Crashes: Extensive Peak at 1 Hour after Sunset, American Journal of Preventive Medicine 21(3): 209-213.

 

Hedlund, J.H.; Curtis, P.D.; Curtis, G.; Williams, A.F. 2004. Methods to Reduce Traffic Crashes Involving Deer: What Works and What Does Not, Traffic injury Prevention 5(2): 122-131.

 

Hothorn, T.; Brandl, R.; Müller, J. 2012. Large-Scale Model-Based Assessment of Deer-Vehicle Collision Risk, PLoS One 7(2): e29510.

 

Huijser, M.P.; Duffield, J.W.; Clevenger, A.P.; Ament, R.J.; McGowen, P.T. 2009. Cost–Benefit Analyses of Mitigation Measures Aimed at Reducing Collisions with Large Ungulates in the United States and Canada: A Decision Support Tool, Ecology and Society 14(2): 15.

 

Huijser, M.P.; Kociolek, A.V.; McGowen, P.T.; Ament, R.; Hardy, A.; Clevenger, A.P. 2007. Wildlife-Vehicle Collision and Crossing Mitigation Measures: A Toolbox for the Montana Department of Transportation (No. FHWA/MT-07-002/8117-34). Montana. Dept. of Transportation. Research Programs. USA. 113 p.

 

Huijser, M.P.; McGowen, P.T.; Camel, W. 2006. Animal Vehicle Crash Mitigation using Advanced Technology Phase I: Review, Design, and Implementation (No. FHWA-OR-TPF-07-01). Western Transportation Institute. USA. 271 p.

 

Jeihani, M.; Ahangari, S.; Hassan Pour, A.; Khadem, N.; Banerjee, S. 2019. Investigating the Impact of Distracted Driving among Different Socio-Demographic Groups. Morgan State University. USA. 54p.

 

Ke, G.; Meng, Q.; Finley, T.; Wang, T.; Chen, W.; Ma, W.; Ye, Q.; Liu, T.Y. 2017. LightGBM: A Highly Efficient Gradient Boosting Decision Tree. In Proceedings of the Thirty-first Annual Conference on Neural Information Processing Systems (NeurIPS), 3146-3154.

 

Khalilikhah, M.; Heaslip, K. 2017. Improvement of the Performance of Animal Crossing Warning Signs, Journal of Safety Research 62: 1-12.

 

Knapp, K.K.; Yi, X.; Oakasa, T.; Thimm, W.; Hudson, E.; Rathmann, C. 2004. Deer-Vehicle Crash Countermeasure Toolbox: A Decision and Choice Resource. Midwest Regional University Transportation Center, Deer-Vehicle Crash Information Clearinghouse, University of Wisconsin-Madison. USA. 260 p.

 

Kondor, R.; Vert, J.P. 2004. Diffusion Kernels. In Kernel Methods in Computational Biology. Cambridge (Massachusetts): MIT Press, 171-192.

 

Langley, R.L.; Higgins, S.A.; Herrin, K.B. 2006. Risk Factors Associated with Fatal Animal-Vehicle Collisions in the United States, 1995–2004, Wilderness & Environmental Medicine 17(4): 229-239.

 

Lao, Y.; Zhang, G.; Wu, Y.J.; Wang, Y. 2011. Modeling Animal–Vehicle Collisions Considering Animal–Vehicle Interactions, Accident Analysis & Prevention 43(6): 1991-1998.

 

Moghaddam, K.; Balali, V.; Khalilikhah, M.; Rad, A.A. 2020. Identifying the Contributing Factors to the Severity of Animal-Vehicle Collisions. In Proceedings of the Construction Research Congress 2020: Infrastructure Systems and Sustainability, 819-826.

 

Bechra, K.N.; Kazi, A.R. 2017. Survey on Car Detection in Live Video incorporated with Machine Intelligence, International Journal of Advance Research and Innovative Ideas in Education 3: 1488-1492.

 

Phillips, S.J.; Dudík, M. 2008. Modeling of Species Distributions with Maxent: New Extensions and a Comprehensive Evaluation, Ecography 31(2): 161-175.

 

Phillips, S.J.; Dudík, M.; Elith, J.; Graham, C.H.; Lehmann, A.; Leathwick, J.; Ferrier, S. 2009. Sample Selection Bias and Presence‐Only Distribution Models: Implications for Background and Pseudo‐Absence Data, Ecological Applications 19(1): 181-197.

 

Ramp, D.; Roger, E.; 2008. Frequency Of Animal-Vehicle Collisions In NSW. In Book Too Close for Comfort: Contentious Issues in Human–Wildlife Encounters. Royal Zoological Society of New South Wales. Australia. 118-126.

 

Reeve, A.F.; Anderson, S.H. 1993. Ineffectiveness of Swareflex Reflectors at Reducing Deer-Vehicle Collisions, Wildlife Society Bulletin (1973-2006) 21(2): 127-132.

 

Rodríguez-Morales, B.; Díaz-Varela, E.R.; Marey-Pérez, M.F. 2013. Spatiotemporal Analysis of Vehicle Collisions Involving Wild Boar and Roe Deer in NW Spain, Accident Analysis & Prevention 60: 121-133.

 

Ujvari, M.; Baagøe, H.J.; Madsen, A.B. 1998. Effectiveness of Wildlife Warning Reflectors in Reducing Deer-Vehicle Collisions: A Behavioral Study, The Journal of Wildlife Management 62(3): 1094-1099.

 

Wang, Y. 1998. Modeling Vehicle-to-Vehicle Accident Risks Considering the Occurrence Mechanismat Four-Legged Signalized Intersections. Doctoral Dissertation, University of Tokyo.