Risk mapping of wildlife-vehicle collisions across thestate of Montana,USA:a machine-learning approach forimbalanced data along rural roads  

作　　者：Matthew Bell Yiyi Wang Rob Ament 

机构地区：[1]Western Transportation Institute,Montana State University,2327 University Way,Bozeman,MT 59717,USA [2]School of Engineering,San Francisco State University,1600 Holloway Ave,San Francisco,CA 94132,USA

出　　处：《Transportation Safety and Environment》2024年第3期124-132,共9页交通安全与环境（英文）

摘　　要：Wildlife-vehicle collisions(WVCs)with large animals are estimated to cost the USA over 8 billion USD in property damage,tens of thousands of human injuries and nearly 200 human fatalities each year.Most WVCs occur on rural roads and are not collected evenly among road segments,leading to imbalanced data.There are a disproportionate number of analysis units that have zero WVC cases when investigating large geographic areas for collision risk.Analysis units with zero WVCs can reduce prediction accuracy and weaken the coefficient estimates of statistical learning models.This study demonstrates that the use of the synthetic minority over-sampling technique(SMOTE)to handle imbalanced WVC data in combination with statistical and machine-learning models improves the ability to determine seasonal WVC risk across the rural highway network in Montana,USA.An array of regularized variables describing landscape,road and traffic were used to develop negative binomial and random forest models to infer WVC rates per 100 million vehicle miles travelled.The random forest model is found to work particularly well with SMOTE-augmented data to improve the prediction accuracy of seasonal WVC risk.SMOTE-augmented data are found to improve accuracy when predicting crash risk across fine-grained grids while retaining the characteristics of the original dataset.The analyses suggest that SMOTE augmentation mitigates data imbalance that is encountered in seasonally divided WVC data.This research provides the basis for future risk-mapping models and can potentially be used to address the low rates of WVCs and other crash types along rural roads.

关 键 词：wildlife-vehicle collisions machine learning SMOTE negative binomial risk mapping 

分 类 号：U492.8[交通运输工程—交通运输规划与管理]