Severity analysis of tree and utility pole crashes: Applying fast and frugal heuristics. (July 2020)

• Record Type: Journal Article
• Authors: Das, Subasish; Storey, Beverly; Shimu, Tahmida Hossain; Mitra, Sudeshna; Theel, Magdalena; Maraghehpour, Bita
• Is Part Of: IATSS research. Volume 44:Number 2(2020)
• Page Start: 85
• Page End: 93
• Publication Date: 2020-07
• Subjects: Tree and utility pole/other pole crashes -- Data mining -- Market basket analysis -- Rules mining; Traffic safety -- Periodicals; Transportation and state -- Periodicals; Verkeersveiligheid; Internationale organisaties; Traffic safety; Transportation and state; Periodicals; 363.1256
• DOI: 10.1016/j.iatssr.2019.08.001
• Languages: English
• ISSNs: 0386-1112

Abstract

Abstract: A roadway departure (RwD) crash is defined as a crash that occurs after a vehicle crosses an edge line or a center line, or otherwise leaves the designated travel path. RwD crashes account for approximately 50% of all traffic fatalities in the U.S. Additionally, crashes related to roadside fixed objects such as trees, utility poles, or other poles (TUOP) make up 12–15% of all fatal RwD crashes in the U.S. Data spanning over seven years (2010–2016) shows that TUOP crashes account for approximately 22% of all fatal crashes in Louisiana, which is significantly higher than the national statistic. This study aims to determine the effect of crash, geometric, environmental, and vehicle characteristics on TUOP crashes by applying the fast and frugal tree (FFT) heuristics algorithm to Louisiana crash data. FFT identifies five major cues or variable threshold attributes that contribute significantly to predicting TUOP crashes. These cues include posted speed limit, primary contributing factor, highway type, weather, and locality type. The balanced accuracy is around 56% for both training and test data. The current model shows higher accuracies compared to machine learning models (e.g., support vector machine, CART). The present findings emphasize the importance of a comprehensive understanding of factors that influence TUOP crashes. The insights from this study can help data-driven decision making at both planning and operation levels. Highlights: Fast and frugal tree algorithm can predict severity of utility pole and tree crashes effectively. The main contributing factors are speed limit, highway type, weather, and locality type. Prediction accuracy of fast and frugal tree is higher than machine learning algorithms.