Exploring the feasibility of classification trees versus ordinal discrete choice models for analyzing crash severity

Khan, Ghazan; Bill, Andrea R.; Noyce, David A.

doi:10.1016/j.trc.2014.10.003

Cited by 23 publications

(11 citation statements)

References 9 publications

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“…According to the results from the CART of the two models, when considering misclassification costs for predictive accuracy (Table 5), Model#1 had overall correctness of 52.9% and Model#2 of 65.1%. Despite these low values, as confirmed by Khan et al [29], Kashani and Mohaymany [36], they can be accepted and interpreted. (Figure 3) found six major variables related to the target variables.…”

Section: Resultsmentioning

confidence: 78%

“…For example, an examination of whether the different ages of drivers in different traffic lanes affects the role of the driver (at-fault/not-at-fault) in a crash can influence the development of effective policy. Research by Khan et al [29], which compared DT and ordinal discrete choice model, confirmed that DT can help to address issues of multicollinearity and variable redundancy. Among studies that have analyzed rear-end crashes ( Table 1), most have analyzed crash frequency, followed by crash severity (fatal/nonfatal).…”

Section: Introductionmentioning

confidence: 94%

“…For CART acceptance, splitting was achieved by using unit misclassification costs. is is the proportion of observed and predicted data comparisons [29].…”

Section: Classi Cation Tree and Building Modelmentioning

confidence: 99%

“…In order to determine the optimal tree model, ten-fold cross-validation was undertaken, which is one of several cross-validation techniques to select for appropriate tree size. To avoid over-fitting the model, the maximum tree depth was five nodes, minimum cases in the parent node were 150, and minimum cases in child node were 75 [29].…”

Section: Classi Cation Tree and Building Modelmentioning

confidence: 99%

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Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach

Champahom

Jomnonkwao

Chatpattananan

et al. 2019

Journal of Advanced Transportation

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Objective. Among crash types on ai highways, rear-end crashes have been found to cause the largest number of fatalities. is study aims to nd ways to decrease rear-end crashes and fatal rear-end crashes. Methods. Classi cation and regression tree (CART) was used to analyze the complicated relationship of variables of big data. e analysis was conducted by creating two models: (1) a model which indicates the causes of rear-end crashes by applying Quasi-Induced Exposure to at-fault driver characteristics; (2) a determined model which studies fatal crashes. Results. Predictor variables in the model of at-fault and not-at-fault drivers found that driver age is most signi cant, followed by number of lanes and median opening area. For the mode of fatality, the use of safety equipment was found to be of most importance. Conclusion. e model results can be used to develop guidelines for public awareness programs for motorists and to propose policy changes to the Department of Highway in order to reduce the severity of rear-end crashes. Moreover, this paper discusses the variables that may result in both the perspective of rear-end crash number and the fatality rate of rear-end crashes as strategies in future research.

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Section: Resultsmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 94%

“…For CART acceptance, splitting was achieved by using unit misclassification costs. is is the proportion of observed and predicted data comparisons [29].…”

Section: Classi Cation Tree and Building Modelmentioning

confidence: 99%

Section: Classi Cation Tree and Building Modelmentioning

confidence: 99%

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Analysis of Rear-End Crash on Thai Highway: Decision Tree Approach

Champahom

Jomnonkwao

Chatpattananan

et al. 2019

Journal of Advanced Transportation

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“…Another popular solution for traffic data analysis and for the identification of accident causes is decision trees [47], [46], [27], [48], [32], [49], [50], [51], [52], [53].…”

Section: Decision Treesmentioning

confidence: 99%

A Framework for Traffic Collision Prediction Using Historical Accident Information and Real-Time Sensor Data: A Case Study for the City of Ottawa

Yanes¹,

Enrique²

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According to recent studies, beyond being a major worldwide problem with huge economic impact, traffic collisions are poised to become as well one of the most important leading causes of death. Proactive traffic enforcement and intervention should be based on a thorough analysis on the collision data available to identify leading causes of accidents, the most prone locations as well as to predict the conditions for collision occurrence. This thesis presents a novel framework for collision prediction that takes into consideration historical and real-time factors, such as weather, geospatial information and social event data that can be obtained with existing sensor technology. A prototype is proposed, implemented and evaluated for the city of Ottawa, Canada, to predict: (1) accident frequency (collision vs nocollisions) and (2) accident severity (in terms of fatal, injury and property damage only accidents). The best performance was achieved in both cases using gradient boosted trees.iii

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