Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Prediction of Crash Severity on Two-Lane, Two-Way Roads Based on Fuzzy Classification and Regression Tree Using Geospatial Analysis
Mitigating crash severity on regional transportation roadways is an important concern in road safety research. This paper presents a comprehensive geospatial approach based on the fuzzy classification and regression tree (FCART) to predict motor vehicle crashes and their severity on two-lane, two-way roads. The combined use of fuzzy and decision tree in FCART model solves the uncertainty associated with input data; the model can be easily understood and interpreted because of its graphical tree structure. The FCART model uses fuzzy logic to resolve the difficulty of analyzing input variables where no definitive boundary exists between the categories. Moreover, a bagging algorithm is applied in the FCART model to deal with high-variance crash data and improve the performance of the learning process. The bagged-FCART algorithm is tested against FCART, the classification and regression tree (CART), and the support vector machine (SVM) as inferential engines to predict crash severity and uncover spatial and nonspatial factors that systematically relate to crash severity. The results show that applying the bagging algorithm in the FCART model considerably improves the prediction accuracy and that the bagged-FCART model is superior to other tested models in predicting crash severity. A sensitivity analysis was also conducted to determine the importance of input factors. Parts of the results obtained from this analysis are consistent with the existing traffic safety literature and demonstrate that vehicle failure, drivers wearing seat belts, and weather condition factors are some of the most important factors contributing to crash severity. The proposed approach illustrates that in addition to these factors, geographical factors such as proximity to curves and adjacent facilities and land use have a significant effect on crash severity. These results support the prioritization of effective safety measures that are geographically targeted and behaviorally sound on two-lane, two-way roads.
Prediction of Crash Severity on Two-Lane, Two-Way Roads Based on Fuzzy Classification and Regression Tree Using Geospatial Analysis
Mitigating crash severity on regional transportation roadways is an important concern in road safety research. This paper presents a comprehensive geospatial approach based on the fuzzy classification and regression tree (FCART) to predict motor vehicle crashes and their severity on two-lane, two-way roads. The combined use of fuzzy and decision tree in FCART model solves the uncertainty associated with input data; the model can be easily understood and interpreted because of its graphical tree structure. The FCART model uses fuzzy logic to resolve the difficulty of analyzing input variables where no definitive boundary exists between the categories. Moreover, a bagging algorithm is applied in the FCART model to deal with high-variance crash data and improve the performance of the learning process. The bagged-FCART algorithm is tested against FCART, the classification and regression tree (CART), and the support vector machine (SVM) as inferential engines to predict crash severity and uncover spatial and nonspatial factors that systematically relate to crash severity. The results show that applying the bagging algorithm in the FCART model considerably improves the prediction accuracy and that the bagged-FCART model is superior to other tested models in predicting crash severity. A sensitivity analysis was also conducted to determine the importance of input factors. Parts of the results obtained from this analysis are consistent with the existing traffic safety literature and demonstrate that vehicle failure, drivers wearing seat belts, and weather condition factors are some of the most important factors contributing to crash severity. The proposed approach illustrates that in addition to these factors, geographical factors such as proximity to curves and adjacent facilities and land use have a significant effect on crash severity. These results support the prioritization of effective safety measures that are geographically targeted and behaviorally sound on two-lane, two-way roads.
Prediction of Crash Severity on Two-Lane, Two-Way Roads Based on Fuzzy Classification and Regression Tree Using Geospatial Analysis
Effati, Meysam (Autor:in) / Rajabi, Mohammd Ali (Autor:in) / Hakimpour, Farshad (Autor:in) / Shabani, Shahin (Autor:in)
19.09.2014
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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