Predicting crash injury severity at unsignalized intersections using support vector machines and naïve Bayes classifiers

Abstract

The Washington, DC crash statistic report for the period from 2013 to 2015 shows that the city recorded about 41 789 crashes at unsignalized intersections, which resulted in 14 168 injuries and 51 fatalities. The economic cost of these fatalities has been estimated to be in the millions of dollars. It is therefore necessary to investigate the predictability of the occurrence of theses crashes, based on pertinent factors, in order to provide mitigating measures. This research focused on the development of models to predict the injury severity of crashes using support vector machines (SVMs) and Gaussian naïve Bayes classifiers (GNBCs). The models were developed based on 3307 crashes that occurred from 2008 to 2015. Eight SVM models and a GNBC model were developed. The most accurate model was the SVM with a radial basis kernel function. This model predicted the severity of an injury sustained in a crash with an accuracy of approximately 83.2%. The GNBC produced the worst-performing model with an accuracy of 48.5%. These models will enable transport officials to identify crash-prone unsignalized intersections to provide the necessary countermeasures beforehand.

Exploring the Injury Severity Risk Factors in Fatal Crashes with Neural Network

“…ML methods are more flexible with no or fewer model assumptions for input variables, and also have better fitting characteristics. Some of the commonly used ML approaches used in crash injury severity prediction include artificial neural networks (ANN) [ 58 , 59 , 60 ], random forest [ 54 , 61 , 62 ], support vector machines (SVM) [ 51 , 63 , 64 ], naïve Bayes [ 65 , 66 , 67 ], K-means clustering (KC) [ 68 , 69 , 70 ], and decision trees (DT) [ 71 , 72 , 73 ].…”

Identification of Road Traffic Injury Risk Prone Area Using Environmental Factors by Machine Learning Classification in Nonthaburi, Thailand

“…Moreover, machine learning algorithms can learn from a large training dataset at a fast learning rate. Arhin and Gatiba [21] implemented support vector machines (SVMs) and Gaussian naïve Bayes classifiers (GNBCs) to predict the injury severity of crashes. A total of 3307 crashes that occurred from 2008 to 2015 were used to develop the models (eight SVM models and a GNBC model).…”