Detection of critical safety events on freeways in clear and rainy weather using SHRP2 naturalistic driving data: Parametric and non-parametric techniques

Abstract

A better understanding of what happened before and during a crash event could lead to development of countermeasures that reduce crash risk on freeways. This paper focused on detecting near-crashes on freeways by comparing environmental conditions and vehicle kinematics signatures of near-crash events to normal driving using data collected from the Second Strategic Highway Research Program (SHRP2) – Naturalist Driving Study. The study considered near-crash events as surrogate measures of crash risk only in rainy and clear weather conditions due to a limited number of near-crashes in other weather conditions. A time chunking technique was used with different aggregation levels to monitor changes in vehicle kinematics on a timescale. Both parametric and non-parametric techniques were used to detect near-crashes on freeways. The Binary Logistic Regression model was used as a parametric detection model, while Decision Tree , K-Nearest Neighbors (K-NN), and Deep Learning Artificial Neural Network were used as non-parametric detection models. The results showed that the logistic regression model provided a good fit of the input data and can detect near-crashes with outstanding discrimination. However, the time zone of interest identified by non-parametric models led to higher accurancy than that identified by the logistic regression model. In addition, Decision Tree and Deep Learning ANN machine learning algorithms showed higher detection accuracy of near-crashes compared to the K-NN algorithm.