Freeway accident duration prediction based on social network information

Abstract

Accident duration prediction is the basis of freeway emergency management, and timely and accurate accident duration prediction can provide a reliable basis for road traffic diversion and rescue agencies. This study proposes a method for predicting the duration of freeway accidents based on social network information by collecting Weibo data of freeway accidents in Sichuan province and using the advantage that human language can convey multi-dimensional information. Firstly, text features are extracted through a TF-IDF model to represent the accident text data quantitatively; secondly, the variability between text data is exploited to construct an ordered text clustering model to obtain clustering intervals containing temporal attributes, thus converting the ordered regression problem into an ordered classification problem; finally, two nonparametric machine learning methods, namely support vector machine (SVM) and k-nearest neighbour method (KNN), to construct an accident duration prediction model. The results show that when the ordered text clustering model divides the text dataset into four classes, both the SVM model and the KNN model show better prediction results, and their average absolute error values are less than 22 %, which is much better than the prediction results of the regression prediction model under the same method.