Vehicle Accident Risk Prediction Based on AdaBoost-SO in VANETs

Abstract

With the rapid expansion of road traffic and vehicle scale, citizens have to face the serious life safety risks caused by vehicle accidents while enjoying an increasingly convenient life. Fortunately, vehicular ad hoc networks have provided us with important information about vehicle big data, which makes us have new methods to analyze vehicle traffic accidents. In this paper, we tackle the problem of predicting the risk of vehicle accidents and propose the trichotomy Adaboost with SMOTE and One-Hot encoding (AdaBoost-SO) algorithm to attain a vehicle accident risk prediction model. In this paper, predicting accident risk is mainly based on the use of big data mining and analysis of real-life accidents data. First, the experimental dataset is reconstructed by a synthetic minority oversampling technique. We complement the missing data and encode each sample feature to one-hot code. Second, the trichotomy AdaBoost algorithm is, respectively, used to train a series of weak classifiers from the experimental dataset and then combine them into a strong classifier to get the prediction model. Finally, the extensive simulation results illustrate that using the prediction model by trichotomy AdaBoost-SO algorithm can take the area under the curve of 0.77 and real-time into account. Through risk prediction and early warning, it provides the intelligent transportation system and driving safety assistance with a theoretical foundation.

Keywords

• VANET
• big data
• AdaBoost
• SMOTE
• prediction model