Abstract
To monitor road safety, billions of records can be generated by Controller Area Network bus each day on public transportation. Automation to determine whether certain driving behaviour of drivers on public transportation can be considered safe on the road using artificial intelligence or machine learning techniques for big data analytics has become a possibility recently. Due to the high false classification rates of the current methods, our goal is to build a practical and accurate method for road safety predictions that automatically determine if the driving behaviour is safe on public transportation. In this paper, our main contributions include (1) a novel feature extraction method because of the lack of informative features in raw CAN bus data, (2) a novel boosting method for driving behaviour classification (safe or unsafe) to combine advantages of deep learning and shallow learning methods with much improved performance, and (3) an evaluation of our method using a real-world data to provide accurate labels from domain experts in the public transportation industry for the first time. The experiments show that the proposed boosting method with our proposed features outperforms seven other popular methods on the real-world dataset by 5.9% and 5.5%.
Highlights
Decreasing the current number of global deaths and injuries from road traffic accidents by half is one of the important Sustainable Development Goals as part of the 2030 Agenda for SustainableDevelopment adopted by the United Nations General Assembly
It is shown in our experiments that the ensemble with our proposed features outperforms any single state-of-the-art method we considered, and our boosting method combines seven state-of-the-art machine learning methods including support vector machine (SVM), random forest (RF), k-nearest neighbour (KNN), discriminant analysis, naive Bayes classifier, adaptive boosting (AdaBoost) and a deep learning neural network called Long Short-Term Memory (LSTM)
It is shown that our feature extraction method can be used to improve the performance of any classification method
Summary
Decreasing the current number of global deaths and injuries from road traffic accidents by half is one of the important Sustainable Development Goals as part of the 2030 Agenda for SustainableDevelopment adopted by the United Nations General Assembly. Decreasing the current number of global deaths and injuries from road traffic accidents by half is one of the important Sustainable Development Goals as part of the 2030 Agenda for Sustainable. Traffic accidents bring huge financial losses to society and cause great physical and mental damages to everyone [1,2]. Analyzing the behavior of drivers, especially public transportation drivers, is important to protect road safety [5,6,7]. To ensure safety for public transportation, public transportation operators can be requested to get an evaluation of drivers and to identify dangerous drivers for retraining. For public transportation fleet management and monitoring, massive data is collected from vehicles using state-of-the-art technologies of sensors for example
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