Abstract
Risky and aggressive driving maneuvers are considered a significant indicator for traffic accident occurrence as well as they aggravate their severity. Traffic violations caused by such uncivilized driving behavior is a global issue. Studies in existing literature have used statistical analysis methods to explore key contributing factors toward aggressive driving and traffic violations. However, such methods are unable to capture latent correlations among predictor variables, and they also suffer from low prediction accuracies. This study aimed to comprehensively investigate different traffic violations using spatial analysis and machine learning methods in the city of Luzhou, China. Violations committed by taxi drivers are the focus of the current study since they constitute a significant proportion of total violations reported in the city. Georeferenced violation data for the year 2016 was obtained from the traffic police department. Detailed descriptive analysis is presented to summarize key statistics about various violation types. Results revealed that over-speeding was the most prevalent violation type observed in the study area. Frequency-based nearest neighborhood cluster methods in Arc map Geographic Information System (GIS) were used to develop hotspot maps for different violation types that are vital for prioritizing and conducting treatment alternatives efficiently. Finally, different machine learning (ML) methods, including decision tree, AdaBoost with a base estimator decision tree, and stack model, were employed to predict and classify each violation type. The proposed methods were compared based on different evaluation metrics like accuracy, F-1 measure, specificity, and log loss. Prediction results demonstrated the adequacy and robustness of proposed machine learning (ML) methods. However, a detailed comparative analysis showed that the stack model outperformed other models in terms of proposed evaluation metrics.
Highlights
Aggressive driving behavior pose a major social and public health concern in urban metropolitans worldwide
Previous studies have mostly used statistical analysis methods to investigate violation contributing factors caused by such uncivilized driving attitudes
In this study we investigated patterns of traffic violations that occurred in the city of Luzhou, China using spatial analysis methods and different machine learning algorithms
Summary
Aggressive driving behavior pose a major social and public health concern in urban metropolitans worldwide. During such situation, drivers commit or tend to commit combination of traffic violations in such a way that endangers other individuals or public property. Traffic violations can be categorized as aggressive or ordinary [1]. Risky and aggressive driving behavior of drivers is regarded as the one of the few leading cause of road traffic accidents (RTAs), in the People’s Republic of China (PRC) [2]. Motorization and auto-ownership have increased exponentially, and so as the rate of RTAs. In PRC, a significant proportion of road traffic injuries (RTIs) are caused by traffic violations mainly associated with such uncivilized driving behaviour. The proportion of vulnerable road users (VRUs) who died on national roads has rapidly increased from 52% in 2008 to 60% in
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