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Article Navigation >  Journal of Traffic and Transportation Engineering  > 2020  >  20(6): 227-235
LU Jian, WANG Ke, JIANG Yu-ming. Real-time identification method of abnormal road driving behavior based on vehicle driving trajectory[J]. Journal of Traffic and Transportation Engineering, 2020, 20(6): 227-235. doi: 10.19818/j.cnki.1671-1637.2020.06.020
Citation: LU Jian, WANG Ke, JIANG Yu-ming. Real-time identification method of abnormal road driving behavior based on vehicle driving trajectory[J]. Journal of Traffic and Transportation Engineering, 2020, 20(6): 227-235. doi: 10.19818/j.cnki.1671-1637.2020.06.020
shu

Real-time identification method of abnormal road driving behavior based on vehicle driving trajectory

doi: 10.19818/j.cnki.1671-1637.2020.06.020

  • College of Transportation Engineering, Tongji University, Shanghai 201804, China

Funds:

National Key Research and Development Program of China 2017YFC0803902

National Natural Science Foundation of China 71871165

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    Abstract
  • To improve the active prevention and control of road traffic safety, the driving trajectory data of cars were used as the research object and the real-time identification problem of abnormal driving behaviors was studied. A massive amount of vehicle driving trajectory data was extracted based on an unmanned aerial vehicle recorded traffic video. The theory of applying the measurement of risk to quantify the typical abnormal driving behaviors was proposed. A large-sample statistical distribution method was used to determine the characteristic value thresholds for abnormal driving behaviors. The abnormal driving behavior spectrum was established and combined with the traffic environment information. The characteristic value of the abnormal driving behavior spectrum was calculated. Based on the characteristic value of the vehicle's abnormal driving behavior spectrum, the abnormal vehicle samples were labeled. The driving behavior spectrum parameters were partially used as input and imbalanced class boosting artificial intelligence algorithms were used to establish an abnormal driving behavior identification model. To verify the effectiveness of the method, vehicle driving trajectory data were collected using an unmanned aerial vehicle traffic video in Shanghai, and the characteristics of car abnormal car-following behavior were analyzed. Research result shows that the threshold of the abnormal car-following characteristic parameter is 0.19 s-1 using the interquartile range method, most of the samples are in the normal car-following state, and about 2% of the samples are in the abnormal car-following state. Based on the ratio of the normal car-following state and the abnormal car-following state in the driving trajectory of each car, a 95% percentile is used to divide the examples of 8 917 cars into 445 abnormal car-following cars and 8 472 normal car-following cars. The imbalanced class boosting algorithm CUSBoost achieves a recall rate of 94.4% and a precision rate of 85.9% in identifying abnormal car-following vehicles. Its balanced score and the area under the precision-recall curve are the highest among all algorithms. As an objective and quantitative expression method of abnormal driving behavior, the abnormal driving behavior spectrum can be combined with the artificial intelligence method to generate a massive library of abnormal driving behaviors. The imbalanced class boosting algorithm can address the imbalance problem of abnormal driving behavior data and has a better ability to identify abnormal driving behaviors compared to conventional algorithms.

     

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