The effect of image recognition traffic prediction method under deep learning and naive Bayes algorithm on freeway traffic safety

• Published in: Image and vision computing Vol. 103; p. 103971
• Main Authors: Yao, Jingxuan, Ye, Yuntao
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.11.2020
• Subjects: Deep learning; Image processing; LSTM; Naive Bayes; Traffic flow prediction; Deep learning; Traffic flow prediction; LSTM; Naive Bayes; Image processing
• ISSN: 0262-8856
• DOI: 10.1016/j.imavis.2020.103971

In order to study and predict the freeway traffic safety and realize the traffic flow in the nonlinear big data environment, based on deep learning, the long-short-time memory (LSTM) model based on recurrent neural network is proposed. The traffic flow is predicted and the predicted value of traffic flow is compared with the actual value at different times. The mean absolute percentage error of LSTM prediction model is tested and compared with the error of time proximity, periodicity, and trend. At the same time, the naive Bayes algorithm is used to carry out image recognition processing for attributes such as license plate number and vehicle color to conduct vehicle matching. The data processing, training process, and model realization of the model are studied, and the accuracy of the naive Bayesian algorithm is tested. The results show that the predicted value of the traffic flow prediction model based on LSTM is not much different from the actual value. The average prediction error for the period from May 7, 2018 to May 9, 2018 is approximately 13.8%. When the time series is 6, the error of the prediction model based on LSTM is 10.72%, and the prediction errors of the three sequences of time proximity, periodicity, and trend are 15.66%, 17.59%, and 20.67%, respectively. Considering the three sequences comprehensively, the prediction model can achieve good prediction effect. The accuracy of the vehicle matching model based on naive Bayes is about 82.7%, which can meet the requirements of the system. Therefore, it can be concluded that the LSTM traffic flow prediction model based on deep learning and the image recognition vehicle matching model based on naive Bayes can realize the traffic safety prediction of freeway, which has great practical significance. •The long-short-time memory model is proposed to predict the freeway safety.•The naive Bayes algorithm is used in image recognition processing.•The LSTM traffic flow prediction model can realize the safety prediction.