Application on traffic flow prediction of machine learning in intelligent transportation

With the development of human society, the shortcomings of the existing transportation system become increasingly prominent, so people hope to use advanced technology to achieve intelligent transportation. However, the recognition rate of most methods of detecting video vehicles is too low and the process is complicated. This paper uses machine learning theory to design a variety of pattern classifiers, including Adaboost, SVM, RF, and SVR algorithms, to classify vehicles. Support vector regression (SVR) is a support vector regression algorithm based on the basic principles of support vector machine (SVM) and then generalized to the regression problem. This paper proposes a short-term traffic flow prediction model based on SVR and optimizes SVM parameters to form an improved SVR short-term traffic flow prediction model. It can be obtained from experiments that the classification error rate of support vector regression (SVR) is the lowest (3.22%). According to the prediction of morning and night peak hours, this paper concludes that the MAPE of SVR is reduced by 19.94% and 42.86%, respectively, and the RMSE is reduced by 29.71% and 47.22%, respectively. Experiments show that the improved algorithm can obtain the optimal parameter combination of SVR faster and better and can effectively improve the accuracy of traffic flow prediction. The target tracking pedestrian counting method proposed in this paper has significantly improved the counting accuracy. The calculation of HOG features can be further expanded, such as the selection of neighborhoods when calculating HOG features, and finally a more efficient pedestrian counting framework is implemented.