Abstract
Traffic flow anomaly detection is helpful to improve the efficiency and reliability of detecting fault behavior and the overall effectiveness of the traffic operation. The data detected by the traffic flow sensor contains a lot of noise due to equipment failure, environmental interference, and other factors. In the case of large traffic flow data noises, a traffic flow anomaly detection method based on robust ridge regression with particle swarm optimization (PSO) algorithm is proposed. Feature sets containing historical characteristics with a strong linear correlation and statistical characteristics using the optimal sliding window are constructed. Then by providing the feature sets inputs to the PSO-Huber-Ridge model and the model outputs the traffic flow. The Huber loss function is recommended to reduce noise interference in the traffic flow. The L2 regular term of the ridge regression is employed to reduce the degree of overfitting of the model training. A fitness function is constructed, which can balance the relative size between the k-fold cross-validation root mean square error and the k-fold cross-validation average absolute error with the control parameter η to improve the optimization efficiency of the optimization algorithm and the generalization ability of the proposed model. The hyperparameters of the robust ridge regression forecast model are optimized by the PSO algorithm to obtain the optimal hyperparameters. The traffic flow data set is used to train and validate the proposed model. Compared with other optimization methods, the proposed model has the lowest RMSE, MAE, and MAPE. Finally, the traffic flow that forecasted by the proposed model is used to perform anomaly detection. The abnormality of the error between the forecasted value and the actual value is detected by the abnormal traffic flow threshold based on the sliding window. The experimental results verify the validity of the proposed anomaly detection model.
Highlights
Traffic flow anomaly detection plays an essential role in the traffic field
Data Description. e traffic flow data set used in the experiment came from a highway intersection in Changsha City and was collected by a single detector with a data interval of 5 minutes. ere were a small number of missing values in the traffic flow data set and the previous value of the missing value was used to fill in the missing points. e data sets containing 5 days of traffic flow were divided into the training set and the test set. e traffic flow from Saturday to Tuesday was used as the training set for the training model. e traffic flow on Wednesday was used as the test set to verify the performance of the trained model
To solve the problem of the large data noises in traffic flow, the traffic flow anomaly detection based on PSO-HuberRidge model is proposed. e strong robustness of the Huber function enables it to effectively reduce the influence of noise in traffic flow data on model training. e addition of the L2 regular term of the ridge regression in the objective function can reduce the risk of model overfitting. e sum of RMSEkcv and MAEkcv based on 10-fold cross-validation is constructed as the fitness function to improve the generalization ability of the model. e optimal model parameters can be obtained through the particle swarm optimization algorithm so as to improve the model performance
Summary
Traffic flow anomaly detection plays an essential role in the traffic field. Traffic jams have become a common thing in big cities and have received considerable critical attention. e traffic flow anomaly detection model can detect the abnormal traffic flow and can be achieved by constructing a traffic flow forecast model, which is helpful to avoid traffic congestion in time. e accurate forecast of traffic flow can provide a basis for real-time traffic control and provide support for the alleviation of traffic jams and the effective use of traffic networks, and the forecast result of traffic flow can directly affect the accuracy of traffic anomaly detection. E support vector regression machine can fit data based on the strategy of structural risk minimization, which is a common model in the field of traffic flow forecasts. To solve the problem of noise in traffic flow data, a Huber-Ridge traffic flow anomaly detection model with the particle swarm optimization (PSO) algorithm is proposed. E remaining part of the paper proceeds as follows: Section 2 introduces the theoretical information of the Huber-Ridge algorithm; Section 3 proposes the data preprocessing steps and the steps using PSO algorithm to optimize the Huber-Ridge model parameters; Section 4 illustrates the model evaluation indexes; Section 5 presents the experimental content which contains the comparison of the forecast models and the results of traffic flow anomaly detections; Section 6 is conclusions
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