A Machine Learning and Fuzzy Heterogeneous Data Sources for Traffic Flow Prediction System

Abstract

The information about Internet traffic should be accurate and timely important for various applications like admission control, congestion control, allocation of bandwidth, and anomaly detection. The prediction of traffic flow is vital for the management and policy of transportation. Mostly, earlier traffic flow prediction techniques utilized simple models for traffic prediction but still these techniques do not meet the desires of various applications of real world. To overcome this, machine learning and fuzzy heterogeneous data sources for Traffic Flow Prediction System (ML-TFPS) is designed and analyzed in this paper. Firstly, the time series model is utilized as a benchmark based on traffic data history for predicting the flow of traffic. Then, heterogeneous data will be integrated for Linear Regression (LR), extreme learning machine (ELM) with machine learning (ML) and fuzzy Traffic Flow Prediction System (MF-TFPS) model. To predict the features of traffic flow, Spark parallelization technology is utilized in described method. MF-TFPS will be intuitively visualized the results of traffic flow prediction. The MF-TFPS will be validated basing on the traffic flow of real data of San Francisco. Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE) parameters will be utilized in this study for performance evaluation. From results it is clear that, MF-TFPS with RVM performs well in prediction of traffic flow than the LR, ELM models. The heterogeneous data will be more informative compared to the actual traffic data which is utilized by other researchers, and nonlinear technique utility is demonstrated that can resulting an improvement in the prediction accuracy of traffic flow.