Traffic Flow Prediction Using An Improved Fuzzy Convolutional LSTM Algorithm

Abstract

Intelligent transportation systems (ITS), is producing large amounts of data. The generated data is used for designing smart  transportation systems. These raw data must be converted to valuable information for transportation planning and management. Deep learning algorithms  have a variety of applications. In this paper, an improved fuzzy convolutional approach is proposed. The proposed model is designed to learn traffic flow features layer by layer through a supervised learning, non-parametric algorithm. The traffic information has been captured from UCI machine learning repository and experimented with a proposed algorithm to capture traffic flow information.The proposed algorithm is evaluated against prediction  metrics such as root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE).The proposed method stands out other existing algorithms and has superior performance in traffic flow forecasting.