Lag‐related noise shrinkage stacked LSTM network for short‐term traffic flow forecasting

Abstract

Abstract For the transport networks only equipped with sparse or isolated detectors, short‐term traffic flow forecasting faces the following problems: (1) there are only temporal information and no spatial information; (2) the noises in the traffic flow significantly affect the forecasting performance. In this paper, a lag‐related noise shrinkage stacked long short‐term memory (LSTM) network is proposed for the traffic flow forecasting task only related to temporal information. To extract effective temporal features, the optimal time lags are selected in the traffic flow and converted into lag‐related multi‐dimensional data. Then, a discrete wavelet threshold denoising shrinkage algorithm is designed to filter the noises to construct a more reliable training set. A multi‐level stacked LSTM network is employed to learn the features of the training set to map the past traffic flow to the future flow. Four benchmark datasets are to evaluate the forecasting performance by extensive experiments. The comparison with the state‐of‐the‐art models demonstrates an average improvement of 7.28% in MAPE and 6.02% in RMSE. In addition, the proposed method has been applied in the Guilin Travel Network Bus Intelligent Dispatching System. It improves the utilization of the vehicles and reduces operating costs.

Keywords

• intelligent transportation systems
• traffic information systems
• traffic modeling
• management and control