Long-Term Traffic Prediction Based on LSTM Encoder-Decoder Architecture

Zhumei Wang · Xing Su · Zhiming Ding

267 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Traffic Prediction and Management Techniques Traffic control and management Hydrological Forecasting Using AI

Abstract

Accurate traffic flow prediction is becoming increasingly important for transportation planning, control, management, and information services of successful. Numerous existing models focus on short-term traffic forecasts, but effective long-term forecasting of traffic flows have become a challenging issue in recent years. To solve this problem, this paper proposes a deep learning architecture which consisting of two parts: the long short-term memory encoder-decoder structure at the bottom and the calibration layer at the top. In the encoder-decoder model, we propose an hard attention mechanism based on learning similar patterns to enhance neuronal memory and reduce the accumulation of error propagation. To correct some of the missing details, we design a control gate in the calibration layer to learn the predicted data in groups according to different forms. The proposed method is evaluated on real-world datasets and compared with other state-of-the-art methods. It is verified that our model can accurately learn local feature and long-term dependence, and has better accuracy and stability in long-term sequence prediction.

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References

2018Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting 2014Traffic Flow Prediction With Big Data: A Deep Learning Approach 2015Long short-term memory neural network for traffic speed prediction using remote microwave sensor data 2003Modeling and Forecasting Vehicular Traffic Flow as a Seasonal ARIMA Process: Theoretical Basis and Empirical Results 2017LSTM network: a deep learning approach for short‐term traffic forecast 2014Deep Architecture for Traffic Flow Prediction: Deep Belief Networks With Multitask Learning 2017Deep learning for short-term traffic flow prediction 1996Combining kohonen maps with arima time series models to forecast traffic flow

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2023Long-term traffic flow forecasting using a hybrid CNN-BiLSTM model

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