Quantum mechanics

Algorithm

Engineering

Traffic flow (computer networking)

Computer security

Stability (learning theory)

Upstream (networking)

Telecommunications

Data mining

Civil engineering

Computer science

Physics

Mathematics

Flow (mathematics)

Long short term memory

Autoencoder

Artificial intelligence

Geometry

Mean squared prediction error

Term (time)

Approximation error

Artificial neural network

Intelligent transportation system

Recurrent neural network

Machine learning

Sensors

Smart cities can effectively improve the quality of urban life. Intelligent Transportation System (ITS) is an important part of smart cities. The accurate and real-time prediction of traffic flow plays an important role in ITSs. To improve the prediction accuracy, we propose a novel traffic flow prediction method, called AutoEncoder Long Short-Term Memory (AE-LSTM) prediction method. In our method, the AutoEncoder is used to obtain the internal relationship of traffic flow by extracting the characteristics of upstream and downstream traffic flow data. Moreover, the Long Short-Term Memory (LSTM) network utilizes the acquired characteristic data and the historical data to predict complex linear traffic flow data. The experimental results show that the AE-LSTM method had higher prediction accuracy. Specifically, the Mean Relative Error (MRE) of the AE-LSTM was reduced by 0.01 compared with the previous prediction methods. In addition, AE-LSTM method also had good stability. For different stations and different dates, the prediction error and fluctuation of the AE-LSTM method was small. Furthermore, the average MRE of AE-LSTM prediction results was 0.06 for six different days.

An AutoEncoder and LSTM-Based Traffic Flow Prediction Method