A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting

Abstract

Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. However, the urban traffic speed has strong temporal, spatial correlation and the characteristic of complex nonlinearity and randomness, which makes it challenging to accurately and efficiently forecast short-term traffic speeds. We investigate the relevant literature and found that although most methods can achieve good prediction performance with the complete sample data, when there is a certain missing rate in the database, it is difficult to maintain accuracy with these methods. Recent studies have shown that deep learning methods, especially long short-term memory (LSTM) models, have good results in short-term traffic flow prediction. Furthermore, the attention mechanism can properly assign weights to distinguish the importance of traffic time sequences, thereby further improving the computational efficiency of the prediction model. Therefore, we propose a framework for short-term traffic speed prediction, including data preprocessing module and short-term traffic prediction module. In the data preprocessing module, the missing traffic data are repaired to provide a complete dataset for subsequent prediction. In the prediction module, a combined deep learning method that is an attention-based LSTM (ATT-LSTM) model for predicting short-term traffic speed on urban roads is proposed. The proposed framework was applied to the urban road network in Nanshan District, Shenzhen, Guangdong Province, China, with a 30-day traffic speed dataset (floating car data) used as the experimental sample. Results show that the proposed method outperforms other deep learning algorithms (such as recurrent neural network (RNN) and convolutional neural network (CNN)) in terms of both calculating efficiency and prediction accuracy. The attention mechanism can significantly reduce the error of the LSTM model (up to 12.4%) and improves the prediction performance.

Traffic Information Enrichment: Creating Long-Term Traffic Speed Prediction Ensemble Model for Better Navigation through Waypoints

“…Nevertheless, while the prediction of MSTFLN is in hours, the prediction of the CNN-LSTM model is restricted to dozens of minutes. Very good results for the short-term prediction were found by the authors of [51], who presented the attention-based LSTM (ATT-LSTM) model for short-term traffic speed forecasting. The average MAE of their ATT-LSTM model was 3.26 km/h for 15 min prediction.…”

An Efficient Short-Term Traffic Speed Prediction Model Based on Improved TCN and GCN

“…Aniekan et al [21] proposed a bi-directional Long Short-Term Memory Neural Network (LSTM-NN) model and combined it with weather data to predict the short-term traffic speed of the main road through Manchester. Wu et al [22] combined LSTM with an attention mechanism to create the ATT-LSTM model and applied it to road traffic speed prediction in Shenzhen. Results showed that the attention mechanism greatly improved prediction accuracy.…”

Applying Clustered KNN Algorithm for Short-Term Travel Speed Prediction and Reduced Speed Detection on Urban Arterial Road Work Zones

“…In addition, they have been proven to encounter poor performance compared to nonparametric methods in unstable traffic conditions and complex road settings [12,13]. Neural network (NN), K-nearest neighbors (KNN) [14], Bayesian network (BN) [15], and support vector machine (SVM) [10,16] are the representatives of nonparametric algorithms [17]. Such approaches were advantageous as they are free of assumptions regarding the underlying model formulation and the uncertainty in estimating the model parameters [18].…”

“…Such approaches were advantageous as they are free of assumptions regarding the underlying model formulation and the uncertainty in estimating the model parameters [18]. Recently, studies using deep learning techniques have been conducted to improve the prediction accuracy of traffic conditions [17,19]. ese include long short-term memory (LSTM) [20,21], deep belief network (DBN) [22], stacked autoencoder (SAE) [23,24], and convolutional neural network (CNN) [25], which were widely used and had achieved good results in predicting traffic conditions [26].…”