A combined method for short-term traffic flow prediction based on recurrent neural network

Lu, Saiqun; Zhang, Qiyan; Chen, Guangsen; Seng, Dewen

doi:10.1016/j.aej.2020.06.008

Cited by 93 publications

(36 citation statements)

References 23 publications

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“…Also, the validity of LSTM models has been verified in studies on prediction of short-term traffic flow and were found to provide high prediction accuracies for flow data [37]. Other studies have documented superior performance when ARIMA and long short-term memory (LSTM) neural networks were combined for short-term traffic flow prediction [38]. In another recent study, type-2 fuzzy LSTM (T2F-LSTM) was developed for long-term traffic volume prediction and extraction of spatial-temporal characteristics of traffic volumes and was found to achieve high prediction accuracies [39].…”

Section: Literature Reviewmentioning

confidence: 86%

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

Abduljabbar

Dia

Tsai

2021

Journal of Advanced Transportation

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This paper presents the development and evaluation of short-term traffic prediction models using unidirectional and bidirectional deep learning long short-term memory (LSTM) neural networks. The unidirectional LSTM (Uni-LSTM) model provides high performance through its ability to recognize longer sequences of traffic time series data. In this work, Uni-LSTM is extended to bidirectional LSTM (BiLSTM) networks which train the input data twice through forward and backward directions. The paper presents a comparative evaluation of the two models for short-term speed and traffic flow prediction using a common dataset of field observations collected from multiple freeways in Australia. The results showed BiLSTM performed better for variable prediction horizons for both speed and flow. Stacked and mixed Uni-LSTM and BiLSTM models were also investigated for 15-minute prediction horizons resulting in improved accuracy when using 4-layer BiLSTM networks. The optimized 4-layer BiLSTM model was then calibrated and validated for multiple prediction horizons using data from three different freeways. The validation results showed a high degree of prediction accuracy exceeding 90% for speeds up to 60-minute prediction horizons. For flow, the model achieved accuracies above 90% for 5- and 10-minute prediction horizons and more than 80% accuracy for 15- and 30-minute prediction horizons. These findings extend the set of AI models available for road operators and provide them with confidence in applying robust models that have been tested and evaluated on different freeways in Australia.

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Section: Literature Reviewmentioning

confidence: 86%

Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction

Abduljabbar

Dia

Tsai

2021

Journal of Advanced Transportation

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“…In recent years, with the extensive application of deep learning methods in traffic flow prediction, an increasing number of traffic researchers have been committed to proposing combined prediction models with excellent performance and efficiency. Initial results have been achieved, and many combined models have been proposed, such as CNN combined with LSTM [11,13], the combined model of CNN and ARIMA [10], and other combined models [30].…”

Section: Related Workmentioning

confidence: 99%

“…As the typical time series, traffic data also have the general characteristics of a nonlinear time series, which is reflected in nonstationarity, periodic distribution of traffic parameters and spatiotemporal correlation. Some recent studies indicate that the traffic time series exhibits stochasticity and uncertainty at different time periods [8,10,11,31]. e main purpose of predicting short-term traffic speed is to provide the accurate traffic speed in the next five minutes, ten minutes, or fifteen minutes and to provide support for improving the operational efficiency of urban roads.…”

Section: Problem Descriptionmentioning

confidence: 99%

“…With good performance in other fields, many deep learning methods (such as convolutional neural network (CNN) models [5], recurrent neural network (RNN) models, and long short-term memory (LSTM) models [6][7][8][9]) have been introduced to predict short-term traffic flow and have achieved better prediction performance than traditional forecasting methods. In addition, the combined model often has a better predictive effect than the single model [10][11][12][13]. For example, Lu et al [10] proposed a combined model of ARIMA and LSTM, and Zheng et al proposed the Conv-LSTM model based on the attention mechanism [11].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the combined model often has a better predictive effect than the single model [10][11][12][13]. For example, Lu et al [10] proposed a combined model of ARIMA and LSTM, and Zheng et al proposed the Conv-LSTM model based on the attention mechanism [11]. Yu et al [12] proposed a low-rank representation (LRR) and dynamic mode decomposition (DMD) combined model (LRDMD).…”

Section: Introductionmentioning

confidence: 99%

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

Huang

Pian

et al. 2020

Journal of Advanced Transportation

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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.

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