Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Zhu, Zheng; Peng, Bo; Xiong, Chenfeng; Zhang, Lei

doi:10.1002/atr.1392

Cited by 115 publications

(37 citation statements)

References 24 publications

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“…As one of non-parametric and data-driven methods, an enhanced K-nearest neighbor(K-NN) algorithm applied in short-term traffic flow prediction based on identify similar traffic patterns [52]. Zhu et al studied a linear conditional Gaussian (LCG) Bayesian network(BN) model for short-term traffic flow prediction, which considers spatial-temporal characteristics as well as speed information [53]. To tackle the task of estimating the number of people who moved between cells, Akagi et al [54] developed a probabilistic model based on collective graphical models, which has considered movements to remote cells.…”

Section: Traditional Machine Learning Methodsmentioning

confidence: 99%

Urban flow prediction from spatiotemporal data using machine learning: A survey

et al. 2020

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Section: Traditional Machine Learning Methodsmentioning

confidence: 99%

Urban flow prediction from spatiotemporal data using machine learning: A survey

et al. 2020

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“…Later, a typical parametric time-series model autoregressive integrated moving average (ARIMA) model [23] was introduced to identify the pattern by decomposing long-term trends and seasonal patterns. However, it suffers from the tendency to concentrate on the mean value of the time-series and unable to predict the extremes [24]. The family of the ARIMA-based model, such as seasonal ARIMA models [25], and Kalman filter model [26], use a vast historical database for model development and are also very sensitive to the traffic data.…”

Section: Related Workmentioning

confidence: 99%

City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN

et al. 2020

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Traffic congestion is a significant problem faced by large and growing cities that hurt the economy, commuters, and the environment. Forecasting the congestion level of a road network timely can prevent its formation and increase the efficiency and capacity of the road network. However, despite its importance, traffic congestion prediction is not a hot topic among the researcher and traffic engineers. It is due to the lack of high-quality city-wide traffic data and computationally efficient algorithms for traffic prediction. In this paper, we propose (i) an efficient and inexpensive city-wide data acquisition scheme by taking a snapshot of traffic congestion map from an open-source online web service; Seoul Transportation Operation and Information Service (TOPIS), and (ii) a hybrid neural network architecture formed by combing Convolutional Neural Network, Long Short-Term Memory, and Transpose Convolutional Neural Network to extract the spatial and temporal information from the input image to predict the networkwide congestion level. Our experiment shows that the proposed model can efficiently and effectively learn both spatial and temporal relationships for traffic congestion prediction. Our model outperforms two other deep neural networks (Auto-encoder and ConvLSTM) in terms of computational efficiency and prediction performance. INDEX TERMS Convolutional neural network, long short-term memory, partially convolutional neural network, spatiotemporal feature, traffic congestion forecasting, transport network.

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“…Zheng et al proposed three classic non-parametric models to improve traffic speed prediction performance with reduced data dimensionality [15]. A Bayesian network approach was presented for traffic flow prediction [16]. An online learning weighted support vector regression (SVR) was proposed to forecast short-term traffic flow [17].…”

Section: Literature Reviewmentioning

confidence: 99%

A Multitask Learning Model for Traffic Flow and Speed Forecasting

Zhang

Zhu

et al. 2020

IEEE Access

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Intelligent Transportation Systems (ITS) research and applications benefit from accurate shortterm traffic state forecasting. To improve the forecasting accuracy, this paper proposes a deep learning based multitask learning Gated Recurrent Units (MTL-GRU) with residual mappings. To enhance the performance of the MTL-GRU, feature engineering is introduced to select the most informative features for the forecasting. Then, based on real-world datasets, numerical results show that the MTL-GRU can well estimate traffic flow and speed simultaneously, and performs better than other counterparts. Experiments also show that the deep learning based MTL-GRU model can overpower the bottleneck caused by enlarging training datasets and continue to gain benefits. The results suggest the proposed MTL-GRU model with residual mappings is promising to forecast short-term traffic state. INDEX TERMS Short-term traffic forecasting, deep learning, multitask learning, feature engineering.

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Short‐term traffic flow prediction with linear conditional Gaussian Bayesian network

Cited by 115 publications

References 24 publications

Urban flow prediction from spatiotemporal data using machine learning: A survey

Urban flow prediction from spatiotemporal data using machine learning: A survey

City-Wide Traffic Congestion Prediction Based on CNN, LSTM and Transpose CNN

A Multitask Learning Model for Traffic Flow and Speed Forecasting

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