Graph Neural Network for Traffic Forecasting: The Research Progress

Jiang, Weiwei; Luo, Jiayun; He, M.; Gu, Weixi

doi:10.3390/ijgi12030100

Cited by 36 publications

(10 citation statements)

References 203 publications

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“…Therefore, a straightforward linear prediction model is inadequate to address this multifaceted challenge. With the ongoing advancements in big data and artificial intelligence technology [1], an array of forecasting models have emerged and found success across diverse applications, including power forecasting [2][3][4], traffic flow forecasting [5][6][7], network traffic forecasting [8][9][10][11], and financial forecasting [12,13].…”

Section: Related Workmentioning

confidence: 99%

Energy consumption forecasting with deep learning

2024

J. Phys.: Conf. Ser.

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This research endeavors to create an advanced machine learning model designed for the prediction of household electricity consumption. It leverages a multidimensional time-series dataset encompassing energy consumption profiles, customer characteristics, and meteorological information. A comprehensive exploration of diverse deep learning architectures is conducted, encompassing variations of recurrent neural networks (RNNs), temporal convolutional networks (TCNs), and traditional autoregressive moving average models (ARIMA) for reference purposes. The empirical findings underscore the substantial enhancement in forecasting accuracy attributed to the inclusion of meteorological data, with the most favorable outcomes being attained through the application of time-series convolutional networks. Additionally, an in-depth investigation is conducted into the impact of input duration and prediction steps on model performance, emphasizing the pivotal role of selecting an optimal duration and number of steps to augment predictive precision. In summation, this investigation underscores the latent potential of deep learning in the domain of electricity consumption forecasting, presenting pragmatic methodologies and recommendations for household electricity consumption prediction.

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Section: Related Workmentioning

confidence: 99%

Energy consumption forecasting with deep learning

2024

J. Phys.: Conf. Ser.

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“…The [13] conducted a comprehensive analysis of 165 papers that discussed the application of data mining and machine learning to traffic management. The authors bring to the attention that there are no established procedures in the industry.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The final output for node 𝑣 after 𝐿 layers of GNN processing can be used to predict traffic conditions, such as congestion levels 𝐶 𝑣 , at node 𝑣, EQU (13).…”

Section: Graph Neural Networkmentioning

confidence: 99%

Enhancing Urban Traffic Management Through Hybrid Convolutional and Graph Neural Network Integration

Mohsin,

Mettu,

Madhuri

et al. 2024

JMC

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Traffic congestion has made city planning and citizen well-being difficult due to fast city growth and the increasing number of vehicles. Traditional traffic management fails to solve urban transportation's ever-changing issues. Traffic prediction and control systems are vital for enhancing Traffic Flow (TF) and minimizing congestion. Smart cities need advanced prediction models to regulate urban TF as traffic management becomes more complex. This paper introduces a hybrid Convolutional Neural Networks (CNN) and Graph Neural Networks (GNN) model for better real-time traffic management. The hybrid model combines CNNs' spatial feature extraction with GNNs' structural and relational data processing to analyze and predict traffic conditions. Traffic camera images are pre-processed to extract spatial characteristics. Traffic network graph construction is used for structural research. The model accurately captures traffic topology and space. The proposed method sequentially processes spatial data with CNNs and integrates them with GNNs. The final hybrid model is trained on one year of traffic data from diverse circumstances and events. The hybrid model is compared to CNN, GNN, and traditional Traffic Prediction Models (TPM) like ARIMA and SVM utilizing MAE, RMSE, and MAPE. The hybrid GNN+CNN model outperforms benchmark models with lower MAE, RMSE, and MAPE across several prediction intervals.

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“…For traffic prediction, the most important thing is the processing of traffic graph data [30]. In order to model spatial dependencies more effectively, more and more models are applying graph neural networks to the field of traffic prediction.…”

Section: Prediction Based On Graph Neural Networkmentioning

confidence: 99%

Multi-Spatial Scale Spatio-Temporal Transformer: A Refined Traffic Data Forecasting Method

张,

Zhang,

Liu

et al. 2023

Preprint

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Accurate traffic data forecasting is essential to improve the efficiency of intelligent transportation systems. Existing traffic prediction models only model spatial dependency based on the connectivity of roads, which overlooks the characteristic information of hidden spatial dependency and leads to a loss of prediction accuracy. In addition, there exists a strict relative positional relationship in the temporal dependency between traffic data, which is often overlooked by existing models, making it difficult to accurately model the temporal dependency. To solve these problems, this paper proposes a traffic data prediction method (MSS-STT) based on Multi-Spatial Scale Spatio-Temporal Transformer. MSS-STT first employs multiple specialized spatial Transformer networks to model different spatial scales in order to capture spatial dependencies and patterns at various levels. It also utilizes graph convolutional neural networks to extract static spatial structural features. Then, a gating mechanism is used to fuse the spatial dependencies from different spatial scales and the static spatial features. Finally, MSS-STT extracts different temporal dependencies by considering the order of time points and the varying contributions to the prediction from different relative positions between time points in historical traffic data. Experiments on three real-world datasets from the Caltrans Performance Measurement System (PeMS) show that the proposed MSS-STT model outperforms the state-of-the-art methods.

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Graph Neural Network for Traffic Forecasting: The Research Progress

Cited by 36 publications

References 203 publications

Energy consumption forecasting with deep learning

Energy consumption forecasting with deep learning

Enhancing Urban Traffic Management Through Hybrid Convolutional and Graph Neural Network Integration

Multi-Spatial Scale Spatio-Temporal Transformer: A Refined Traffic Data Forecasting Method

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