Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network

Jiang, Wenhao; Xiao, Yunpeng; Liu, Yanbing; Liu, Qilie; Li, Zheng

doi:10.1155/2022/5221362

Cited by 15 publications

(3 citation statements)

References 41 publications

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“…In a similar manner, In [29] presented Bi-GRCN, a Bi-directional Graph Recurrent Convolutional Network, as a method for predicting spatio-temporal traffic flow. Bi-GRCN employs a graph neural network structure to capture the geographical relationships between traffic regions and the temporal changes in traffic flow over time.…”

Section: Spatial-temporal Graph Neuralmentioning

confidence: 99%

“…Bi-GRCN [29] Integration of Temporal Components Model Complexity, Interpretability Informer model [30,31] Applicability in Intelligent Transport Applications Dependency on Training Data, Interpretability KNN algorithm [32] Simple Implementation Scalability, Imbalanced Data EEMD-ANN method [33] Multiscale Prediction, Adaptability to Varied Time Scales Complexity and Computational Cost, Generalization Across Traffic Conditions Ensemble learning approach [34] Robustness, Adaptability to Diverse Traffic Conditions Complexity, Training and Maintenance Costs ETPF [35] Adaptability to Nonlinear and Non-Gaussian Systems, Efficient Sampling and Resampling…”

Section: Computational Complexitymentioning

confidence: 99%

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Multilevel Learning for Enhanced Traffic Congestion Prediction Using Anomaly Detection and Ensemble Learning.

Khasawneh,

Daraghmeh,

Awasthi

et al. 2024

Preprint

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Traffic congestion is a major challenge in modern transportation systems, leading to increased travel time, greater fuel consumption, and higher levels of harmful emissions. Accurately forecasting traffic congestion at a high level of precision is crucial for implementing effective traffic control strategies, such as managing traffic lights. To generate accurate and reliable predictions, it is crucial to precisely detect abnormal traffic patterns in the data, particularly in densely populated urban areas. This study presents a novel approach for forecasting traffic congestion by employing a multilevel learning technique that integrates both anomaly detection and ensemble learning methods. As an initial step in the learning process, we evaluate different anomaly detection techniques to identify unusual traffic patterns in different locations over time. After predicting the anomaly, the data pattern is cleaned accordingly, and a set of baseline learner models is trained as a secondary learning process. The top-performing models are chosen and undergo an ensemble process to combine their results, evaluating both stacking and voting ensemble methods as a third learning process. We evaluate the efficacy of the proposed strategy by employing a real-world traffic dataset and diverse evaluation metrics. The dataset undergoes several preprocessing techniques, including the windowing process with various settings, to convert the time series data into frequency patterns and produce a more generalized model. The comparative results of this study demonstrate that the proposed multilevel learning approach achieves superior prediction accuracy compared to the baseline models, underscoring the influence of the multilevel learning strategy on the accuracy of predictions. This study highlights the utilization of anomaly detection and ensemble learning to enhance the precision of traffic congestion prediction, thereby promoting further exploration of this approach in intelligent transportation systems.

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Section: Spatial-temporal Graph Neuralmentioning

confidence: 99%

Section: Computational Complexitymentioning

confidence: 99%

Multilevel Learning for Enhanced Traffic Congestion Prediction Using Anomaly Detection and Ensemble Learning.

Khasawneh,

Daraghmeh,

Awasthi

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Facing increasingly severe trafc congestion pressure, various countries around the world have adopted a variety of countermeasures, the most important of which is the development of intelligent transportation systems (ITSs) [1,2]. Trafc fow parameter prediction, as the basis of an ITS, is the prerequisite and basis for trafc fow control and induced management [2][3][4]. Trafc fow parameter prediction refers to the estimation of trafc parameters in the future based on historical survey data.…”

Section: Introductionmentioning

confidence: 99%

A Combined Discrete Road Traffic State Prediction Model Based on GFD-ARMA-FISHER Analytical Framework

Yuan

Han

2022

Mathematical Problems in Engineering

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With the popularization of the Internet and the widespread application of mobile terminal, travelers are increasingly dependent on traffic information. It is particularly important to construct and develop more accurate discrete traffic state prediction models. Considering that most of the previous studies about traffic state prediction are essentially a prediction of specific parameters, this paper proposes a generalized fractional difference (GFD)-auto regressive moving average (ARMA)-Fisher combined model that can directly predict the discrete traffic state. First, using the original historical traffic flow data as training samples for cluster analysis, we obtain different traffic state classification samples. Then, using the Fisher discriminant method, we develop a discrete traffic state recognition model based on the traffic state classification samples. The model can help us identify the discrete state of the future traffic flow when we input a set of predicted traffic parameters to it. In order to improve the accuracy of the model prediction, this paper creatively proposes to apply the GFD method to the stabilization of original traffic flow data and then develop an ARMA model to predict the traffic parameters processed by GFD (GFD-ARMA). Last, we obtain the predicted discrete traffic state using the calibrated Fisher discrimination model whose inputs are the parameters predicted by the GFD-ARMA. The proposed method is tested using field data from CHangzhou, China. The results suggest that the developed GFD-ARMA-FISHER method shows a higher accuracy for traffic state prediction and is better than other similar methods.

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Traffic flow prediction: A 3D adaptive multi‐module joint modeling approach integrating spatial‐temporal patterns to capture global features

Ul Abideen,

Sun,

Sun

2024

Journal of Forecasting

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The challenges in citywide traffic flow are intricate, encompassing various factors like temporal and spatial dependencies, holidays, and weather. Despite the complexity, there are still research gaps in effectively incorporating these spatio‐temporal relations through deep learning. Addressing these gaps is crucial for tackling issues such as traffic congestion, public safety, and efficient traffic management within cities. This paper underscores notable research gaps, including the development of models capable of handling both local and global traffic flow patterns, integrating multi‐modal data sources, and effectively managing spatio‐temporal dependencies. In this paper, we proposed a novel model named 3D spatial–temporal‐based adaptive modeling graph convolutional network (3D(STAMGCN)) that addresses for traffic flow data in better periodicity modeling. In contrast to earlier studies, 3D(STAMGCN) approaches the task of traffic flow prediction as a periodic residual learning problem. This is achieved by capturing the input variation between historical time segments and the anticipated output for future time segments. Forecasting traffic flow, as opposed to a direct approach, is significantly simpler when focusing on learning more stationary deviations. This, in turn, aids in the training of the model. Nevertheless, the networks enable residual generation at each time interval through learned variations between future conditions and their corresponding weekly observations. Consequently, this significantly contributes to achieving more accurate forecasts for multiple steps ahead. We executed extensive experiments on two real‐world datasets and compared the performance of our model to state‐of‐the‐art (SOTA) techniques.

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Bi-GRCN: A Spatio-Temporal Traffic Flow Prediction Model Based on Graph Neural Network

Cited by 15 publications

References 41 publications

Multilevel Learning for Enhanced Traffic Congestion Prediction Using Anomaly Detection and Ensemble Learning.

Multilevel Learning for Enhanced Traffic Congestion Prediction Using Anomaly Detection and Ensemble Learning.

A Combined Discrete Road Traffic State Prediction Model Based on GFD-ARMA-FISHER Analytical Framework

Traffic flow prediction: A 3D adaptive multi‐module joint modeling approach integrating spatial‐temporal patterns to capture global features

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