A Survey on Traffic Flow Prediction Methods

Irawan, Kevin; Yusuf, Rahadian; Prihatmanto, Ary Setijadi

doi:10.1109/icidm51048.2020.9339675

Cited by 9 publications

(4 citation statements)

References 13 publications

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“…Emergencies and exceptional Events: Besides dealing with emergencies such as accidents or natural disasters, urban traffic management must also be prepared to deal with exceptional events that might impede traffic flow. Having contingency plans and coordinating with emergency services is critical [13].…”

Section: Data Managementmentioning

confidence: 99%

A Graph Neural Network-based Traffic Flow Prediction System with Enhanced Accuracy and Urban Efficiency

E.V.N.Jyothi

2024

jes

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This research culminates in a robust Traffic Flow Prediction System poised to redefine the landscape of Intelligent Transportation Systems (ITS). Our findings highlight the substantial promise of this system through a meticulously structured methodology spanning data generation, dynamic network construction, multi-modal data integration, and the employment of state-of-the-art Graph Neural Networks (GNNs). Notably, the "Current Framework" stands out, demonstrating superior performance over alternative regression models, substantiated by a remarkable 35% reduction in Mean Squared Error (MSE) and a commendable 7% increase in R-squared (R²). Nevertheless, this system is not without its caveats. Ongoing model refinement, adaptability to the ever-evolving traffic landscape, and scalability considerations are essential for future exploration. These achievements usher in a new era for traffic management, with the potential to curtail congestion by up to 20%, bolster safety measures, and usher in an era of enhanced urban transportation efficiency.

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Section: Data Managementmentioning

confidence: 99%

A Graph Neural Network-based Traffic Flow Prediction System with Enhanced Accuracy and Urban Efficiency

E.V.N.Jyothi

2024

jes

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“…Furthermore, two feedforward neural network layers with non-linear activation are used to improve the prediction ability, and its output is added to the input of the self-attention mechanism to build the residual connection for stable training using Eq. (25).…”

Section: Transformermentioning

confidence: 99%

“…For instance, [22,23,24] reviewed models focusing on addressing short-term traffic prediction only. On the other hand, [25,26,27] targeted more classical approaches including statistical-based and machine learning (ML)-based models while [24,28,29] focused on deep learning models. We also note another work [30] which reviewed traffic prediction from the perspective of smart cities and highlighted existing traffic data sources and data models for fusing traffic data in ITSs.…”

Section: Introductionmentioning

confidence: 99%

Hybrid deep learning models for traffic prediction in large-scale road networks

Chai

Duanmu

et al. 2023

Information Fusion

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“…As one of the three key traffic parameters, average road speed is an important tool to reflect the traffic state and has gradually become one of the major constituents of current traffic prediction. Shortterm traffic speed prediction can use real-time traffic data to predict the traffic conditions within the next few minutes, and accurate and timely predictions play a key role in traffic condition improvement, traffic event prediction, and traffic travel organization [3].…”

Section: Introductionmentioning

confidence: 99%

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

Sun

Shao

et al. 2021

Sensors

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Timely and accurate traffic speed predictions are an important part of the Intelligent Transportation System (ITS), which provides data support for traffic control and guidance. The speed evolution process is closely related to the topological structure of the road networks and has complex temporal and spatial dependence, in addition to being affected by various external factors. In this study, we propose a new Speed Prediction of Traffic Model Network (SPTMN). The model is largely based on a Temporal Convolution Network (TCN) and a Graph Convolution Network (GCN). The improved TCN is used to complete the extraction of time dimension and local spatial dimension features, and the topological relationship between road nodes is extracted by GCN, to accomplish global spatial dimension feature extraction. Finally, both spatial and temporal features are combined with road parameters to achieve accurate short-term traffic speed predictions. The experimental results show that the SPTMN model obtains the best performance under various road conditions, and compared with eight baseline methods, the prediction error is reduced by at least 8%. Moreover, the SPTMN model has high effectiveness and stability.

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A Survey on Traffic Flow Prediction Methods

Cited by 9 publications

References 13 publications

A Graph Neural Network-based Traffic Flow Prediction System with Enhanced Accuracy and Urban Efficiency

A Graph Neural Network-based Traffic Flow Prediction System with Enhanced Accuracy and Urban Efficiency

Hybrid deep learning models for traffic prediction in large-scale road networks

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

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