Traffic Flow Prediction Using Spatiotemporal Analysis and Encoder-Decoder Network

Hong, Genxuan; Wang, Zhanquan; Gao, Fuchen; Ji, Hengming

doi:10.1145/3457682.3457726

Cited by 1 publication

(1 citation statement)

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“…The collection and analysis of data from diverse sources, such as sensors, cameras, and GPS devices, is essential in urban traffic management. Managing and integrating this data may be complicated and complex [11] Behavioral variables: Addressing behavioral variables such as driver conduct, adherence to traffic laws, and public understanding of traffic management measures is also part of managing urban traffic. Encouraging responsible conduct and educating the public about traffic rules and regulations [12].…”

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

View full text Add to dashboard Cite

show abstract

Traffic Flow Prediction Using Spatiotemporal Analysis and Encoder-Decoder Network

Cited by 1 publication

References 15 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

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