Forecasting Traffic Volume Using Statistical and Artificial Intelligence Tools: Prophet, FbProphet, and Neural Prophet

Authors

  • Sigma Sathyan, Jagadeesha S. N.

Keywords:

Traffic Flow Prediction, Traffic Congestion, Short-term Traffic Prediction, Prophet, FbProphet, Neural Prophet

Abstract

The rising volume of vehicles on Indian roads causes congestion, accidents, and pollution levels, making solving traffic problems in Indian cities increasingly difficult. Transport networks may now function more efficiently thanks to Intelligent Transport Systems (ITS), a key solution that makes use of information and communication technologies. To achieve a complete approach, this research integrates policy initiatives, advanced technology such as ITS, and infrastructural upgrades. The use of predictive modeling methods in ITS implementations—such as Prophet, FbProphet, and Neural Prophet—becomes essential as technology develops. These models are essential to the development of sustainable and effective transport systems because they provide the ability to predict and react to dynamic traffic patterns. In this research, three traffic volume prediction models—Prophet, FbProphet, and Neural Prophet—are compared.

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References

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Published

12.06.2024

How to Cite

Sigma Sathyan. (2024). Forecasting Traffic Volume Using Statistical and Artificial Intelligence Tools: Prophet, FbProphet, and Neural Prophet. International Journal of Intelligent Systems and Applications in Engineering, 12(4), 1421 –. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/6416

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Research Article