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What is Weather?

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Precipitation and Co-precipitation

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Related Experiment Video

Updated: Sep 6, 2025

Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street

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Published on: January 20, 2023

Road Traffic Forecast Based on Meteorological Information through Deep Learning Methods.

Fernando José Braz¹, João Ferreira², Francisco Gonçalves²

¹Instituto Federal Catarinense Campus Araquari, Araquari 89245-000, Brazil.

Sensors (Basel, Switzerland)

|June 24, 2022

Summary

Accurate road traffic flow forecasting is crucial for safety and planning. Integrating weather data with machine learning models, like Convolutional Neural Networks (CNNs), significantly improves prediction accuracy for traffic flow.

Keywords:

deep learning highway traffic method comparison weather-based traffic prediction

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Last Updated: Sep 6, 2025

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Area of Science:

Traffic Engineering
Meteorology
Data Science

Background:

Effective traffic flow forecasting is vital for urban planning, traffic management, and public safety.
Traffic patterns, especially in high-demand areas like summer resorts, are heavily influenced by weather conditions.

Purpose of the Study:

To investigate if incorporating meteorological data into traffic flow prediction models enhances accuracy.
To evaluate the performance of various machine learning models for traffic flow forecasting.

Main Methods:

Utilized datasets combining traffic flow, radar, and meteorological sensor information.
Implemented and compared Long Short-Term Memory (LSTM), autoregressive LSTM, and Convolutional Neural Network (CNN) models.
Assessed model performance based on prediction accuracy and computational time.

Main Results:

Weather conditions were identified as essential factors for accurate traffic flow prediction.
Models demonstrated the capability to forecast traffic flow with reasonable accuracy for one-hour periods.
The CNN model achieved the lowest prediction error and fastest prediction generation.

Conclusions:

Integrating meteorological data significantly improves traffic flow forecasting models.
Machine learning, particularly CNNs, offers a viable solution for precise, short-term traffic flow prediction.
Accurate traffic forecasts enable better traffic management and enhance traveler experiences.