What question did this study set out to answer?

The study aims to enhance energy forecasting accuracy by integrating traffic data and predictive modelling techniques.

April 23, 2026Open Access

Predictive Modelling for Sustainable Traffic Management

Key Points

The study aims to enhance energy forecasting accuracy by integrating traffic data and predictive modelling techniques.
Compared various predictive models including Linear Regression, Random Forest, Decision Tree, SVR, LSTM, and GBR.
Utilized multi-source data such as traffic volume, energy consumption, and weather for model training and validation.
Analyzed model performance using metrics like R², MAE, RMSE, and MAPE.
The ensemble model achieved an R² of 0.9907, indicating very high predictive accuracy.
Mean Absolute Error (MAE) was 162.6 kWh, showing low average prediction error.
Mean Absolute Percentage Error (MAPE) was 3.2%, demonstrating high predictive reliability.

Abstract

Enhancing the accuracy in predicting energy needs involves combining predictive modelling with the integration of traffic flow, accidents, and congestion data. Given the real-time nature of traffic data, accurate energy forecasting is crucial for smart grid management. This study compares Linear Regression, Random Forest, Decision Tree, Support Vector Regressor (SVR), and LSTM neural networks, along with a Gradient Boosting Regressor (GBR) meta-learner ensemble. Using multi-source data (traffic volume, energy consumption, and weather), the ensemble model achieved the best performance with R² = 0.9907, MAE = 162.6 kWh, RMSE = 213.3 kWh, and MAPE = 3.2%. The work supports sustainable urban energy management aligned with SDG-7.

Predictive Modelling for Sustainable Traffic Management

Key Points

Abstract

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