Predictive methods for CO2 emissions and energy use in vehicles at intersections
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View abstract on PubMed
Summary
This summary is machine-generated.This study developed advanced models to accurately predict CO2 emissions and vehicle energy use at busy urban intersections. Extreme Gradient Boosting (XGBoost) showed the highest accuracy, improving traffic management and infrastructure planning.
Area Of Science
- Environmental Science
- Transportation Engineering
- Data Science
Background
- Current emission models struggle with complex urban intersection dynamics, particularly stop-and-go traffic.
- Accurate prediction of vehicle emissions and energy consumption is crucial for urban sustainability.
- High-traffic intersections are significant contributors to urban air pollution and energy waste.
Purpose Of The Study
- To develop and validate novel predictive models for CO2 emissions and vehicle energy consumption at urban intersections.
- To enhance the accuracy of traffic emission modeling in real-world urban driving conditions.
- To provide tools for improved road infrastructure planning and traffic management strategies.
Main Methods
- Utilized machine learning algorithms including linear regression, LASSO, Ridge, Random Forest, and Extreme Gradient Boosting (XGBoost).
- Applied Density-Based Spatial Clustering of Applications with Noise (DBSCAN) for intersection-specific data grouping.
- Collected real-world driving data using portable emissions measurement systems and the Hioki 3390 power analyzer.
Main Results
- Extreme Gradient Boosting (XGBoost) demonstrated superior accuracy in predicting emissions and energy consumption compared to other models.
- The developed models were successfully validated and applied in traffic simulation software (Vissim).
- DBSCAN effectively clustered intersection-specific data, enabling more targeted and accurate analysis.
Conclusions
- The advanced predictive models offer a significant improvement over existing methods for urban intersection traffic analysis.
- These findings support the optimization of energy use and reduction of CO2 emissions in urban transportation networks.
- The study provides a foundation for data-driven decision-making in urban planning and traffic management to mitigate environmental impact.
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