Air Pollution Prediction
& Reduction Strategies
Using SUMO simulation and Machine Learning to predict and reduce urban air pollution in real-time
Pioneering Urban Air Quality Research
Urban air pollution remains one of the most pressing environmental challenges of our time, impacting millions of people across the globe. This groundbreaking research integrates SUMO (Simulation of Urban Mobility) with advanced machine learning techniques to predict and mitigate air pollution in real-time.
Through detailed simulation of traffic dynamics and emissions data from Flushing, NY, we developed predictive models capable of identifying air pollution hotspots and proposing targeted intervention strategies for healthier urban environments.
Advanced Research Methodology
Combining simulation technology with machine learning for unprecedented air quality insights
SUMO Simulation
Urban mobility simulation capturing realistic traffic patterns and emission data from Flushing, NY
Machine Learning Models
Random Forest Regressor and XGBoost algorithms for accurate pollution prediction
Emission Analysis
Comprehensive tracking of CO₂, NOx, HC, and particulate matter (PMx) pollutants
Real-time Prediction
Live pollution hotspot identification and intervention strategy recommendations
Geospatial Mapping
OSMWebWizard integration for accurate urban network representation
Performance Analysis
Comprehensive model validation with marginal error assessment and optimization
Research Technology Stack
SUMO
Python
Scikit-learn
XGBoost
Pandas
NumPy
Matplotlib
TensorFlow
Research Results & Visualizations
Data-driven insights from comprehensive urban air quality analysis
SUMO Traffic Network Simulation
Flushing, NY traffic patterns and vehicle movement analysis
Machine Learning Model Performance
Random Forest vs XGBoost comparison and accuracy metrics
Real-time Emission Monitoring
Live NOx, CO₂, and particulate matter tracking dashboard
Pollution Hotspot Mapping
Geospatial analysis of high-emission zones and intervention strategies
Key Research Findings
Research Impact & Applications
Urban Planning
Provides city planners with data-driven insights for implementing targeted emission reduction strategies and optimizing traffic flow patterns.
Environmental Policy
Supports environmental agencies in developing evidence-based policies for urban air quality management and public health protection.
Smart City Integration
Enables real-time monitoring systems that can be integrated into smart city infrastructure for automated pollution response.
Future Research
Establishes foundation for expanding research to include broader vehicle types and larger metropolitan areas.
Explore the Research
Dive deeper into the methodology, code implementation, and detailed findings of this groundbreaking urban air quality research.
Academic Citation
Mohammed Y. Hossain & Billy M. Kyaw. (2024). "Air Pollution Prediction and Reduction Strategies in Urban Areas using SUMO and Machine Learning." Master's Thesis, New York Institute of Technology.