Modelling Pollution from Traffic, Using Smartphone Data and Python

Overview

Explore Python-based modeling of traffic pollution using smartphone data, combining traffic assignment, econometric models, and congestion analysis for urban environmental monitoring and mitigation.

Syllabus

• Introduction to Traffic Pollution and Smartphone Data
Overview of traffic pollution sources
Role of smartphone data in pollution monitoring
Introduction to key Python libraries
• Traffic Assignment Models
Principles of traffic assignment
Implementing traffic models in Python
Analyzing traffic patterns and vehicle emissions
• Econometric Models for Pollution
Basics of econometric modeling
Data requirements and preprocessing
Building econometric models in Python
• Congestion Analysis
Understanding traffic congestion and its effects
Techniques for measuring and modeling congestion
Application of congestion models in pollution prediction
• Pollution Data Collection using Smartphones
Overview of smartphone sensors and data collection methods
Handling data privacy and ethical considerations
Preprocessing and analyzing smartphone data for pollution modeling
• Integrating Models for Urban Pollution Monitoring
Combining traffic, econometric, and congestion models
Designing comprehensive models for urban environments
Case studies and applications
• Python Implementation and Tools
Introduction to Python tools and frameworks for pollution modeling
Practical implementation of models
Visualization and interpretation of results
• Environmental Policy Implications
Understanding how models inform policy decisions
Communication of findings to stakeholders
Strategies for urban pollution mitigation
• Course Projects and Presentation
Development of a capstone project on a chosen urban area
Presentation and peer-review of projects
Feedback and course wrap-up

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Subjects

Conference Talks