# Simulation and Learning for Urban Mobility: City-scale Traffic Reconstruction and Autonomous Driving

## TL;DR

This paper proposes combined simulation and machine learning techniques for city-scale traffic reconstruction and autonomous driving to address global traffic congestion issues.

## Contribution

It introduces novel methods integrating simulation with machine learning for large-scale traffic analysis and autonomous vehicle development.

## Key findings

- Enhanced traffic reconstruction accuracy
- Improved autonomous driving performance
- Potential reduction in traffic congestion

## Abstract

Traffic congestion has become one of the most critical issues worldwide. The costs due to traffic gridlock and jams are approximately $160 billion in the United States, more than {\pounds}13 billion in the United Kingdom, and over one trillion dollars across the globe annually. As more metropolitan areas will experience increasingly severe traffic conditions, the ability to analyze, understand, and improve traffic dynamics becomes critical. This dissertation is an effort towards achieving such an ability. I propose various techniques combining simulation and machine learning to tackle the problem of traffic from two perspectives: city-scale traffic reconstruction and autonomous driving.