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

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 £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.

交通拥堵已成为全球最关键的问题之一。在美国，因交通堵塞造成的损失约为1600亿美元，在英国超过130亿英镑，全球每年则超过1万亿美元。随着更多的大都市地区将面临日益严重的交通状况，分析、理解和改善交通动态的能力变得至关重要。本论文致力于实现这种能力。我提出了多种将模拟和机器学习相结合的技术，从两个角度解决交通问题：城市规模的交通重建和自动驾驶。