21st Century Traffic Control and Management for Large Congested Cities: Exploiting Pervasive Vehicular Automation, Connectivity and Sharing

该研究计划的目标是开发世纪智能城市的综合交通控制系统，利用车辆自动化和连接以及共享移动服务的新兴创新。 加拿大和世界各地的城市越来越忙碌，这是一种不断上升的趋势。 人口和人类活动不断增长，而交通基础设施却受到空间、财政和环境制约的限制。随着汽车自动化和连接（例如谷歌汽车）以及车辆和乘车共享（例如uberPOOL）的趋势，我们的交通系统和城市正处于范式转变的边缘，仅次于20世纪初引入汽车本身的第一次革命。技术和新的交通服务正在迅速改变我们的城市如何形成以及我们如何在其中移动的基本面。然而，我们尚未对后果做好充分准备。 未来智慧城市中智慧交通最大胆的愿景是自动化、互联化、绿色化和共享化。然而，净效应还远不能确定，它可能是显著的积极影响，也可能是严重的消极影响，这取决于我们如何明确地设计和利用这些创新，以及如何管理它们在社会中的发展和采用。我们还没有建立科学和工具来同时管理大城市背景下的需求（路上的旅行）和供应（基础设施容量和效率）。因此，这项研究的重点是城市中大型复杂的交通系统，而不是车辆技术本身。我们专注于如何利用颠覆性的交通技术和服务，以最大限度地提高社会效益，同时避免潜在的风险和意想不到的后果。 更具体地说，我们将使用现代机器学习结合基础交通流理论和经济学来：（1）量化变革性技术对交通系统及其性能的影响，（2）创建交通控制，优化和管理方法，工具，战略和政策，引导不可避免的转型朝着积极的方向发展，改善经济，社会，以及未来城市的环境可持续性。我们将开发出行定价系统，以避免加剧城市扩张和交通拥堵。我们还将利用高水平的自动化和无处不在的连接来建立新的交通控制系统，以提高基础设施的容量。新的制度和政策将利用技术、最少的公共基础设施和合理的成本来实施和执行。 这项研究的意义在于为加拿大进入智能城市时代做好准备，智能城市的核心是智能交通。这对加拿大的经济、社会和环境可持续性和福祉至关重要。

The objective of this research program is to develop integrated traffic control systems for smart cities in the 21st century that exploit emerging innovations in vehicular automation and connectivity and shared mobility services. Cities in Canada and worldwide are growing increasingly busy, a trend that perpetually rises. Populations and human activity are growing, while transportation infrastructure is limited by space, financial and environmental constraints. With trending vehicular automation and connectivity (e.g. the google car) and vehicle and ride sharing (e.g. uberPOOL) our transportation systems and our cities are at the precipice of a paradigm shift, second only to the first revolution of introducing the car itself in the early 1900s. Technology and new transportation services are rapidly transforming the fundamentals of how our cities shapeup and how we move within them. However, we are not yet fully prepared for the consequences. The boldest vision for smart transportation in smart cities of the future is automated, connected, green and shared. The net effect, however, is far from certain, and can be significantly positive or drastically negative, depending on how we explicitly design and exploit such innovations and manage their evolution and adoption in society. We have yet to build science and tools to concurrently manage both the demand (trips on the road) and supply (infrastructure capacities and efficiency) in the context of large cities. This research, therefore, focuses on the context of large and complex transportation systems in cities, and not on vehicular technology creation per se. We focus on how to exploit disruptive transportation technologies and services to maximize the societal benefits while avoiding potential risks and unintended consequences. More specifically, we will use modern machine learning coupled with fundamental traffic flow theory and economics to: (1) quantify the impacts of transformative technologies on the transportation system and its performance and (2) create traffic control, optimization and management methods, tools, strategies, and policies that guide the inevitable transformation in a positive direction, improving the economic, social, and environmental sustainability of our future cities. We will develop systems for trip pricing to avoid exacerbating sprawl and traffic congestion. We will also exploit high levels of automation and pervasive connectivity to build new traffic control systems that boost infrastructure capacity. The new systems and policies will be implementable and enforceable using technology, with minimal public infrastructure and at reasonable costs. The significance of this research is to prepare Canada for the era of smart cities at the core of which is smart transportation. This is essential for the economic, social, and environmental sustainability and wellbeing of Canada.