There are more than 7,000 public transit agencies in the U.S. (and many more
private agencies), and together, they are responsible for serving 60 billion
passenger miles each year. A well-functioning transit system fosters the growth
and expansion of businesses, distributes social and economic benefits, and
links the capabilities of community members, thereby enhancing what they can
accomplish as a society. Since affordable public transit services are the
backbones of many communities, this work investigates ways in which Artificial
Intelligence (AI) can improve efficiency and increase utilization from the
perspective of transit agencies. This book chapter discusses the primary
requirements, objectives, and challenges related to the design of AI-driven
smart transportation systems. We focus on three major topics. First, we discuss
data sources and data. Second, we provide an overview of how AI can aid
decision-making with a focus on transportation. Lastly, we discuss
computational problems in the transportation domain and AI approaches to these
problems.

AI-driven smart transportation systems can improve efficiency and increase utilization by addressing primary requirements, data sources, decision-making, and computational problems in the transportation domain.

智能交通的人工智能

Artificial Intelligence for Smart Transportation

The target of reducing travel time only is insufficient to support the
development of future smart transportation systems. To align with the United
Nations Sustainable Development Goals (UN-SDG), a further reduction of fuel and
emissions, improvements of traffic safety, and the ease of infrastructure
deployment and maintenance should also be considered. Different from existing
work focusing on the optimization of the control in either traffic light signal
(to improve the intersection throughput), or vehicle speed (to stabilize the
traffic), this paper presents a multi-agent Deep Reinforcement Learning (DRL)
system called CoTV, which Cooperatively controls both Traffic light signals and
Connected Autonomous Vehicles (CAV). Therefore, our CoTV can well balance the
achievement of the reduction of travel time, fuel, and emissions. In the
meantime, CoTV can also be easy to deploy by cooperating with only one CAV that
is the nearest to the traffic light controller on each incoming road. This
enables more efficient coordination between traffic light controllers and CAV,
thus leading to the convergence of training CoTV under the large-scale
multi-agent scenario that is traditionally difficult to converge. We give the
detailed system design of CoTV and demonstrate its effectiveness in a
simulation study using SUMO under various grid maps and realistic urban
scenarios with mixed-autonomy traffic.

为了实现联合国可持续发展目标，此论文提出一种名为 CoTV 的多智能体深度强化学习系统，可协同控制交通信号和连接自动驾驶车辆，以实现减少旅行时间、燃料和排放的目标，且易于部署和维护，并在多种实际城市场景中进行了有效性模拟研究。