Artificial Intelligence for Smart Transportation

TL;DR

This paper explores how Artificial Intelligence can enhance the efficiency and utilization of public transit systems by analyzing data, decision-making processes, and computational challenges in smart transportation.

Contribution

It provides a comprehensive overview of AI applications in transit, focusing on data sources, decision-making, and computational problems specific to transportation systems.

Findings

AI can improve transit efficiency and utilization

Data-driven decision-making enhances transportation planning

AI approaches address complex computational problems in transit systems

Abstract

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.