Artificial Intelligence for Smart Transportation

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.Comment: This is a pre-print for a book chapter to appear in Vorobeychik, Yevgeniy., and Mukhopadhyay, Ayan., (Eds.). (2023). Artificial Intelligence and Society. ACM Pres

Addressing smart city challenges utilizing machine learning: vehicular crash and public transportation fuel consumption prediction

According to the United Nations Department of Economic and Social Affairs, 64% of the developing world and 86% of the developed world will be urbanized by 2050. This presents both new challenges and wonderful opportunities. Thanks to the fast, steady growth of technologies such as the Internet of Things (IoT), and Internet of People, the process of collecting the data required to solve the challenges that urbanization brings forth has been alleviated; thus, improving the quality of life for the citizens of urban environments. This thesis focuses on solutions to two of the challenges facing urbanized areas: vehicular crashes and public transportation fuel consumption by utilizing innovative machine learning models. These solutions can assure the safety of citizens, assist with urban planning, emission reduction, smart city development, etc