AI for Smart Transportation
Michael Wilbur
Amutheezan Sivagnanam
Afiya Ayman
Samitha Samaranayake
Abhishek Dubey
Aron Laszka
Abstract
Public transportation systems worldwide face persistent challenges of efficiency, reliability, and sustainability. In this chapter, we explore how AI-powered approaches can reimagine transportation planning and operations, with particular emphasis on optimization and learning-based methods. We discuss predictive demand modeling, vehicle routing, and multimodal integration, illustrated through case studies on on-demand paratransit services, energy-efficient vehicle assignment, and microtransit operations. These examples demonstrate how AI can lower operational costs, improve system performance, and reduce emissions while managing the trade-offs between efficiency and service coverage. By highlighting the role of AI in advancing both operational effectiveness and energy sustainability, the chapter positions transportation as a critical testbed for understanding how AI can deliver practical improvements in large, complex infrastructure systems.
Type
Venue
Artificial Intelligence and Society, 11–45. River Publishers, 2026
Authors
Ph.D. 2025
Amutheezan joined the Applied Artificial Intelligence Lab as a Ph.D. student in fall 2019. His research interest is
in Machine Learning and Data Science. Previously, he graduated with a Bachelors’s Degree in Computer Science and
Engineering from the University of Moratuwa, Sri Lanka in 2017. He worked as an intern at WSO2 Lanka (PVT) Ltd., as
part of the Data Analytics Team in 2016. He also worked as a software engineer in MillenniumIT Software PVT Ltd. from
2018 to 2019. He has previously worked on a machine learning project on semi-supervised based sentiment analysis of
microblogging platforms and on a data analytics project on monitoring flows of HL7 messages. Currently, his research
work focuses on optimizing bus-route scheduling and minimizing energy costs.
Ph.D. 2025
Afiya Ayman joined the Applied Artificial Intelligence Lab as a Ph.D. student in January 2019. Her research interest
is in contemporary computational sciences, including and not limited to Data Science, Machine Learning, and Cyber
Security. She earned her bachelor’s degree in Computer Science & Engineering from Chittagong University of
Engineering & Technology, Bangladesh. Currently, her research focuses on the application of neural networks and
machine learning to restructure the operational planning of a regional transportation authority to be more
energy-efficient.
Associate Professor
Aron Laszka is an Associate Professor in the College of Information Sciences and Technology at the Pennsylvania State University. Previously, he was an Assistant Professor at the University of Houston, a Research Assistant Professor at Vanderbilt University, and a Postdoctoral Scholar at the University of California, Berkeley.
His research interests revolve around artificial intelligence and cyber-physical systems, focusing on machine learning for decision making and optimization, with applications in smart and connected communities, critical infrastructure, and cybersecurity.
His research is funded by the National Science Foundation, the Department of Energy, the Department of Transportation, and other agencies.