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.