Context-Aware AI for Personalized Public Transit Recommendations

Authors- Rangraju D

Abstract--With rapid urbanization and rising demands on public transportation systems, cities worldwide are facing increasing pressure to deliver efficient, responsive, and user-centric mobility solutions. Traditional transit systems, designed for mass efficiency, often fall short in adapting to the specific and dynamic needs of individual commuters. Context-aware Artificial Intelligence (AI) introduces a paradigm shift by enabling personalized transit recommendations based on real-time contextual data such as location, time, preferences, environmental conditions, and user behavior. This paper explores the foundational principles and technologies behind context-aware AI in public transit, including sensor fusion, machine learning, and real-time data analytics. It presents key use cases, such as multimodal journey planning, dynamic routing, accessibility-aware travel, and crowd-aware suggestions. Case studies from leading smart cities—such as Singapore, Helsinki, and Tokyo—demonstrate real-world implementations and benefits. Ethical and regulatory considerations related to data privacy, algorithmic fairness, and digital accessibility are critically examined. The paper also discusses operational challenges, including infrastructure gaps, data fragmentation, and the digital divide. Finally, it highlights future innovations in edge AI, federated learning, and natural language interfaces that promise to enhance the adaptability and inclusiveness of transit systems. Context-aware AI emerges as a transformative approach to shaping the future of urban mobility, aligning transportation services with the nuanced realities of individual commuter needs.

DOI: /10.61463/ijset.vol.13.issue2.378