Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Cities

Abstract:Urban transportation systems generate heterogeneous data, yet these data do not automatically become actionable management intelligence. This chapter adopts a behavior-centered perspective on artificial intelligence (AI), treating mobility records and passenger-generated text as behavioral evidence rather than behavioral truth. It examines four directions: bus arrival prediction for service reliability, taxi mobility pattern discovery for demand analysis and planning, abnormal behavior detection for accountable regulatory support, and passenger-perceived risk mining for service improvement. These directions are integrated through a closed-loop framework linking data input, behavior representation, AI inference, decision support, public value, and governance feedback. The chapter identifies data quality, privacy, fairness, interpretability, uncertainty, transferability, and human accountability as essential conditions for deployment. It thereby establishes a unified pathway from behavioral evidence to operational, planning, regulatory, and passenger-service decisions.

Artificial Intelligence (cs.AI)

arXiv:2607.17694 [cs.AI]

(or arXiv:2607.17694v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2607.17694 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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