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.
- 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, establishing a unified pathway from behavioral evidence to operational, planning, regulatory, and passenger-service decisions.
Blogger's Review: This article provides an innovative perspective by merging traditional transportation management with artificial intelligence, emphasizing the behavioral evidence characteristic of data and proposing systematic solutions. The future of intelligent transportation systems will rely on in-depth analysis and rational utilization of these behavioral data.