Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Cities
2026-07-20 • Artificial Intelligence
Artificial Intelligence
AI summaryⓘ
The authors look at how data from city transportation, like bus times and taxi trips, can help improve services. Instead of taking data at face value, they treat it as clues about how people behave. They explore four ways AI can help: predicting bus arrivals, finding taxi patterns, spotting unusual events, and understanding passengers' safety concerns. The chapter also talks about important things like data quality, privacy, and fairness that must be handled to use AI well. Overall, the authors show how to turn transportation data into better decisions for managing and planning urban transport.
urban transportationartificial intelligencebus arrival predictionmobility patternsbehavioral dataabnormal behavior detectiondata privacyfairness in AIdecision support systems
Authors
Junbiao Pang, Muhammad Ayub Sabir, Fatima Ashraf
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.