Artificial Intelligence for Understanding and Managing Transportation Behavior in Sustainable Smart Cities
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 repres...
| Authors | Junbiao Pang, Muhammad Ayub Sabir, Fatima Ashraf |
|---|---|
| Primary category | cs.AI |
| Published | 20 July 2026 |
| Last updated at source | 20 July 2026 |
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