AI research explores transportation behavior in smart cities

This chapter explores the application of artificial intelligence (AI) to understand and manage transportation behavior within sustainable smart cities. It proposes a behavior-centered AI approach, treating mobility data and passenger feedback as evidence rather than absolute truth. The research outlines four key areas: predicting bus arrivals, discovering taxi mobility patterns, detecting abnormal behavior, and mining passenger-perceived risks. These are integrated into a closed-loop framework that emphasizes data quality, privacy, fairness, interpretability, and human accountability for successful deployment. AI

IMPACT This research outlines a framework for using AI to improve urban transportation efficiency and passenger experience.

RANK_REASON Academic paper published on arXiv. [lever_c_demoted from research: ic=1 ai=1.0]

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