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This paper presents TrafficSci, an agentic AI system that automates the discovery of universal traffic laws across cities through iterative workflows, successfully rediscovering established laws and identifying a new temporal memory scale in urban driving behavior.
This paper examines how the underrepresentation of elderly riders in mobility datasets introduces systematic bias into mobility modeling, using Citi Bike data from Jersey City. It shows that models trained on majority-dominated populations misrepresent elderly mobility behavior, and that higher-capability models do not necessarily improve subgroup fidelity under limited demographic data.