Researchers demonstrate AI tools to optimize transit service and model rider behavior
UF presenters showed an AI system that balances equity (access for riders without cars) and on-time performance, and demonstrated how anonymized mobile-app 'tap' data from JTA can be used to visualize individual rider trips and wait times to inform service planning.
The full story
During the JAX Twin presentation, UF researchers demonstrated an AI-driven transit application intended to reconcile competing goals — serving riders who lack private vehicles and maintaining on-time performance — and to generate operational suggestions for JTA. Changjie Chen described the system as an agent-based/generative-AI approach that evaluates multiple objectives and recommends operational adjustments.
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