AI RESEARCH
AlphaTransit: Learning to Design City-scale Transit Routes
arXiv CS.AI
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ArXi:2605.28730v1 Announce Type: new Designing a transit network requires many sequential route extension decisions, but their quality is often visible only after the full network is assembled. This delayed-feedback challenge lies at the heart of the Transit Route Network Design Problem (TRNDP), where route interactions can be deceptive: an extension that appears useful locally can create transfer bottlenecks, produce redundant overlap, or reduce overall throughput. To guide route construction under delayed simulator feedback, we.