AlphaTransit pairs MCTS with a learned policy-value network to reach 54.6% and 82.1% service rates on a Bloomington transit benchmark, outperforming plain RL and plain MCTS baselines.
Planning and
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
An iterative exact algorithm solves a mixed-integer line planning model faster than CPLEX by dynamically expanding paths and frequencies, and accounting for lost demand improves overall resource efficiency.
citing papers explorer
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AlphaTransit: Learning to Design City-scale Transit Routes
AlphaTransit pairs MCTS with a learned policy-value network to reach 54.6% and 82.1% service rates on a Bloomington transit benchmark, outperforming plain RL and plain MCTS baselines.