mlsanityData to 7 Oct 2026

A Neural-Evolutionary Algorithm for Autonomous Transit Network Design

arXiv 2403.07917 ↗First posted 27 Feb 2024cs.NE cs.LGcited by 1

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Abstract (arXiv)

Planning a public transit network is a challenging optimization problem, but essential in order to realize the benefits of autonomous buses. We propose a novel algorithm for planning networks of routes for autonomous buses. We first train a graph neural net model as a policy for constructing route networks, and then use the policy as one of several mutation operators in a evolutionary algorithm. We evaluate this algorithm on a standard set of benchmarks for transit network design, and find that it outperforms the learned policy alone by up to 20% and a plain evolutionary algorithm approach by up to 53% on realistic benchmark instances.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 25 of its 71 references that are in the map. First posted on arXiv 27 Feb 2024.

Builds on

25 of 71 references.

References · 25

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13 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026AlphaTransit: Learning to Design City-scale Transit Routescited by 0