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Planning and Optimizing Transit Lines

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arxiv 2405.10074 v1 pith:M2L2LBQK submitted 2024-05-16 math.OC

Planning and Optimizing Transit Lines

classification math.OC
keywords lineplanningtransitdifferentmodelsfrequencylinesperformance
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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For all line-based transit systems like bus, metro and tram, the routes of the lines and the frequencies at which they are operated are determining for the operational performance of the system. However, as transit line planning happens early in the planning process, it is not straightforward to predict the effects of line planning decisions on relevant performance indicators. This challenge has in more than 40 years of research on transit line planning let to many different models. In this chapter, we concentrate on models for transit line planning including transit line planning under uncertainty. We pay particular attention to the interplay of passenger routes, frequency and capacity, and specify three different levels of aggregation at which these can be modeled. Transit line planning has been studied in different communities under different names.The problem can be decomposed into the components line generation, line selection, and frequency setting. We include publications that regard one of these individual steps as well as publications that combine two or all of them. We do not restrict to models build with a certain solution approach in mind, but do have a focus on models expressed in the language of mathematical programming.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. AlphaTransit: Learning to Design City-scale Transit Routes

    cs.AI 2026-05 unverdicted novelty 6.0

    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.

  2. An Exact Algorithm for Public Transport Line Planning Considering Passenger and Operational Costs and Lost Demand

    math.OC 2026-04 unverdicted novelty 6.0

    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.

  3. An ALNS Heuristic for Large-Scale Line Planning with Mode Choice and Line Generation

    math.OC 2026-07 conditional novelty 5.0

    An ALNS heuristic with dynamic line generation and logit-based endogenous demand produces higher-frequency, higher-ridership bus networks for Odense, but the results depend on hand-set demand-sensitivity parameters.

  4. Line Planning at Scale: Models, Methods, and Insights

    math.OC 2026-06 unverdicted novelty 5.0

    Empirical comparison on Dutch and Swiss railway instances shows a compact direct connection model outperforms the canonical change-and-go network on over 83% of 972 cases, with the latter failing on many large instances.