Transit network design considering link capacities

Durán-Micco, Javier; Vansteenwegen, Pieter

doi:10.1016/j.tranpol.2022.08.018

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…The TND optimization problem has often been tackled using various methods such as Genetic Algorithms (GA), Tabu Search and Simulated Annealing. Duran-Micco et al [9] provided a comprehensive review of the TND literature, focusing on studies published within recent years. Cipriani et al [10] described a procedure to solve the BTND based on two stages; the first consists of a heuristic algorithm that provides a set of feasible routes, while the second uses a GA to find the optimal network regarding frequencies and routes.…”

Section: Literature Reviewmentioning

confidence: 99%

A Bus Network Design Model under Demand Variation: A Case Study of the Management of Rome’s Bus Network

Gemma,

Cipriani,

Crisalli

et al. 2024

Sustainability

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This paper proposed a methodology to design bus transit networks that can be consistently adjusted according to demand variations both in level and distribution. The methodology aims to support the activities of service providers in optimizing the service capacity of the bus network according to a system-wide analysis. It stems from the changes imposed by the COVID-19 pandemic. Such an experience has imposed a rethinking of the methodology used for the optimal design of robust transit network services that are easy-to-adapt to demand variations without redesigning the whole network every time. Starting from an existing model, this design methodology is articulated in two parts: the first part for solving the problem with the maximum level of transit demand, aiming at giving an upper bound to the solution, and the second part, where the network is optimized for other specific transit demands. This method has been applied to a real context in the city of Rome, considering two levels of demand taken from COVID-19 experiences. They are characterized by the application of different policies regarding different timings for shopping and schools’ openings as well as by policies on smart working. The results show the effectiveness of the proposed methodology to design robust transit networks suited to comply with large demand variations. Moreover, the procedure is suitable and easy to implement, in order to adapt quickly to changes in demand without having to modify line routes, but adapting them in an optimal way, even when dealing with realistic-sized transit networks.

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Section: Literature Reviewmentioning

confidence: 99%