Artigo Acesso aberto Revisado por pares

Optimal Driving Strategies for a Fleet of Trains on Level Track with Prescribed Intermediate Signal Times and Safe Separation

2022; Institute for Operations Research and the Management Sciences; Volume: 57; Issue: 2 Linguagem: Inglês

10.1287/trsc.2022.1170

ISSN

1526-5447

Autores

Phil Howlett, Peter Pudney, Amie Albrecht,

Tópico(s)

Transportation Planning and Optimization

Resumo

We propose an analytic solution to the problem of finding optimal driving strategies that minimize total tractive energy consumption for a fleet of trains traveling on the same track in the same direction subject to clearance-time equality constraints that ensure safe separation and compress the headway timespan. We assume the track is divided into sections by a set of trackside signals at fixed positions. For each intermediate signal there is an associated signal segment consisting of the two adjacent sections. Successive trains are safely separated only if the leading train leaves the signal segment before the following train enters. Although the fleet can be safely separated by a complete set of clearance times and associated clearance-time inequality constraints the problem of finding optimal schedules with safe separation rapidly becomes intractable as the number of trains and signals increases. The main difficulty is in distinguishing between active equality constraints and inactive inequality constraints. The curse of dimensionality means it is not feasible to check every different combination of active constraints, find the optimal strategies for each train, optimize the corresponding prescribed times and calculate the cost. Nevertheless we can formulate and solve an alternative problem with active clearance-time equality constraints for successive trains defined at selected signals. We show that this problem can be formulated as an unconstrained convex optimization and propose a solution algorithm that finds the optimal schedule and the associated optimal strategies for each train. Finally we find optimal schedules for a case study using realistic parameters on a busy metropolitan line.

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