Simulation and Optimization of Cologne's Tram Schedule

Ullrich, Oliver; Franz, Sebastian; Speckenmeyer, Ewald; Lückerath, Daniel

doi:10.11128/sne.22.tn.10123

Cited by 9 publications

(20 citation statements)

References 12 publications

(9 reference statements)

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“…For an in-depth description of the optimization method, see [21]. A more detailed discussion of the simulation software can be found in [7].…”

Section: Simulation and Optimization Of Tram Schedulesmentioning

confidence: 99%

“…[1,3,4,5,9,10,12]). Most of them aim at one general objective like minimizing 1 This section is an abbreviated version of [21], section 1.…”

Section: Optimization Of Tram Schedulesmentioning

confidence: 99%

“…The presented results of Montpellier's Tramway and of our hometown Cologne's KVB network (see [21]) will be utilized as a base for this. …”

Section: Schedule Generationmentioning

confidence: 99%

See 2 more Smart Citations

A Robust Schedule for Montpellier’s Tramway Network

Ullrich¹,

Lückerath²,

Speckenmeyer³

2015

SNE

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The city of Montpellier in the Languedoc-Roussillon region of France features a fast growing tram network as a central part of its public service infrastructure. Here, as in many other tram networks, resources like tracks and stations are shared between dierent lines. Because of the resulting dependencies, small inevitable delays can spread through the network and aect its global performance.This article examines whether a robust tram schedule may help to raise punctuality in Montpellier's tram network. To accomplish this, we apply a tool set designed to generate schedules optimized for robustness, which also satisfy given sets of planning requirements. These tools allow to compare time tables with respect to their punctuality and other key indicators.After an introduction to the goals of this paper, we continue with a description of the tool set focusing on optimization and simulation modules. These software utilities are then employed to generate and simulate robust and non-robust schedules for Montpellier's tram network, which are subsequently compared for the resulting delays.

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“…For an in-depth description of the optimization method, see [21]. A more detailed discussion of the simulation software can be found in [7].…”

Section: Simulation and Optimization Of Tram Schedulesmentioning

confidence: 99%

“…[1,3,4,5,9,10,12]). Most of them aim at one general objective like minimizing 1 This section is an abbreviated version of [21], section 1.…”

Section: Optimization Of Tram Schedulesmentioning

confidence: 99%

See 1 more Smart Citation

A Robust Schedule for Montpellier’s Tramway Network

Ullrich¹,

Lückerath²,

Speckenmeyer³

2015

SNE

Self Cite

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“…The resulting software tools were applied to the KVB network of Cologne, Germany (see [23]), and the TAM Tramway network of Montpellier, France (see [25] The simulation engine was run on up to eight processors of the described type, under the parameter set described in section 5.1. Average runtime, speedup and marginal utility are shown in table 3 and figure 9.…”

Section: 2mentioning

confidence: 99%

Model-based Parallelization of Discrete Traffic Simulation Models

Ullrich¹,

Lückerath²,

Speckenmeyer³

2014

SNE

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To re-establish regular operations in a tram traffic network after a large disturbance, e.g. resulting from vehicle breakdown or station closure, the viability of several rescheduling and rerouting strategies has to be evaluated prior to their implementation. Here, a multi-modal traffic simulation system can help to enhance the decision quality. Such a system obviously faces tight time constraints, so simulation data has to be acquired fast.In this paper we propose a method for the parallel execution of discrete traffic simulation models, which would accelerate data generation in comparison to a sequential model. To assess this method's dynamic behavior in real-world applications, some experiments conducted on a software system modeling schedule based tram traffic are presented.After giving an introduction to the scope and aim, we show some background on the parallelization of discrete simulation models. The main part of the paper begins with the proposal of a method to parallelize the execution of simulation models with problem specific properties. Some estimations of the method's efficiency are shared, followed by several experiments to highlight its dynamic behavior in real-world applications. The paper ends with a short summary and some thoughts on further research.

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“…robustness against disturbances (see Cacchiani et al 2012), synchronicity of departures to accommodate transfer connections (see Ceder et al 2001;Eanki 2004;Ibarra-Rojas and Rios-Solis 2012), or the minimization of passenger waiting time (see Saharidis et al 2014;Wu et al 2015). Another common objective is regularity as a measure of the resilience of a timetable against small inevitable disturbances (see Genç 2003; Bampas et al 2006;Ullrich et al 2012).…”

Section: Introductionmentioning

confidence: 98%

Do regular timetables help to reduce delays in tram networks? It depends!

2015

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In public transit planning, regularity of timetables is seen as an important means to improve capacity efficiency by assuring an even trip distribution, as well as to improve product attractiveness and appreciation. This paper focuses on examining whether a regular timetable can also help to reduce network delay, especially resulting from inevitable small disturbances. Following the formulation of a mathematical optimization approach, we propose a number of conditions a network has to fulfill for timetable regularity to have a delay reducing impact. A set of three network properties is identified, which consists of (a) the sharing of resources between tram lines, (b) a low variability of driving times, and (c) the non-redundancy of the network's central resources. To test the impact of these properties, a series of optimization and simulation experiments is conducted on models of the tram network of the cities of Cologne, Germany, and Montpellier, France. Small disturbances are introduced to the simulated operations to check whether the presence of all three properties is necessary for a network to benefit from a regular timetable. The results show that while with all properties present a regular timetable can indeed help to reduce delays resulting from small disturbances, the non-compliance with any one of the conditions nullifies the impact of regularity on the result.

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Simulation and Optimization of Cologne's Tram Schedule

Cited by 9 publications

References 12 publications

A Robust Schedule for Montpellier’s Tramway Network

A Robust Schedule for Montpellier’s Tramway Network

Model-based Parallelization of Discrete Traffic Simulation Models

Do regular timetables help to reduce delays in tram networks? It depends!

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