Multi-train trajectory optimisation to maximise rail network energy efficiency under travel-time constraints

Goodwin, Jonathan; Fletcher, D. I.; Harrison, Robert F.

doi:10.1177/0954409715593304

Cited by 10 publications

(18 citation statements)

References 14 publications

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“…Recently GAs have been applied to tasks involving rail network operation, for example timetabling [5,6], train control [7], and resource allocation [8]. However, typically these tasks have required the evaluation of 10 4 -10 5 candidate solutions to find a good solution.…”

Section: Genetic Algorithm For Optimizing Rail Networkmentioning

confidence: 99%

Investigating Bayesian Optimization for rail network optimization

Hickish

Fletcher

Harrison

2019

International Journal of Rail Transportation

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Optimizing the operation of rail networks using simulations is an ongoing task where heuristic methods such as Genetic Algorithms have been applied. However, these simulations are often expensive to compute and consequently, because the optimization methods require many (typically >10 4 ) repeat simulations, the computational cost of optimization is dominated by them. This paper examines Bayesian Optimization and benchmarks it against the Genetic Algorithm method. By applying both methods to test-tasks seeking to maximize passenger satisfaction by optimum resource allocation, it is experimentally determined that a Bayesian Optimization implementation finds 'good' solutions in an order of magnitude fewer simulations than a Genetic Algorithm. Similar improvement for realworld problems will allow the predictive power of detailed simulation models to be used for a wider range of network optimization tasks. To the best of the authors' knowledge, this paper documents the first application of Bayesian Optimization within the field of rail network optimization.

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Section: Genetic Algorithm For Optimizing Rail Networkmentioning

confidence: 99%

Investigating Bayesian Optimization for rail network optimization

Hickish

Fletcher

Harrison

2019

International Journal of Rail Transportation

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“…The model developed by Goodwin et al 5 uses a genetic algorithm (GA) based optimiser, although the same consideration of noise could be applied with alternative optimisation approaches -it is not specific to the GA implementation.…”

Section: Optimisation In Uncertain Systemsmentioning

confidence: 99%

“…In this paper a method is introduced that attempts directly to impose robustness in planned trajectories through probabilistic variation of CPs and DTs within a genetic algorithm (GA) based, multi-train optimisation process. This is in contrast to simple application of a GA to optimise train trajectory, which has been well covered by previous publications, for example by Chang and Sim, Yang et al and Goodwin et al 3,4,5 . The aim is to prove the concept of including uncertainties in the rail network optimisation process to support future application to an operational network.…”

Section: Introductionmentioning

confidence: 99%

A methodology for robust multi-train trajectory planning under dwell-time and control-point uncertainty

Goodwin

Fletcher

Harrison

2019

Proceedings of the Institution of Mechanical Engineers, Part F:

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Methods to optimise train movements to maintain time and avoid excessive energy consumption are becoming widely applied, but outcomes remain sensitive to uncertainties within the system. In this paper, variability in station dwell-times and the points of application of planned train control (traction, coasting or braking) are taken as examples of typical rail system uncertainties and are used to demonstrate an approach to multi-train trajectory optimisation that is resilient to them. Trade-offs are explored between highly optimised train trajectories that are vulnerable to perturbation and less optimal trajectories that are robust to typical disturbances. Beyond dwell and control variations the method has application for many other common rail network uncertainties, e.g. differences in the traction characteristics of nominally identical trains or variable train loading. Relative to optimisation without consideration of uncertainty, the approach is shown to find control strategies of substantially increased robustness for the test cases examined and offers a more principled way to plan a network than the ad hoc use of recovery time to mitigate everyday operational disturbances.

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“…However, the model was complex, which makes it difficult to use for multiple trains. Goodwin et al [29] used the GA to obtain the suboptimal condition transition points of multiple trains, but the calculation efficiency of GA was not high. Liu [30,31] discussed the two-trains and three-trains systems in turn and proposed that the optimal control strategy of the tracking train adopts four modes or five modes of movements, and the optimized solution can be obtained by a heuristic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Driving Strategies for Multi-Train by Optimization and Update Speed Profiles Considering Transmission Losses of Regenerative Energy

et al. 2019

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This paper aims at minimizing the total energy consumption of multi-train in an urban rail transit (URT) system by optimizing and updating speed profiles considering regenerative braking power losses on the catenary. To make full use of regenerative energy and decrease traction energy consumption simultaneously, energy-efficient control strategies of multi-train and a corresponding solution method are proposed. The running process of multi-train is divided into several sections based on passenger stations. Speed profiles of each train in each section are collaboratively optimized by searching only one transition point from the optimized single-train speed profile, which can be worked out by searching the switching point of coasting mode, and the optimized multi-train speed profiles are updated based on departure orders of trains. Moreover, an electrical network model is established to analyze energy flows, and dynamic losses of recovered regenerative energy on the line can be calculated. Besides, an improved optimization strategy of multi-train, which contains seven motion phases, is presented for steep slope. Simulation results based on Guangzhou Metro Line 8 verify the effectiveness of the proposed methods. Total energy consumption of optimized multi-train can be decreased by 6.95% compared with multi-train adopted single-train optimal control strategy, and the energy-saving rate of 21.08% can be achieved compared with the measured data by drivers under same trip time. In addition, the influence of departure interval on total energy consumption is analyzed and the optimal departure interval can be obtained.

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Multi-train trajectory optimisation to maximise rail network energy efficiency under travel-time constraints

Cited by 10 publications

References 14 publications

Investigating Bayesian Optimization for rail network optimization

Investigating Bayesian Optimization for rail network optimization

A methodology for robust multi-train trajectory planning under dwell-time and control-point uncertainty

Energy-Efficient Driving Strategies for Multi-Train by Optimization and Update Speed Profiles Considering Transmission Losses of Regenerative Energy

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