eprintid: 1553314
rev_number: 31
eprint_status: archive
userid: 608
dir: disk0/01/55/33/14
datestamp: 2017-09-29 11:42:56
lastmod: 2019-09-16 02:53:20
status_changed: 2017-09-29 11:42:56
type: thesis
metadata_visibility: show
creators_name: Pavlides, A
title: Cost functions for railway operations and their application to timetable optimisation
ispublished: unpub
divisions: A01
divisions: B04
divisions: C05
divisions: F44
keywords: Railway timetabling, Multi-objective optimisation, Genetic algorithm
abstract: This thesis investigates cost functions for evaluating and optimising the performance of a timetable with mixed train services. Specifically, the performance considered herein includes crowdedness, journey time, punctuality and waiting time. To examine the implications of optimising using these cost functions, a multi-objective optimisation algorithm is developed to derive an optimised timetable for mixed train services. The optimisation algorithm consists of three stages: a Genetic Algorithm (GA) is used to determine the optimal sequence of train runs, followed by Dijkstras shortest path algorithm for determining the optimal schedule based on the sequence determined by GA, and finally an iterative Hill-Climbing procedure for determining the optimal number of train runs in the system. Experiments were carried out on the Brighton Main Line and examined the effect of different timetabling parameters. The first series of experiments showed that the cost of the timetable can be driven down simply through resequencing the trains such that trains exiting the network quickly are more evenly distributed through the time period examined. This occurs since trains exiting early create a buffer which can absorb delays, preventing their propagation. The experiments have also shown that different demand levels influence the number of trains to be scheduled. The optimal number of trains to schedule though relies on the equilibrium between the crowdedness and punctuality cost function. Scheduling additional trains leads to a non-linear reduction in the marginal gains in terms of the crowdedness function while, on the other hand, the cost of punctuality increase exponentially. Finally, we derive the Pareto Frontiers for different combinations of cost functions. This research contributes to the state-of-art of railway system analysis and optimisation.
date: 2017-05-28
date_type: published
oa_status: green
full_text_type: other
thesis_class: doctoral_open
language: eng
thesis_view: UCL_Thesis
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 1291244
language_elements: English
lyricists_name: Chow, Andy
lyricists_name: Pavlides, Aris
lyricists_id: HFACH67
lyricists_id: APAVL70
actors_name: Pavlides, Aris
actors_id: APAVL70
actors_role: owner
full_text_status: public
pagerange: 1-1
pages: 209
event_title: UCL
institution: UCL (University College London)
department: Civil, Environmental & Geomatic Engineering
thesis_type: Doctoral
editors_name: Chow, AHF
citation:        Pavlides, A;      (2017)    Cost functions for railway operations and their application to timetable optimisation.                   Doctoral thesis , UCL (University College London).     Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1553314/1/Cost%20functions%20for%20railway%20operations%20and%20their%20application%20to%20timetable%20optimisation.pdf