eprintid: 750
rev_number: 9
eprint_status: archive
userid: 17
dir: disk0/00/00/07/50
datestamp: 2019-01-21 22:43:54
lastmod: 2019-01-21 22:43:54
status_changed: 2019-01-21 22:43:54
type: report
metadata_visibility: show
creators_name: Barons, Martine
creators_name: Bick, Chris
creators_name: Caselli, Marco
creators_name: Choffrut, Antoine
creators_name: Doan, Vinh
creators_name: Eslami, Payman
creators_name: Foniok, Jan
creators_name: Guzman-Rincon, Laura
creators_name: Hill, Roger
creators_name: Kawabata, Emily
creators_name: Mizzi, Giovanni
creators_name: Norman, Chris
creators_name: Peyerimhoff, Norbert
creators_name: Piwarska, K.
creators_name: Please, C.P.
creators_name: Rooney, Caoimhe
creators_name: Trejo, Sofia
creators_name: Whincop, Luke
creators_name: Whittaker, Robert
creators_name: Williams, Jessica
creators_name: Xu, Yuanwei
corp_creators: Ben Roullier
title: Strategies for the use of Data and Algorithmic Approaches in Railway Traffic Management
ispublished: pub
subjects: transport
studygroups: esgi130
companyname: Resonate
full_text_status: public
abstract: A Railway Traffic Management problem can be defined as forecasting fu- ture progression of trains, identifying conflicts where two or more trains compete for available infrastructure, investigating options for resolution of conflicts, re-planning train schedules to minimise the impact on sy- stem performance. Performance management of complex networks is a problem common to a number of industries and applications. There has been much work over many decades on modelling the generation and optimisation of railway timetables. Much of this focuses on relatively simple railways and services and is therefore quite straightforward. Main line railways have a number of features that introduce significant com- plexity. Traditionally the problem of re-planning a timetable in near real time to manage and recover from service perturbations and disruption is simplified to help arrive at a solution in an acceptable amount of time, but this then can have unintended consequences which can amplify rat- her than reduce the disruption in the network. Resonate are interested in looking at different strategies / models / techniques for dealing with the problem, the likely strengths and risks of these, and how they might be adapted to improve existing solutions. The study group participants undertook a brief survey of recent literature on modelling train delays and found machine learning approaches, network models and a statisti- cal approach to defining the efficiency of a station in dissipating delays which are worthy of further consideration. We then explored total of nine modelling approaches during the study group. The approaches fell broadly into two groups: those that sought to understand the pro- pagation of delays (Approaches 1 to 6) and those that sought to offer strategies for minimising delays (Approaches 8 and 9). Approach 7 pro- poses a way of understanding the propagation of delays and using that to evaluate candidate policy decisions. There are a number of promising approaches here which provide useful lines of enquiry, many suitable for expansion beyond the simple railways modelled, to include variable train speeds, junctions and intersections, temporal differences in usage, such as tidal flows in and out of cities, and resource constraints.
date: 2017
date_type: completed
citation:   Barons, Martine and Bick, Chris and Caselli, Marco and Choffrut, Antoine and Doan, Vinh and Eslami, Payman and Foniok, Jan and Guzman-Rincon, Laura and Hill, Roger and Kawabata, Emily and Mizzi, Giovanni and Norman, Chris and Peyerimhoff, Norbert and Piwarska, K. and Please, C.P. and Rooney, Caoimhe and Trejo, Sofia and Whincop, Luke and Whittaker, Robert and Williams, Jessica and Xu, Yuanwei  (2017) Strategies for the use of Data and Algorithmic Approaches in Railway Traffic Management.  [Study Group Report]     
document_url: http://miis.maths.ox.ac.uk/miis/750/1/Resonate%20ESGI130%20report.pdf