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Optimising the Loading Diversity of Rail Passenger Crowding using On-Board Occupancy Data

Abstract

dc:description.abstract

Crowded conditions on trains can lead to lower passenger satisfaction, discourage rail travel, result in negative economic impacts and are a factor in a number of health and safety hazards. In the UK there is an annual survey of rail passenger crowding, although the measures used do not reflect coach-by-coach variations, nor do they reflect variations across the peak period. In this MPhil thesis I investigated the application of weight-based automatic passenger counting data to deliver more even loadings on trains through the provision of new real-time and static solutions. In addition I investigated the potential benefits of such solutions in terms of reduced dwell times and reduced crowding. The overall concept proposed was to make the most of the existing available capacity; for example, so that no-one is standing when seats are available. Through analysing a large sample of air suspension data, I identified station-specific trends where some coaches were over capacity while others had spare capacity. I also conducted a critical review of academic research into on-train crowding and solutions that seek to optimise ‘loading diversity’. This study contributes to this emerging subject area in several ways: I propose two new metrics to describe inter-coach loading diversity that, unlike existing metrics, contain information relative to the capacity; I have revealed a link between the inter-coach loading diversity metrics and estimated boarding times, with trains classified as ‘very uneven’ on departure typically having dwell times of approximately five to ten seconds greater than services that were classified as being ‘even’ with a similar total number of passengers on board; and finally I have applied classification supervised learning techniques to predict the load factor for a given service and these predictors were an improvement over taking the historical average.

Degree

thesis:*
Level dc:type.qualificationlevel
mphil
Grantor dc:publisher.institution
The Open University
Year dc:date.issued
2016

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Ball, Simon David

Rights

Language dc:language
en

Chain of custody

source
Harvested from
The Open University
Base URL
oro.open.ac.uk/cgi/oai2
Last updated
2026-07-24
Source record
OAI-PMH GetRecord
related terms
citation

Ball, Simon David. Optimising the Loading Diversity of Rail Passenger Crowding using On-Board Occupancy Data. mphil thesis, The Open University, 2016.