Improving pre-trip information about transfer-involved rail routes: algorithms and analytical methods

Abstract

Small delays and major disruptions are frequently encountered in rail passenger transport, which brings challenges not only to railway timetabling and operations but also to timetablebased passenger information. This thesis is aimed at identifying the unresolved problem(s) in the existing pre-trip timetable information systems and at developing a set of novel algorithms and analytical models to enhance the pre-trip timetable information about and the understanding of those transfer-involved routes within a national-level railway network. Specifically, it tries to answer the following four inter-related questions: i) which transferinvolved routes are the weaknesses in terms of pre-trip timetable information, among the numerous origin-destination pairs; ii) how to develop an effective and easy-to-implement approach to coping with these weaknesses; iii) how to quantify and know in advance the potential effect of a specific information improvement strategy; and iv) what are the potential factors that render some of the transfer-involved routes particularly vulnerable to delays and disruptions.

Since the research touches on multiple disciplines, the relevant concepts in railway timetabling and operations, journey planning algorithms, statistical analysis, and decision theory are firstly introduced. Built on these fundamentals and an introduction to the concepts of critical transfers and critical routes, a screening algorithm is developed that is able to efficiently identify those transfer-involved rail routes that may be particularly vulnerable to delays and disruptions and may need information enhancements. After that, by reviewing the pros and cons of existing methods, a novel historical-data-driven algorithm is developed to deal with those weaknesses in terms of pre-trip timetable information. In order to obtain a more precise estimation of the potential effect of a particular information enhancement strategy, an analytical framework is developed that is able to evaluate a specific strategy ex ante. The underlying assumptions are presented and the potential limitations are discussed. All of the algorithms and models presented in this thesis have been extensively tested by exploiting the open data from British railways, the results of which are promising in terms of efficiency and effectiveness. Some interesting findings are presented about British railways, followed by a discussion of potential directions in future research.

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Identifiers

ORCID for Jonathan Preston: orcid.org/0000-0002-6866-049X

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