Decomposing Journey Time Variance on Urban Rail Transit Systems
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Imperial College London Department of Civil and Environmental Engineering Decomposing journey time variance on urban rail transit systems Ramandeep Singh Submitted in part fulfilment of the requirements for the degree of Doctor of Philosophy in Civil and Environmental Engineering and the Diploma of Imperial College, July 2019 Declaration of originality I confirm that this thesis is my own original work. Any material from the published or unpub- lished work of others is appropriately referenced. Copyright declaration The copyright of this thesis rests with the author. Unless otherwise indicated, its contents are licensed under a Creative Commons Attribution-Non Commercial-No Derivatives 4.0 In- ternational Licence (CC BY-NC-ND). Under this licence, you may copy and redistribute the material in any medium or format on the condition that; you credit the author, do not use it for commercial purposes and do not distribute modified versions of the work. When reusing or sharing this work, ensure you make the licence terms clear to others by naming the licence and linking to the licence text. Please seek permission from the copyright holder for uses of this work that are not included in this licence or permitted under UK Copyright Law. i Abstract In this thesis, automated fare collection (AFC) data are used to analyse and quantify transit journey time service quality on the London Underground metro system. The thesis comprises of three main research areas. The first part focuses on characterising passenger journey time variance through the generation of empirical probability distributions of journey times under regular and incident-affected operating conditions. The distributions are parametrically defined, and practical passenger-oriented performance metrics are proposed based on the moments of the distributions. The second area of research involves decomposing total passenger journey times recorded by the AFC data into sub-components that distinguish between the walking and in-vehicle phases of a passenger journey. To achieve this, a Bayesian assignment algorithm is proposed to allocate individual passengers to individual trains. Total journey times are then decomposed into the constituent components of access, on-train, and egress times. In the third area of research, the degree to which different service supply and demand factors influence journey times is analysed. Semiparametric regression methods are applied to quantify the effect of physical station and route characteristics, operational service supply factors, and passenger demand levels for each journey time component. To quantify the effect of individual passenger characteristics on journey times, passenger-level heterogeneity within each journey time component is analysed. As an extension to the access time model, the influence of train headways on passenger wait times at the origin station is also derived. The main outputs of the thesis are the quantification of journey time performance, and the identification of the key service supply and demand factors that impact journey times. The results can be directly applied by operators to guide where potential interventions should be made in order to improve the reliability of journey times for urban rail transit networks. ii Acknowledgements The research presented in this thesis has been carried out under the supervision of Professor Daniel J. Graham, Research Director of the Transport Strategy Centre (TSC), and Mr Richard Anderson, Managing Director of the TSC at Imperial College London. I am deeply grateful to both Professor Graham and Richard for their continued guidance, advice, and support throughout the duration of this research. I further express special thanks to Dr. George Goldberg, who performed vital data cleaning of the raw Oyster card files at the beginning of the project, and Dr. Daniel H¨orcher, for valuable feedback on the research and on the write up of the thesis. Transport for London (TfL) and the TSC at Imperial College jointly funded the research, and these sources of financial support are gratefully acknowledged. Particular thanks goes to Mr Andrew Hyman of TfL. I am thankful for the support from Alexandra Williams and Sarah Willis from the Department of Civil and Environmental Engineering. Special thanks to Fionnuala Ni Dhonnabhain for being a constant source of support and for providing a listening ear at any time of the day over the last few years. Many thanks to my colleagues in the TSC, particularly my fellow PhD students and the post- docs: Anupriya, Ana, Chuyu, Csaba, Farah, Jose, Keita, Laila, Liang, Nan, Praj, Saeed, and Shane. I must thank the fantastic friends that I was fortunate to make during my time here, including the researchers within the Centre for Transport Studies and Sophie, Nils, Jean Paul, Filip, Aurelien, Laura, Luis, Alex, Stas, Acile, and Magnus. Final thanks go to my family: my grandmother, parents, Harpreet, and Darash. iii Contents 1 Introduction 1 1.1 Background ...................................... 1 1.2 Motivationandobjectives . ..... 4 1.3 Contributions ................................... .. 5 1.4 Thesisoutline ................................... .. 8 2 Literature review 11 2.1 Definitionofjourneytimevariance . ....... 12 2.2 Definitionofjourneytimecomponents . ....... 15 2.3 Journeytimeperformancemetrics. ...... 16 2.3.1 Operations-based performance metrics . ....... 17 2.3.2 Passenger-oriented performance metrics . ......... 24 2.3.3 London Underground performance metrics . ..... 32 2.4 Journeytimedistributions . ..... 36 2.4.1 Underlying principles of journey time distributional form ......... 37 2.4.2 Unimodaldistributionstudies . .... 38 iv CONTENTS v 2.4.3 The effect of temporal and spatial aggregation . ........ 42 2.4.4 Fitting and evaluation methods for unimodal distributions ........ 43 2.4.5 Multimodaldistributionstudies . ...... 44 2.5 Factorsthataffectjourneytimes. ....... 46 2.5.1 Theoretical regression model framework . ....... 47 2.5.2 Empiricalstudiesontotaljourneytimes . ....... 49 2.5.3 Trainrunningtimes. 53 2.5.4 Traindwelltimes ............................... 56 2.5.5 Platformwaittimes. 60 2.5.6 Summary of factors that influence urban rail journey times........ 64 2.5.7 Evaluation of regression methods used in empirical work ......... 66 2.5.8 Anoteonmachinelearningmethods . ... 67 2.6 Summary of literature review and research contributions ............. 69 3 Data 72 3.1 DefinitionofLondonUndergroundstudyarea . ...... 73 3.2 Passengertripdata ............................... ... 73 3.2.1 Datasummary ................................ 73 3.2.2 Descriptivestatistics . .... 76 3.2.3 Limitations .................................. 78 3.3 Trainmovementdata............................... .. 80 3.3.1 Datasummary ................................ 80 vi CONTENTS 3.3.2 Descriptivestatistics . .... 82 3.3.3 Limitations .................................. 87 3.4 Incidentdata .................................... 88 3.4.1 Datasummary ................................ 88 3.4.2 Descriptivestatistics . .... 89 3.4.3 Limitations .................................. 91 3.5 Additionalnetworkdata . ... 93 3.5.1 Rolling OriginDestinationsurveydata . ....... 93 3.5.2 Physical network and station characteristics . .......... 94 4 Characterising journey time distributions 96 4.1 Introduction.................................... .. 96 4.2 Studyareaanddata ................................ 98 4.3 Methods........................................100 4.3.1 Operatingscenarios. 101 4.3.2 Unimodaldistributionfitting . 104 4.3.3 Evaluation criteria for unimodal distributions . ...........106 4.3.4 Multimodaldistributionfitting . 109 4.3.5 Journeytimeperformancemetrics. 112 4.4 Results......................................... 120 4.4.1 Distributionalform . 120 4.4.2 Journeytimeperformancemetrics. 127 CONTENTS vii 4.5 Conclusions ..................................... 137 5 Passenger to train assignment 139 5.1 Introduction.................................... 139 5.2 Literaturereview ................................ 141 5.2.1 Priortoautomateddataavailability. .......142 5.2.2 Automateddatamethods . .142 5.2.3 Limitations of reviewed methods and thesis contributions. 146 5.3 Studyareaanddata ................................ 148 5.4 Methods........................................150 5.4.1 Proposedassignmentalgorithm . 150 5.4.2 Comparison of relative performance of proposed assignment algorithm . 159 5.5 Results......................................... 160 5.6 Conclusions ..................................... 164 6 Decomposingjourneytimesviasemiparametricregression 165 6.1 Introduction.................................... 165 6.2 Studyareaanddata ................................ 168 6.3 Methods........................................169 6.3.1 Generalregressionmodelframework . 170 6.3.2 Dependentvariables . 172 6.3.3 Modelcovariates ............................... 173 viii CONTENTS 6.3.4 Supplysidecovariates . 174 6.3.5 Demandcovariates .............................. 177 6.3.6 Modelevaluation ............................... 179 6.4 Results......................................... 183 6.4.1 Datapreparation ............................... 183 6.4.2 Modelform ..................................184 6.4.3 Covariatesignificance. 188 6.4.4 Modelresiduals ................................ 190 6.4.5 Resultsformodelcovariates . 191 6.4.6 Resultsforservicesupplycovariates. ........195 6.4.7 Resultsfordemandcovariates . 202 6.5 Applications..................................... 206 6.5.1 Headwaysandwaitingtimes . 206 6.5.2 Casestudy: