A Framework For Measuring Passenger Experienced Transit Reliability Using Automated Data
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A Framework for Measuring Passenger-experienced Transit Reliability Using Automated Data
A public transport operator's ability to understand and improve service reliability experienced by passengers depends upon their ability to measure it. Traditionally, largely due to data collection limitations, passengers' experience of reliability has not been measured directly. As a result, operators often fail to effectively measure, and thus manage, key issue affecting passengers' perceived reliability. However, with the relatively recent availability of automatic data collection (ADC) systems, it has become technically feasible to measure passengers' reliability experience at a detailed level. If used in practice, passenger-experienced reliability measurement has the potential to improve public transport systems' responsiveness to passengers needs across many functional areas. This thesis develops a framework for the understanding and practical use of passenger-experienced reliability measurement on high-frequency transit systems. A model of passenger-experienced reliability based on total travel time variability is developed, and the key differences from "operational" reliability identified. This model is applied to identify public transport management functions which should be targeted as a result of passenger-experienced reliability measurement. The model and potential applications are then synthesized to develop a set of design criteria for passenger-experienced reliability metrics. Two new measures of passenger-experienced reliability are developed, both aiming to quantify the "buffer time" passengers must add to compensate for travel time variability. The first measure, derived from passengers' actual travel times from automatic fare collection (AFC) data, is essentially the median travel time variability experienced by frequent travelers over each origin-destination (OD) pair of interest. The second measure, derived from vehicle location data, OD matrices, and train load estimates, is based on a simulation of passengers' waiting, boarding, transfer, and in-vehicle travel process. This second metric is aimed at "non-gated" systems without exit AFC data, for which passengers' travel times cannot be measured directly. These two metrics are tested and evaluated using data from the Hong Kong MTR system. These metrics' response to incidents, scheduled headways, and passenger demand are tested at the OD pair and line levels. The results are used to evaluate these metrics according to the previously-developed design criteria for passenger-experienced reliability metrics. The first metric is found to be suitable for implementation (where adequate data is available), while the second is found to inadequately measure demand-related delays. An implementation guide for the AFC-based metric is developed. This guide is structured around four main implementation decisions: (1) coordination with an operator's existing metrics, (2) defining the service scope, (3) determining an appropriate frequency of calculation, and (4) defining appropriate time of day intervals and date periods. This guide is then demonstrated using a case study application from MTR: an investigation of the 2014 Hong Kong political demonstrations' impact on MTR reliability.
Mobility Patterns, Big Data and Transport Analytics
Mobility Patterns, Big Data and Transport Analytics provides a guide to the new analytical framework and its relation to big data, focusing on capturing, predicting, visualizing and controlling mobility patterns - a key aspect of transportation modeling. The book features prominent international experts who provide overviews on new analytical frameworks, applications and concepts in mobility analysis and transportation systems. Users will find a detailed, mobility 'structural' analysis and a look at the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications and transportation systems analysis that are related to complex processes and phenomena. This book bridges the gap between big data, data science, and transportation systems analysis with a study of big data's impact on mobility and an introduction to the tools necessary to apply new techniques. The book covers in detail, mobility 'structural' analysis (and its dynamics), the extensive behavioral characteristics of transport, observability requirements and limitations for realistic transportation applications, and transportation systems analysis related to complex processes and phenomena. The book bridges the gap between big data, data science, and Transportation Systems Analysis with a study of big data's impact on mobility, and an introduction to the tools necessary to apply new techniques. - Guides readers through the paradigm-shifting opportunities and challenges of handling Big Data in transportation modeling and analytics - Covers current analytical innovations focused on capturing, predicting, visualizing, and controlling mobility patterns, while discussing future trends - Delivers an introduction to transportation-related information advances, providing a benchmark reference by world-leading experts in the field - Captures and manages mobility patterns, covering multiple purposes and alternative transport modes, in a multi-disciplinary approach - Companion website features videos showing the analyses performed, as well as test codes and data-sets, allowing readers to recreate the presented analyses and apply the highlighted techniques to their own data
Modelling Public Transport Passenger Flows in the Era of Intelligent Transport Systems
This book shows how transit assignment models can be used to describe and predict the patterns of network patronage in public transport systems. It provides a fundamental technical tool that can be employed in the process of designing, implementing and evaluating measures and/or policies to improve the current state of transport systems within given financial, technical and social constraints. The book offers a unique methodological contribution to the field of transit assignment because, moving beyond “traditional” models, it describes more evolved variants that can reproduce:• intermodal networks with high- and low-frequency services;• realistic behavioural hypotheses underpinning route choice;• time dependency in frequency-based models; and• assumptions about the knowledge that users have of network conditionsthat are consistent with the present and future level of information that intelligent transport systems (ITS) can provide. The book also considers the practical perspective of practitioners and public transport operators who need to model and manage transit systems; for example, the role of ITS is explained with regard to their potential in data collection for modelling purposes and validation techniques, as well as with regard to the additional data on network patronage and passengers’ preferences that influences the network-management and control strategies implemented. In addition, it explains how the different aspects of network operations can be incorporated in traditional models and identifies the advantages and disadvantages of doing so. Lastly, the book provides practical information on state-of-the-art implementations of the different models and the commercial packages that are currently available for transit modelling. Showcasing original work done under the aegis of the COST Action TU1004 (TransITS), the book provides a broad readership, ranging from Master and PhD students to researchers and from policy makers to practitioners, with a comprehensive tool for understanding transit assignment models.