Service Reliability Measurement Using Automated Fare Card Data

Uniman, David L.; Attanucci, John; Mishalani, Rabi G.; Wilson, Nigel H. M.

doi:10.3141/2143-12

Cited by 51 publications

(57 citation statements)

References 5 publications

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“…The resulting travel time distribution captures all passenger trips that started within that time period and are distributed over all trains that served the respective OD pair. For a more in-depth discussion, see Uniman (2009) and Uniman et al (2010). In the specific case of a high-frequency metro line, the temporal aggregation of passenger journeys captures passengers of multiple trains, but often only a few trains are affected by a delay.…”

Section: Passenger Impactmentioning

confidence: 99%

A framework for evaluating operations control on a metro line: integrating multiple perspectives and automatically collected train and passenger movement data

et al. 2013

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Transit operations control, the task of implementing the operations plan in daily operations on a metro line, plays a key role in service delivery because it determines the quality of the service experienced by passengers. Yet, it is one of the most poorly understood aspects of rail transit operations. Faced with a disruption or infeasibility, dispatchers typically choose between several response strategies. However, to date, it has been very difficult to evaluate the positive and negative effects of individual control strategies with respect to operations and passenger travel times under real-world conditions. This paper proposes a framework for the study of rail operations control decisions that integrates automatically collected service and passenger demand data, which are increasingly available and accessible to transit agencies. The framework supports a multiperspective analysis methodology that can inform operational policies and plans, and help operations control decision-makers choose the most appropriate strategies to manage service. By using automatically collected data, taking into consideration the operations control decision environment, and acknowledging that the reliability of the resulting service depends on many factors endogenous to it, this paper takes a distinctly different approach from previous studies, which have relied heavily on modeling, assumed simple operating contexts, and did not consider the full range of available data. Two real-world applications ofThe research presented in this paper was carried out when the corresponding author was a student at the Massachusetts Institute of Technology. the framework, where control decisions are evaluated in terms of their operational and passenger impacts, are presented. The methodology is found to be versatile and valuable in providing insights that could not have been gained otherwise. Although the framework is applied to the London Underground, its logic, structure, and procedures are applicable and transferable to other metro systems recognizing that certain specifics would need to be tailored to the available data.

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Section: Passenger Impactmentioning

confidence: 99%

A framework for evaluating operations control on a metro line: integrating multiple perspectives and automatically collected train and passenger movement data

et al. 2013

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“…The CityDashboard data have also been used to extract useful information for other purposes such as generating insights into sustainable transport systems or the health impact of bicycle sharing systems (Woodcock et al, 2014); for example, the Bike Share Map 42 shows the status of biking system docks in real-time for several cities around the world. Uniman et al (2010) used data from the Oyster Smart Card (public transport card for the London Underground) to determine the reliability of the Underground system. Using data on the entries and exits to/from London Underground stations, they developed metrics based on the travel time of passengers.…”

Section: Passive Non-framework Datamentioning

confidence: 99%

Sources of VGI for Mapping

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et al. 2017

Mapping and the Citizen Sensor

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The concept of Volunteered Geographic Information (VGI) is often exemplified by the mapping of features in OpenStreetMap (OSM), yet there are many other sources of VGI available. Some VGI is very focused on the creation of map-based products, while in other applications location is simply one attribute that is routinely collected, due to the proliferation of Global Positioning System (GPS) enabled devices, e.g. mobile phones and tablets. This chapter aims to provide an overview of the variety of sources of VGI currently available, categorised according to whether they can contribute to framework data (i.e. the type of data that are commonly part of the spatial data infrastructure of national mapping agencies and governments) or not and whether the data have been actively or passively collected. A range of examples are presented to illustrate the different types of VGI in each of these main categories. Finally, the chapter discusses some of the main issues surrounding the use of VGI and points to chapters in the book where these issues are described in more detail.

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“…In most cases, when deducting the transfer times, travel distance increased one station. Considering the passenger's psychological requirements of reducing walking distance and waiting time, passengers who travel at a loop line will choose a path which has a small number of transfer times [4] .…”

Section: B the Impact Of Different Transfer Times On Travel Timementioning

confidence: 99%