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A Big Data Mashing Tool for Measuring Transit System Performance

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Seeing Cities Through Big Data

Part of the book series: Springer Geography ((SPRINGERGEOGR))

Abstract

This research aims to develop software tools to support the fusion and analysis of large, passively collected data sources for the purpose of measuring and monitoring transit system performance. This study uses San Francisco as a case study, taking advantage of the automated vehicle location (AVL) and automated passenger count (APC) data available on the city transit system. Because the AVL-APC data are only available on a sample of buses, a method is developed to expand the data to be representative of the transit system as a whole. In the expansion process, the General Transit Feed Specification (GTFS) data are used as a measure of the full set of scheduled transit service.

The data mashing tool reports and tracks transit system performance in these key dimensions:

  • Service Provided: vehicle trips, service miles;

  • Ridership: boardings, passenger miles; passenger hours, wheelchairs served, bicycles served;

  • Level-of-service: speed, dwell time, headway, fare, waiting time;

  • Reliability: on-time performance, average delay; and

  • Crowding: volume-capacity ratio, vehicles over 85 % of capacity, passenger hours over 85 % of capacity.

An important characteristic of this study is that it provides a tool for analyzing the trends over significant time periods—from 2009 through the present. The tool allows data for any two time periods to be queried and compared at the analyst’s request, and puts the focus specifically on the changes that occur in the system, and not just observing current conditions.

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Acknowledgement

The authors would like to thank the San Francisco County Transportation Authority (SFCTA) for funding this research, the San Francisco Municipal Transportation Agency (SFMTA) for providing data, and both for providing valuable input and advice.

Author information

Authors and Affiliations

  1. Department of Civil Engineering, University of Kentucky, 261 Oliver H. Raymond Bldg., 508 Administration Drive, Lexington, 40506, KY, USA

    Gregory D. Erhardt

  2. Arup, 1 Nicholson St, East Melbourne, VIC, 3002, Australia

    Oliver Lock

  3. Centre for Advanced Spatial Analysis (CASA), University College London, 90 Tottenham Court Road, London, W1T 4TJ, UK

    Elsa Arcaute & Michael Batty

Authors
  1. Gregory D. Erhardt
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  2. Oliver Lock
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  3. Elsa Arcaute
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  4. Michael Batty
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Corresponding author

Correspondence to Gregory D. Erhardt .

Editor information

Editors and Affiliations

  1. Urban Studies and Urban Big Data Centre, University of Glasgow, Glasgow, United Kingdom

    Piyushimita (Vonu) Thakuriah

  2. Department of Urban Planning and Policy, University of Illinois at Chicago, Chicago, Illinois, USA

    Nebiyou Tilahun

  3. Department of Urban Planning and Policy, University of Illinois at Chicago, Chicago, Illinois, USA

    Moira Zellner

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Erhardt, G.D., Lock, O., Arcaute, E., Batty, M. (2017). A Big Data Mashing Tool for Measuring Transit System Performance. In: Thakuriah, P., Tilahun, N., Zellner, M. (eds) Seeing Cities Through Big Data. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-319-40902-3_15

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