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First published online January 1, 2010

Service Reliability Measurement Using Automated Fare Card Data: Application to the London Underground

Abstract

This paper explores the potential of using automated fare card data to quantify the reliability of service as experienced by passengers of rail transit systems. The distribution of individual passenger journey times can be accurately estimated for those systems requiring both entry and exit fare card validation. With the use of this information, a set of service reliability measures is developed that can be used to routinely monitor performance, gain insights into the causes of unreliability, and serve as an input into the evaluation of transit service. An estimation methodology is proposed that classifies performance into typical and nonrecurring conditions, which allows analysts to estimate the level of unreliability attributable to incidents. The proposed measures are used to characterize the reliability of one line in the London Underground under typical and incident-affected conditions with the use of data from the Oyster smartcard system for the morning peak period. A validation of the methodology with the use of incident-log data confirms that a large proportion of the unreliability experienced by passengers can be attributed to incident-related disruptions. In addition, the study revealed that the perceived reliability component of the typical Underground trip exceeds its platform wait time component and equals about half of its on-train travel time as well as its station access and egress time components, suggesting that sizable improvements in overall service quality can be attained through reliability improvements.

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References

1. Abkowitz M., Slavin H., Waksman R., Englisher L., and Wilson N. Transit Service Reliability. Report UMTA-MA-06-0049-78-1. U.S. Department of Transportation Systems Center, Cambridge, Mass., 1978.
2. Strathman J., Kimpel T., and Dueker K. Time Point-Level Analysis of Passenger Demand and Transit Service Reliability. Final Technical Report TNW2000–03. Portland State University, Oregon, 2000.
3. Paine F. T., Nash A. N., Hille S. T., and Brunner G. A. Consumer Attitudes Toward Auto vs. Public Transport Alternatives. Journal of Applied Psychology, Vol. 53, No. 6, 1976, pp. 472–480.
4. Bates J., Polak J., Jones P., and Cook A. The Valuation of Reliability for Personal Travel. Transportation Research, Part E, Vol. 37, No. 2, 2001, pp. 191–229.
5. Frumin M., Uniman D., Wilson N. H. M., Mishalani R. G., and Attanucci J. P. Service Quality Measurement in Urban Rail Networks with Data from Automated Fare Collection Systems. Presented at Conference on Advanced Studies for Public Transport, Hong Kong, 2009.
6. Love A., and Jackson P. The Meaning of Reliability. Qualitative Research Commentary Report S.00756. London Buses, United Kingdom, 2000.
7. Lam T., and Small K. The Value of Time and Reliability: Measurement from a Value Pricing Experiment. Transportation Research, Part E, Vol. 37, 2001, pp. 231–251.
8. Lomax T., Schrank D., Turner S., and Margiotta R. Selecting Travel Time Reliability Measures. Texas Transportation Institute and Cambridge Systematics, Inc., 2003.
9. De Jong G., Plasmeijer E. Kroes R., Sanders P., and Warffemius P. The Value of Reliability. In Proc., European Transport Conference, Strasbourg, France, Oct. 4–6, 2004.
10. Furth P., and Muller T. Service Reliability and Hidden Waiting Time. In Transportation Research Record: Journal of the Transportation Research Board, No. 1955, Transportation Research Board of the National Academies, Washington, D.C., 2006, pp. 79–87.
11. Chan J. Rail OD Estimation and Journey Time Reliability Metrics Using Automated Fare Data. MS Thesis. Massachusetts Institute of Technology, Cambridge, 2007.
12. Uniman D. Service Reliability Measurement Using Smart Card Data: Application to the London Underground. Master's thesis. Massachusetts Institute of Technology, Cambridge, 2009.
13. Draper N., and Smith H. Applied Regression Analysis, 3rd ed. John Wiley & Sons, 1998.
14. Wolberg J. Data Analysis Using the Method of Least Squares: Extracting the Most Information from Experiments. Springer, New York, 2006.
15. TfL—Group Business Planning and Performance. TfL Business Plan 2005/6 to 2009/10. Transport for London, United Kingdom, 2007.
16. TfL—Transport Planning Business Operations. London Travel Report 2007. Transport for London, United Kingdom, 2007.
17. TfL—Group Transport Planning and Policy. Transport 2025: Transport Vision for a Growing World City. Transport for London, United Kingdom, 2006.
18. London Underground (LU) Limited. Modelling and Performance Tools. 2004. LU intranet. Accessed Aug. 10, 2006.

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Article first published online: January 1, 2010
Issue published: January 2010

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© 2010 National Academy of Sciences.
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Authors

Affiliations

David L. Uniman
Room 1–274, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139.
EMBARQ Center for Sustainable Transport—Mexico, Felipe Carrillo Puerto 54, Col. Villa Coyoacan, C.P. 04000, Mexico City, Mexico.
John Attanucci
Room 1–274, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139.
Rabi G. Mishalani
Ohio State University, 2070 Neil Avenue, Room 470, Columbus, OH 43210.
Nigel H. M. Wilson
Room 1–238, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139.

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