MIT1_258JS10_lec26

MIT1_258JS10_lec26 - Service Reliability Measurement using...

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Service Reliability Measurement using Oyster Data - A Framework for the London Underground David L. Uniman MIT – TfL January 2009 1
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Introduction Research Objective To develop a framework for quantifying reliability from the perspective of passengers using Oyster data that is useful for improving service quality on the Underground. • How reliable is the Underground? - how do we think about reliability? - how do we quantify it? - how do we understand its causes? - how do we improve it? 2
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Background – What is Service Reliability? • Reliability means the degree of predictability of the service attributes including comfort, safety, and especially travel times. ± Passengers are concerned with average travel times, but also with certainty of on-time arrival % of Trips T’ arrival T departure T’ departure Time of Day T desired arrival Probability of Late Arrival Avg. Travel Time “Buffer” Avg. Travel Time - Adapted from Abkowitz (1978) 3
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Framework - Reliability Buffer Time Metric • Criteria for Reliability Measure • Representative of passenger experience • Straightforward to estimate and interpret • Usefulness and applicability – compatible with JTM • Propose the following measure: Reliability Buffer Time (RBT) Metric “The amount of time above the typical duration of a journey required to arrive on- time at one’s destination with 95% certainty” RBT = (95 th percentile – 50 th percentile) O-D, AM Peak, LUL Period sample size 20 No. of Actual Distribution Observations 50 th perc. 95 th perc. Travel Time RBT 4
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Framework – Separating Causes of Unreliability •T w o types of factors that influence reliability and affect the applicability & usefulness of the measure: 1. Chan (2007) found evidence for the effects of service characteristics on travel time variability – impact on aggregation (e.g. Line Level measure) 2. In this study, observed that reliability was sensitive to the performance of a few (3-4) days each Period, which showed large and non-recurring delays (believe Incident-related ) Waterloo to Picc. Circus (Bakerloo NB) - February, AM Peak Comparison of Travel Time Distributions (normalized) 35 0.3 30 0.25 25 95th pe rcen til e [ mi n] % of j our ney s February 14th 0.2 20 15 10 February 5th 0.15 5 0.05 0 0 4-Feb 7-Feb 10-Feb 13-Feb 16-Feb 19-Feb 22-Feb 25-Feb 28-Feb 2-Mar 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Weekdays Travel Time [min] 0.1 5
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Framework – Classification of Performance • Propose to classify performance into two categories along two
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This note was uploaded on 12/06/2011 for the course ESD 11.380j taught by Professor Fredsalvucci during the Fall '02 term at MIT.

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MIT1_258JS10_lec26 - Service Reliability Measurement using...

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