In this section
@article{Singh:2019:10.1177/0361198119848415,
author = {Singh, R and Graham, DJ and Anderson, RJ},
doi = {10.1177/0361198119848415},
journal = {Transportation Research Record},
pages = {516--528},
title = {Characterizing journey time performance on urban metro systems under varying operating conditions},
url = {http://dx.doi.org/10.1177/0361198119848415},
volume = {2673},
year = {2019}
}
TY - JOUR
AB - Automated fare collection (AFC) data provide opportunities for improved measurement of public transport service quality from the passenger perspective. In this paper, AFC data from the London Underground are used to measure service quality through an analysis of journey time performance under regular and incident-affected operating conditions. The analysis involves two parts: (i) parametrically defining the shape of journey time distributions, and (ii) defining three performance metrics based on the moments of the distributions to measure the mean and variance of journey times. The metrics show that mean journey times are longest during the afternoon peak across all lines analyzed, and are more variable during the afternoon and off-peak periods depending on the line. Under incident conditions, mean journey times range from 8% to 39% longer compared with regular conditions, depending on the line. Overall, the main application of this work is that the metrics presented here can be directly applied by operators to quantify customer journey time performance, and can be further extended for industry-wide application to compare performance across metro networks.There has been increasing recognition in the transport industry of the need for performance metrics that capture journey time reliability from a passenger perspective as opposed to the traditional operator-oriented indicators. In a report for the Organisation for Economic Co-operation and Development (OECD) on service quality metrics used by metro operators, it is noted that the three most commonly reported metrics relating to journey time are train delay, wait times, and passenger journeys on-time (1). The first two metrics capture train performance from a schedule and headway adherence point of view. The third attempts to capture the experience of the user; however, it is recognized that operator-oriented indicators are rarely able to measure the true impact of passenger delay (2).The journey time distribution on
AU - Singh,R
AU - Graham,DJ
AU - Anderson,RJ
DO - 10.1177/0361198119848415
EP - 528
PY - 2019///
SN - 0361-1981
SP - 516
TI - Characterizing journey time performance on urban metro systems under varying operating conditions
T2 - Transportation Research Record
UR - http://dx.doi.org/10.1177/0361198119848415
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000479070500045&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/75239
VL - 2673
ER -
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