Characterizing journey time performance on urban metro systems under varying operating conditions

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 a route provides a better representation of the passenger experience. A journey time distribution can be constructed by collating multiple passenger journey times between a given origin and destination; the form and moments of the distribution provide an indication of the travel conditions. The form of the distribution can be influenced by a number of factors, including passenger demand levels (3), service attributes such as frequency, infrastructure provisions, the occurrence of service delay incidents, and the level of temporal and spatial aggregation over which journey times are analyzed (4–6). In recent academic literature, metrics based on the distribution of journey times have been nominated as improved alternatives to the operator-oriented metrics (7–9). Much of the research on journey time distributional form has focused on road travel, with limited work on rail transit. This paper contributes to the understanding of urban rail passenger journey times by analyzing journey time distributions on the London Underground metro system, using automated fare collection (AFC) data administered through the Oyster smart card system. The objective of the paper is to characterize and quantify journey times and journey time variance at a line level, under regular and incident-affected operational conditions. A two-part analysis is presented: first, empirical journey time distributions are generated and parametrically defined to determine whether different forms are observed over different lines, over different times of the day, and under regular and incident-affected conditions; and second, new metrics based on the moments of the distributions are proposed to provide improved measurement of journey time performance from the passenger perspective. The paper begins with a review of the existing literature on journey time distributions and performance indicators. Details of the Oyster data and incident data are then summarized. Next, the methods used in the distribution fitting process, and definitions of the proposed performance measures are given. Results for the regular and incident-affected journey time distributions are then presented, and conclusions are summarized in the final section