01706977

Component

https://doi.org/10.1177/0361198119848415

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.

19-04939

English

pp 516-528

2019-7

Transportation Research Record: Journal of the Transportation Research Board

Web

Figures (4) ; References (23) ; Tables (5)

Automatic fare collection; Metrics (Quantitative assessment); Quality of service; Rapid transit; Travel time

London Underground

London (England)

Operations and Traffic Management; Planning and Forecasting; Public Transportation

TRIS, TRB, ATRI

Apr 16 2019 2:58PM