Metro Station Operating Costs: An Econometric Analysis

Quddus, Mohammed; Harris, Nigel G; Graham, Daniel J.

doi:10.5038/2375-0901.10.2.6

Cited by 6 publications

(1 citation statement)

References 13 publications

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“…Metro efficiency has been assessed for individual (11) and multi ple metros (12), but without identifying the impact of particular technical characteristics, such as automation. Quddus et al investigated factors that affect station operating costs (13), but no similar study has assessed factors that affect the efficiency of train service operations.…”

Section: Literature Reviewmentioning

confidence: 99%

Impacts of Unattended Train Operations on Productivity and Efficiency in Metropolitan Railways

Cohen

Barron

Anderson

et al. 2015

Transportation Research Record: Journal of the Transportation R

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Urban metro subway systems (metros) around the world are choosing increasing levels of automation for new and existing lines: the global length of metro lines capable of unattended train operation (UTO) is predicted to triple in the next ten years. Despite significant investment in this technology, empirical evidence for the financial and service quality impacts of UTO in metros remains scarce.We used questionnaires and semi-structured interviews with metros of the CoMET and Nova benchmarking groups to assemble emerging evidence for how automation affects costs, staffing, service capacity and reliability.Data from 23 lines suggest that UTO can reduce staff numbers by 30-70%, with the amount of wage cost reduction depending on whether staff on UTO lines are paid more. Based on the experience of seven metros, capital costs of lines capable of unattended operation are higher, but internal rate of return has been estimated by two metros at 10-15%. Automated lines are capable of operating at the highest service frequencies of up to 42 trains per hour, and the limited available data suggest that automated lines are more reliable. Our findings indicate that UTO is a means to a more flexible and reliable operating model that can increase metro productivity and efficiency. We identify important work that is needed to better understand impacts, identifying where statistical analyses will add value once sufficiently large datasets become available.

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Section: Literature Reviewmentioning

confidence: 99%