Modelling the impact of rail delays on passenger satisfaction
journal contributionposted on 19.08.2021, 10:50 authored by Fredrik Monsuur, Marcus EnochMarcus Enoch, Mohammed Quddus, Stuart Meek
Rail use and rail traffic in the UK increased substantially in the 25 years from 1994 to the end of 2019, a situation which led to progressively more delays and increasingly dissatisfied passengers. This study aims to quantify how disruptions to rail services are perceived by passengers to highlight situations that cause the highest rates of dissatisfaction so that they can be more effectively managed by the rail industry.
Passenger satisfaction data from 7000 or so responses to the UK National Rail Passenger Survey (NRPS) where passengers had experienced delays were integrated with Network Rail data of the exact operational performance (e.g. train punctuality, service frequency, delay cause, magnitude of delay) that was encountered on each surveyed trip. An ordered logit model was then applied which allows for random taste variation to understand how passenger satisfaction was affected by rail delays.
The study found that passengers reacted negatively to delays over 30 min, and dissatisfaction was exacerbated when passengers had to stand during the journey and/or received poor information, and when trains were cancelled. Policy implications for train operators include: (1) only cancel trains as a last resort; (2) prioritise trains approaching the ten minute delay threshold; (3) prioritise minimising delays to trains carrying high numbers of standing passengers; (4) enhance information quality and information delivery mechanisms as far as possible. Government should re-orientate franchise contracts to: (1) incentivise train operating companies to place more emphasis on passenger satisfaction when implementing service recovery strategies; and (2) improve delay information provision. Already the results are helping rail operators and practitioners to develop targeted recovery strategies aimed at minimising passenger dissatisfaction. This is the first academic study to investigate how rail passenger satisfaction is influenced by operational factors such as real-time delay, train frequency, train cancellations as well as delay causes.
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