Time Valuations of Rolling Stock Improvements

Time Valuations of Rolling Stock Improvements


Fitsum Teklu, SYSTRA, Mark Wardman, SYSTRA, Rafael Maldonado, SYSTRA


This paper presents the results of a Stated Preference study into rail passengers’ valuations of a range of rolling stock improvements.


Rolling stock investments such as improved cleanliness, air-conditioning, on-board information systems and internet connection are one of the instruments that the railways use to deliver better customer experience to their passengers. Decisions on which intervention to go for require a good understanding of passengers’ valuations of such improvements. This paper presents the results of a stated preference (SP) study undertaken for the Rail Passenger Demand Forecasting Council in the UK that derived time valuations for a range of such improvements.
The SP study was preceded by focus groups that were designed to understand which rolling stock improvements passengers valued highly and test some aspects of the SP design, including which numeraire (whether time or money) to use and the number of rolling stock improvements that respondents can easily trade against each other. Guided by the focus groups, respondents were offered SP exercises with two options, and asked to trade-off three rolling stock attributes against time. The three rolling stock attributes each had three discrete levels of worst, medium and best. The journey times were presented at four different levels to obtain more precise time valuations.
The SP design also included trade-offs in which one of the options presented all rolling stock attributes at their best levels and the other offered all of them at their worst levels. This was included to understand whether a package of improvements is valued higher/lower than the sum of the individual improvements.
The SP surveys were undertaken on-train on a range of rail services in the UK. About 1720 passengers completed the paper-based self-completion questionnaires which included the SP exercises.
A discrete choice model was used to analyse the data. The analysis tested a range of modelling approaches with different specifications of the utility function, to take account of differences between passenger segments, passengers on different types of trains, whether or not the passenger is standing and/or carrying a mobile device. The preferred model uses a multiplicative specification of the utility function that uses multiplicative modifiers to identify significant variations between segments and to take account of SP design variables (e.g. the package effect).
In addition to the model results, the paper discusses a range of theoretical and practical issues in relation to stated preference techniques used to value ‘soft factors’ such as rolling stock improvements.


Association for European Transport