There is a significant body of research related to the valuation of reliability in transportation. Such work has tended to rely on the stated preference methodology, where respondents are asked to make trade-offs between the mean and standard deviation of travel time. The literature has suggested that a revealed preference (RP) methodology may provide an alternative means of estimating a value of reliability. In this article we show how emerging data sources reveal travelers'preferences and, in combination with traditional choice modeling methods, can be used to estimate a value of reliability. We illustrate this RP methodology using smart-card data from the multimodal public transport network of London, UK. We are able to estimate a useable "reliability ratio" for three of four public transport modes modeled, and account for an unexpected result based upon the data available.

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The common narrative in the media is that the British public house is in terminal decline and that these losses are having a significant impact on local communities. While it is true that some pubs are closing, it is also true that others are opening. This article examines the quarterly trend of pub numbers over eight years in various neighbourhoods and utilises a multilevel model to estimate and illustrate these trends. City and town centres with a thriving night‐time economy show the most significant increases in pub numbers, whereas suburban areas show a more mixed picture, with pubs in less affluent areas performing better. The areas where reductions are most pronounced are rural locations, especially remote rural communities. The article concludes with an analysis of the reasons behind these trends and suggests avenues for future research.