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Travel time reliability: a review of late time valuations, elasticities and demand impacts in the passenger rail market in Great Britain

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Abstract

This paper provides an extensive review and reconciliation of British and European evidence relating to the value of, and demand responses to, rail reliability. In particular, we compare the elasticities implied by stated preference valuations of late time with directly estimated lateness elasticities. We find that the implied lateness elasticities are substantially greater than those directly estimated. A possible explanation for this is that lateness has been over-valued, but more sobering explanations would be to suggest that, whilst rail travellers dislike unreliability, they may be unwilling or unable to reduce their rail travel in response to experiences of poor performance, or else conventional economic approaches to deducing elasticities are not appropriate. The findings have been used to update the recommendations of the UK rail industry’s Passenger Demand Forecasting Handbook.

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Notes

  1. In this context, we are primarily concerned with random (e.g. as might be caused by an unforeseen incident) as opposed to systematic (e.g. as might be reflected by a longer travel time during peak hours relative to off-peak) variability.

  2. The so-called lateness multiplier expresses late arrival time in equivalent units of scheduled travel time.

  3. Transport researchers have also shown interest in non-expected utility paradigms such as Prospect Theory (Kahnemann and Tversky 1979), but expected utility (it would seem) continues to prevail in both the economics and transportation literatures.

  4. See Batley and Ibáñez (2013) for a more detailed comparison of the theoretical properties of the three approaches.

  5. Of course, discrete choice models based on appropriate choice contexts yield demand elasticities as well as valuations for reliability. Nonetheless, in this context they have been almost exclusively used for valuation.

  6. Given that timetable patterns often vary across the day, GJT is calculated in 15 minute time intervals and is weighted according to a profile of desire departure times. Note that in practice the λ and ϖ weights are not (as represented here for simplicity) constant.

  7. Suppose late time were entered into GJT to create an enhanced GJT (EGJT). We would then have:

    \(EGJT = GJT + w_{AML} AML\)

    and for forecasting purposes we would have:

    \(V = EGJT^{e}\)

    The implied elasticities to GJT and lateness would be:

    \(\eta {}_{GJT} = e\frac{GJT}{EGJT};\quad \eta_{AML} = e\frac{{w_{AML} AML}}{EGJT}\)

    Rearranging terms:

    \(e = \frac{EGJT}{GJT}\eta_{GJT} = \frac{EGJT}{{w_{AML} AML}}\eta_{AML}\)

    It follows that:

    \(\eta_{AML} = \eta_{GJT} \frac{{w_{AML} AML}}{GJT}\)

  8. Usually, cheap talk works through in terms of cost sensitivity, which would leave time based multipliers unaffected. In this study, the cheap talk effect was interacted with the late time (and rolling stock) coefficient.

  9. There are slightly fewer observations here for late time than in Table 1 since this review of European evidence focussed on studies with monetary valuations and hence studies providing time multipliers but not money values were not covered. Details of the studies covered here are available from the authors on request.

  10. The attributes to which the other multipliers related were walking and waiting time, time spent in congested traffic and searching for a parking space, service headway and departure time shifts.

  11. \(\begin{gathered} \eta_{AML} = \frac{\partial V}{\partial AML}\frac{AML}{V} = \frac{\partial V}{\partial PPM}\frac{\partial PPM}{\partial AML}\frac{AML}{V} = \frac{\partial V}{\partial PPM}\frac{\partial PPM}{\partial AML}\frac{AML}{V}\frac{PPM}{PPM} \hfill \\ = \frac{\partial V}{\partial PPM}\frac{PPM}{V}\frac{\partial PPM}{\partial AML}\frac{AML}{PPM} \hfill \\ \end{gathered}\)

  12. A service group is made up of several station-to-station flows. A TOC will typically consist of several service groups.

  13. Indeed, this is an issue that is widely recognised to affect GJT elasticity estimates. Even though GJT is not a small elasticity, it is not uncommon to find that it is insignificant because of little variation in service quality across the flows and time periods of interest.

  14. This review was undertaken as part of the 2013 PDFH update (PDFH5.1).

  15. After the review was conducted, we became aware of a confidential study of short and long distance non-season flows into London where the AML elasticity was −0.18 with a 95 % confidence interval of only ± 0.006 and also a shorter distance largely commuter based flow into London where the highly significant AML elasticity was around −0.25 for both seasons and non-season tickets. Nonetheless, we have not uncovered additional evidence relating to season tickets.

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Acknowledgments

The authors are grateful for the funding received from the Association of Train Operating Companies and the Office of Rail Regulation to support this research and would like to express their thanks to John Segal and Fitsum Teklu of the MVA Consultancy for their support and advice. The views expressed here are those of the authors and do not necessarily reflect those of the sponsors. Thanks are due to the comments of four anonymous referees.

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  1. Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT, UK

    Mark Wardman & Richard Batley

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Correspondence to Mark Wardman.

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Wardman, M., Batley, R. Travel time reliability: a review of late time valuations, elasticities and demand impacts in the passenger rail market in Great Britain. Transportation 41, 1041–1069 (2014). https://doi.org/10.1007/s11116-014-9526-5

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