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New appraisal values of travel time saving and reliability in Great Britain.
This paper provides an overview of the study ‘Provision of market research for value of time savings and reliability’ undertaken by the Arup/ITS Leeds/Accent consortium for the UK Department for Transport (DfT). The paper summarises recommendations for revised national average values of in-vehicle travel time savings, reliability and time-related quality (e.g. crowding and congestion), which were developed using willingness-to-pay (WTP) methods, for a range of modes, and covering both business and non-work travel purposes. The paper examines variation in these values by characteristics of the traveller and trip, and offers insights into the uncertainties around the values, especially through the calculation of confidence intervals. With regards to non-work, our recommendations entail an increase of around 50% in values for commute, but a reduction of around 25% for other non-work—relative to previous DfT ‘WebTAG’ guidance. With regards to business, our recommendations are based on WTP, and thus represent a methodological shift away from the cost saving approach (CSA) traditionally used in WebTAG. These WTP-based business values show marked variation by distance; for trips of less than 20 miles, values are around 75% lower than previous WebTAG values; for trips of around 100 miles, WTP-based values are comparable to previous WebTAG; and for longer trips still, WTP-based values exceed those previously in WebTAG.
Batley, R.P., Bates, J., Bliemer, M., Börjesson, M., Bourdon, J., Ojeda Cabral, M., Chintakayala, P.K., Choudhury, C., Daly, A.J., Dekker, T., Drivyla, E., Fowkes, A., Hess, S., Heywood, C., Johnson, D., Laird, J., Mackie, P., Parkin, J., Sanders, S., Sheldon, R., Wardman, M. & Worsley, T. (2019), New appraisal values of travel time saving and reliability in Great Britain. Transportation, 46, Issue 3, pp 583–621.
Mode choice with latent availability and consideration: theory and a case study.
Over the last two decades, passively collected data sources, like Global Positioning System (GPS) traces from data loggers and smartphones, have emerged as a very promising source for understanding travel behaviour. Most choice model applications in this context have made use of data collected specifically for choice modelling, which often has high costs associated with it. On the other hand, many other data sources exist in which respondents’ movements are tracked. These data sources have thus far been underexploited for choice modelling. Indeed, although some information on the chosen mode and basic socio-demographic data is collected in such surveys, they (as well as in fact also some purpose collected surveys) lack information on mode availability and consideration. This paper addresses the data challenges by estimating a mode choice model with probabilistic availability and consideration, using a secondary dataset consisting of ‘annotated’ GPS traces. Stated mode availability by part of the sample enabled the specification of an availability component, while the panel nature of the data and explicit incorporation of spatial and environmental factors enabled estimation of latent trip specific consideration sets. The research thus addresses an important behavioural issue (explicit modelling of availability and choice set) in addition to enriching the data for choice modelling purposes. The model produces reasonable results, including meaningful value of travel time (VTT) measures. Our findings further suggest that a better understanding of mode choices can be obtained by looking jointly at availability, consideration and choice.
Calastri, C., Hess, S., Choudhury, C.F., Daly, A.J. & Gabrielli, L. (2019), Mode choice with latent availability and consideration: theory and a case study. Transportation Research Part B, 123, pp. 374-385.
VTT or VTTS: a note on terminology for value of travel time work.
The value of travel time (VTT) can be said to be the most important number in transport economics, and its estimation has been the topic of extensive academic and applied work. Numerous papers use the term “value of travel time savings”, or VTTS. The addition of the word “savings” has not arisen suddenly but goes back to the 1970s, and has also been used in the titles of national studies. The addition
of ‘savings’ is in our view incorrect, misleading and unhelpful. Unlike money, time cannot be stored or borrowed – there is no piggy bank for spare minutes. In addition, the modelling approaches used for many of the more advanced VTT studies in fact produce valuations that are ‘bracketed' between gains
and losses in time, and an average between these gains and losses, typically the geometric mean, is then used as the VTT. It is then clear that the value obtained from this averaging cannot be described as the value of time savings (or reductions), as it includes the higher value of losses (i.e. increases) as well. To exemplify the magnitude of our theoretical points, we show how for the 2015 UK VTT study, using
the bracketed value for commuters and labelling it as a VTTS implies an overestimation by a factor of more than 2.
Daly, A. & Hess, S. (2019), VTT or VTTS: a note on terminology for value of travel time work, Transportation, forthcoming.
Developing Advanced Route Choice Models for Heavy Goods Vehicles Using GPS Data.
This paper presents a novel application in route choice modelling using Global Positioning System (GPS) data, focussing on heavy goods vehicles which typically make longer journeys with decisions potentially underpinned by different priorities from those used by car drivers. The scope of the study is larger than many previous ones, using the entire road network of England. Making use of the error components model put forward for route choice by Frejinger and Bierlaire (2007), the work reveals low elasticities in response to changes in travel time, reflecting the limited opportunity for avoiding specific roads on long distance journeys by heavy goods vehicles.
Hess, S., Quddus, M.A., Rieser-Schüssler, N. & Daly, A.J. (2014), Developing Advanced Route Choice Models for Heavy Goods Vehicles Using GPS Data. Transportation Research Part E, 77, pp 29-44.
Developing advanced route choice models for heavy goods vehicles using GPS data.
This paper presents a novel application in route choice modelling using Global Positioning System (GPS) data, focussing on heavy goods vehicles which typically make longer journeys with decisions potentially underpinned by different priorities from those used by car drivers. The scope of the study is larger than many previous ones, using the entire road network of England. Making use of the error components model put forward for route choice by Frejinger and Bierlaire (2007), the work reveals low elasticities in response to changes in travel time, reflecting the limited opportunity for avoiding specific roads on long distance journeys by heavy goods vehicles.
Hess, S., Quddus, M., Rieser, N. & Daly, A.J. (2015), Developing advanced route choice models for heavy goods vehicles using GPS data. Transportation Research Part E, 77, pp 29-44.
The Attribution of Transport User Benefits by Source using Discrete Choice Models.
A major transport project would typically affect the cost of travel of several different alternatives, and give rise to a combination of gains and losses to users of each alternative. The attribution of benefits to each of the travel alternatives needs to recognise that travellers may change their behaviour as a result of the project. These changes in demand arise not only from changes in the cost of each specific alternative but also from cost changes in other competing alternatives. The appropriate treatment of inter-modal effects is central to the determination of the user benefit produced by each alternative. The paper sets out a number of desirable criteria that source-related measures of user benefit should satisfy which include local consistency with the rule of a half. It explores the effect of alternative path specifications on the resulting measures and demonstrate that they can give different results when larger cost changes can occur, such as in modelling a new alternative. Appropriate measures that are able to treat this problem are developed and the results compared to those obtained by numerical methods.
Hyman, G. & Daly, A.J. (2014), The Attribution of Transport User Benefits by Source using Discrete Choice Models. Research in Transport Economics, 47, pp 103-111.
Analytic approximations for computing probit choice probabilities.
The multinomial probit model has long been used in transport applications as the basis for mode- and route-choice in computing network flows, and in other choice contexts when estimating preference parameters. It is well known that computation of the probit choice probabilities presents a significant computational burden, since they are based on multivariate normal integrals. Various methods exist for computing these choice probabilities, though standard Monte Carlo is most commonly used. In this article we compare two analytical approximation methods (Mendell–Elston and Solow–Joe) with three Monte Carlo approaches for computing probit choice probabilities. We systematically investigate a wide range of parameter settings and report on the accuracy and computational efficiency of each method. The findings suggest that the accuracy and efficiency of an optimally ordered Mendell–Elston analytic approximation method offers great potential for wider use.
Connors, R. D., Hess, S., & Daly, A. (2014). Analytic approximations for computing probit choice probabilities. Transportmetrica A: Transport Science, 10(2), 119-139.
Contrasting imputation with a latent variable approach to dealing with missing income in choice models.
Income is a key variable in many choice models. It is also one of the most salient examples of a variable affected by data problems. Issues with income arise as measurement errors in categorically captured income, correlation between stated income and unobserved variables, systematic over- or under-statement of income and missing income values for those who refuse to answer or do not know their (household) income. A common approach for dealing especially with missing income is to use imputation based on the relationship among those who report income between their stated income for reporters and their socio-demographic characteristics. A number of authors have also recently put forward a latent variable treatment of the issue, which has theoretical advantages over imputation, not least by drawing not just on data on stated income for reporters, but also choice behaviour of all respondents. We contrast this approach empirically with imputation as well as simpler approaches in two case studies, one with stated preference data and one with revealed preference data. Our findings suggest that, at least with the data at hand, the latent variable approach produces similar results to imputation, possibly an indication of non-reporters of income having similar income distributions from those who report it. But in other data sets the efficiency advantage over imputation could help in revealing issues in the complete and accurate reporting of income.
Hess, S., Sanko, N., Dumont, J. & Daly, A.J. (2014), Contrasting imputation with a latent variable approach to dealing with missing income in choice models. Journal of Choice Modelling, 12, pp 47-57.
Temporal transfer of models of mode and destination choice for the Greater Toronto and Hamilton area.
Transport planning relies extensively on forecasts of traveler behavior over horizons of 20 years and more. Implicit in such forecasts is the assumption that travelers’ tastes, as represented by the behavioral model parameters, are constant over time. In technical terms, this assumption is referred to as the "temporal transferability" of the models. This paper summarizes the findings from a literature review that demonstrates there is little evidence about the transferability of mode-destination models over typical forecasting horizons. The literature review shows a relative lack of empirical studies given the importance of the issue. To provide further insights and evidence, models of commuter mode-destination choice been developed from household interview data collected across the Greater Toronto and Hamilton Area in 1986, 1996, 2001, and 2006. The analysis demonstrates that improving model specification improves the transferability of the models, and in general the transferability declines as the transfer period increases. The transferability of the level-of-service parameters is higher than transferability of the cost parameters, which has important implications when considering the accuracy of forecasts for different types of policy. The transferred models over-predict the key change in mode share over the transfer period—specifically, the shift from local transit to auto driver between 1986 and 1996—but under-predict the growth in commuting tour lengths over the same period.
Fox, J., Daly, A., Hess, S. & Miller, E. (2014), Temporal transfer of models of mode and destination choice for the Greater Toronto and Hamilton area. Journal of Transport and Land Use, 7 (2), pp 41-62.
The value of small time savings for non-business travel.
The paper addresses the issue of how small time savings are handled in the appraisal of transport proposals. A review is made of the policy of eleven governments and this is followed by a critical discussion of the treatment of small time changes that have been estimated in Stated Choice (SC) studies, from the 1960s until the present day. By examining the reasons for the use of SC experiments in the past and the method's shortcomings in understanding the values of small time savings, this paper concludes there is a strong case for reconsidering the use of Revealed Preference (RP) data.
Daly, A., Tsang, F. & Rohr, C. (2014), The value of small time savings for non-business travel. Transport Economics and Policy, 48 (2), pp 205-218.
Practical solutions for sampling alternatives in large scale models.
Many large-scale real-world transport applications have choice sets that are so large as to make model estimation and application computationally impractical. The ability to estimate models on subsets of the alternatives is thus of great appeal, and correction approaches have existed since the late 1970s for the simple multinomial logit (MNL) model. However, many of these models in practice rely on nested logit specifications, for example, in the context of the joint choice of mode and destination. Recent research has put forward solutions for such generalized extreme value (GEV) structures, but these structures remain difficult to apply in practice. This paper puts forward a simplification of the GEV method for use in computationally efficient implementations of nested logit. The good performance of this approach is illustrated with simulated data, and additional insights into sampling error are also provided with different sampling strategies for MNL.
Daly, A., Hess, S. & Dekker, T. (2014), Practical solutions for sampling alternatives in large scale models. Transportation Research Record, 2429 (1), pp 148-156.
