Time-of-day (TOD) choice can be considered as a fifth stage in the modelling of transport behaviour, additional to the conventional four stages. Twenty years ago in The Netherlands, a stated preference (SP) study was designed for investigating the choice of time-of-day (departure time) and transport mode. A nested logit time period and mode choice model, largely based on this SP data set, was included as one of the components of The Netherlands national transport model (LMS). A new TOD SP survey has now been developed to obtain up-to-date information for the next re-estimation round of the LMS. The fieldwork was carried out in in 2019, followed by the re-estimation of the nested logit model of period and mode choice on the new SP data. The context for the SP is that of a tour (round trip) carried out by the respondent as car driver or by train, also distinguishing by travel purpose (commuting, business, education and other). This means that we are asking questions both about the outward leg of the tour and the inward leg. Both car drivers and train users are asked to participate in two SP experiments on TOD and mode choice: the first focussing on the trade-off between congestion or crowding and the departure/arrival times; the second also with differentiation in costs between peak and off-peak. Our tentative conclusion is that TOD choice seems to have become (relatively to mode choice) more flexible in the past two decades, in line with the trends towards more flexibility in scheduling activities over the day and a 24 hours economy. Moreover, we now estimate nest coefficients for both car drivers and train users (until now the assumption that had to be made in the LMS was that the nest coefficients for train followed those for car).@en

This study proposes a novel Artificial Neural Network (ANN) based method to derive the Value-of-Travel-Time (VTT) distribution. The strength of this method is that it is possible to uncover the VTT distribution (and its moments) without making assumptions about the shape of the distribution or the error terms, while being able to incorporate covariates and taking the panel nature of stated choice data into account. To assess how well the proposed ANN-based method works in terms of being able to recover the VTT distribution, we first conduct a series of Monte Carlo experiments. After having demonstrated that the method works on Monte Carlo data, we apply the method to data from the 2009 Norwegian VTT study. Finally, we extensively cross-validate our method by comparing it with a series of state-of-the-art discrete choice models and nonparametric methods. Based on the promising results we have obtained, we believe that there is a place for ANN-based methods in future VTT studies.

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The value of travel time is an important input to cost-benefit analysis of transport projects. Time gains are often one of the main benefits of transport projects, but they need to be converted from hours and minutes to money units, using a value of travel time. Many countries around the world have official values of travel time (by travel purpose, often also by mode) for this conversion. Only a few countries also include travel time reliability benefits in project appraisal, partly because of lack of values of travel time reliability, but mainly because of a lack of forecasting models that include travel time reliability. This chapter describes methods used for deriving the value of travel time and reliability and briefly discusses how to forecast time and reliability changes. Finally, some selected outcomes and suggestions for future research are presented. In this chapter, we focus on national value of travel time (and reliability) studies, that have been carried out especially in Europe, and that have been feeding into official appraisal guidelines.

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The Value-of-Travel-Time (VTT) expresses travel time gains into monetary benefits. In the field of transport, this measure plays a decisive role in the Cost-Benefit Analyses of transport policies and infrastructure projects as well as in travel demand modelling. Traditionally, theory-driven discrete choice models are used to infer the VTT distribution from choice data. This study proposes an alternative data–driven method to infer the VTT distribution based on Artificial Neural Networks (ANNs). The strength of the proposed method is that it is possible to uncover the VTT distribution (and its moments) without making strong assumptions about the shape of the distribution or the error terms, while being able to incorporate covariates and account for panel effects. We apply our method to data from the 2009 Norwegian VTT study. Finally, we cross-validate our method by comparing it with a series of state-of-the-art discrete choice models and other nonparametric methods used in the VTT literature. Based on the very encouraging results we have obtained, we believe that there is a place for ANN-based methods in future VTT studies.

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The value of travel time can theoretically be defined as the opportunity cost of travel minus the direct utility from spending the time during the trip. This paper searches for empirical evidence to support this formulation. We show that travellers who indicate that a shorter trip duration is useful or a longer trip duration is very inconvenient have a higher value of travel time. Furthermore, we show that people who can spend their travel time usefully have a lower value of travel time. Finally, the availability of a computer, laptop etc. during the trip increases the probability of travel time being useful. This study determines the sizes of these effects in a case study for The Netherlands. This is important since the value of travel time is likely to change over time. The outcomes of this paper can be used to estimate the size of the expected reduction in value of travel time as a result of future comfort improvements that increase the possibility to work, read a book, watch a movie and communicate during a trip.

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Objective Until 2016, the KiM Netherlands Institute for Transport Policy Research annually explained the development of train passenger kilometres over the preceding ten years by using the national development of its drivers (such as population growth and train kilometers), as well as the elasticities of demand for those drivers. Since some years this method appeared to be less appropriate for completely explaining the increase in train patronage (+24% from 2005 to 2016). To acquire a complete as possible explanation of patronage development, KiM developed a more thorough method, using the Ministry of Infrastructure and Water Management’s National Model System (LMS), in cooperation with experts of public transport and of LMS. The LMS is a forecasting system for simulating developments in mobility, as based on a spatiotemporal detailed model of the drivers of mobility. The application of this forecasting system for an ex post evaluation can be regarded as innovative. Method The LMS generates OD matrices between origin and destination zones for car, public transport and bicycle (trips and kilometers) on an average working day. The output depends on a number of influencing factors such as population, employment, income, car ownership, road congestion, and the public transport service levels and pricing. To explain the development of public transport use from 2005 until 2016 models runs were made of 2004, 2010 and 2014, because much input data were already available for those years . To assess the impact of each influencing factor, we compared a model run with another model run in which only that particular influencing factor was changed, as based on the actual development, while all other factors remained constant. We extrapolated the results to the period 2005-2016, as based on empirical national data. Results Based on LMS-analyses, we concluded that the 24% increase in train patronage during the period 2005-2016 was mainly determined by population growth (+5%), train passenger kilometres by students travelling with a student pass card (+4%) and improvements in level of service (+10%). Improvements in level of service consisted of higher frequencies, new rail lines, and better connections between train services. The increase in jobs, income, air traffic, road congestion and fuel price appeared to have small effects. A decreasing impact on train passenger kilometers was caused by the increase of train fares (-5%). The LMS analyses left a growth of 8% unexplained. From other data and analyses it appeared that this 8% increase in train passenger kilometres can be the result of a larger impact from the amount of students (about +1%), improved punctuality (about +1%), and a considerably smaller impact of price changes than -5%, because not all discount offers can be regarded as included in the LMS analyses. Conclusions LMS analyses explained the development of train passenger kilometers 2005-2016 better than the previous method, which was based on national developments of influencing factors and elasticities. Cause of the unexplained differences between the monitored developments of public transport use and the explanations based on LMS analyses can be: an insufficient degree of detail of data on patronage and level of service of public transport available for research, as well as insufficient representations in LMS analyses of socioeconomic factors, of pricing and of level of service. @en