In 2017, the newly installed Dutch government announced in its coalition agreement to introduce a distancebased heavy goods vehicle charge, similar to the charges levied in other European countries. To study the possible transport impacts, we applied available models and methods in preparation for the introduction of this truck charge in the Netherlands in 2023, in order to present decision information to the government on different aspects of the charge. In this paper we present the analysis of different implementation scenarios for a distancebased truck charge. Different behavioural responses can be expected in terms of freight transport demand, mode choice, logistic efficiency, and route choice. Many studies however lack sufficient detail to properly describe the
impact of road charges for HGV on OD flows and freight traffic. In our contribution we explore the bandwidth of impacts of different pricing schemes, using strategic transport models for freight demand and traffic assignment,
with detailed modal split and route choice models, ensuring a proper representation of generalsed transport costs. We explain how we use available transport models in an overarching analytical framework to make a
comprehensive impact assessment of the different responses, and to decompose the impacts on the different responses. Final impacts are quantified in terms of freight demand (spatial pattern of transport flows), modal split and traffic flows (route choice, and congestion).
Depending on the pricing scenario the tonne kilometres decrease by 0.4%–4.8% on average. The modal shift impacts are stronger on longer transport distances: this is explained both by the larger impact of a distance-based charge on these routes, and by higher substitution possibilities to barge or rail. The results indicate that the overall impacts of the introduction of the different charging alternatives are moderate. However, the network impacts at local level can be substantial due to the impact of re-routing of truck trips to avoid charge.@en

There are some examples where freight choices may be of a multiple discrete nature, especially the ones at more tactical levels of planning. Nevertheless, this has not been investigated in the literature, although several discrete-continuous models for mode/vehicle type and shipment size choice have been developed in freight transport. In this work, we propose that the decision of port and mode of the grain consolidators in Argentina is of a discrete-continuous nature, where they can choose more than one alternative and how much of their production to send by each mode. The Multiple Discrete Extreme Value Model (MDCEV) framework was applied to a stated preference data set with a response variable that allowed this multiple-discreteness. To our knowledge, this is the only application of the MDCEV in regional freight context. Free alongside ship price, freight transport cost, lead-time and travel time were included in the utility function and observed and random heterogeneity was captured by the interaction with the consolidator’s characteristics and random coefficients. In addition, different discrete choice models were used to compare the forecasting performance, willingness to pay measures and structure of the utility function against.

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This chapter aims to provide an overview of the overall set-up of transport models and their applications, plus a reflection on transport modeling itself. Main characteristics of transport models are discussed with special attention for the four main components: trip generation, trip distribution, modal split, and network assignment. Both aggregate and disaggregate model approaches are considered. Furthermore, a description is given of practical issues when building and using these models in practice, with special attention for quality control. The main focus is on passenger transport but related models for freight transport models and land use and transport interaction are briefly discussed. The chapter concludes with a reflection on the value and limitations of transport modeling and an overview of new modeling developments.

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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

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 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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