One of the options to reduce CO2 emissions as a result of transport, is to decrease the number of vehicle kilometres driven. Carpooling and teleworking are possibilities to obtain that goal. The paper will analyse their potential contribution.

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).

To prepare the Dutch regional and national strategic transport models (LMS/NRM) for policy questions of the future, Rijkswaterstaat – WVL wants to improve the correspondence between link speeds and route travel times estimated by these models in the base year and observed link flows and route travel times. This paper describes a project in which the heavily congested model of NRM-West (the regional model of the Randstad containing Amsterdam, Rotterdam, The Hague and Utrecht) is used as a testcase for modelling improvements to the LMS/NRM to better fit with observed (big) data mentioned before.
In this project commissioned by Rijkswaterstaat – WVL, the traffic assignment model of LMS/NRM is replaced by the quasi dynamic traffic assignment model STAQ (first described in Brederode et al., 2010), and the OD matrices are recalibrated taking into account the flow metering effects of active bottlenecks as observed, together with the traditional calibration on observed flows from loop detector data.
Because implementation of a different assignment model in the LMS/NRM methodology implies a lot of (potential) side effects to the model system, we restrict ourselves in this project to not make any changes to the software (solve) used for the matrix estimation itself. Instead congestion effects are taken into account while generating assignment matrices (a.k.a. screenline matrices) based on STAQ output. This means that the methods tested within this project can easily be transferred to models using other matrix estimation methods intended to be used with static traffic assignment models. Incorporation of congestion effects into the assignment matrices also allows for direct comparison of flows from the assignment model with observed flows, instead of comparing unconstrained modelled demand with a ‘link demand’ (referred to as ‘wensvraag’ in dutch) estimated from the observed flow, as is currently the case for LMS/NRM.
The paper primarily presents (preliminary) results of the project in the form of comparisons of observed congestion patterns, speeds and travel times with assignment results. Furthermore, the paper describes what needed to be done to be able to use STAQ in NRM/LMS, how the flow metering effects on active bottlenecks as calculated by STAQ are used to construct assignment matrices that take flow metering into account, along with pitfalls, limitations and opportunities of the method in practice.
Furthermore, the paper provides a perspective on applicability of a matrix estimation method that can include observed travel times (from e.g. floating car data) and distribution patterns (from e.g. GSM data) in its objective function on top of the methodology described above. Depending on progress on this method, which is being undertaken as part of ongoing PhD research of the lead author of this paper, preliminary results on the NRM-West will be included.