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PrefaceThe objectives of the project 'Uncertainty in traffic forecasts' that RAND Europe carried out for the Transport Research Centre of the Dutch Ministry of Transport, Public Works and Water Management were:To develop a methodology to estimate the amount of uncertainty in forecasting for new infrastructure (especially roads).To implement and test this methodology in two case-studies (using the Dutch National Model system LMS and the New Regional Models NRM respectively).This report presents the outcomes of all phases of this project:Literature review for public projects;Literature review for public-private partnership (PPP) projects; Development of a method to quantify the uncertainty in traffic forecasts for the LMS and NRM;Outcomes from a large number (100) of model runs with the LMS to derive uncertainty margins around the mean traffic forecasts;Outcomes from a large number (100) of model runs with the NRM for the Dutch province of Noord-Brabant to derive uncertainty margins around the mean traffic forecasts.This report was written for modellers with an interest in the uncertainty margins around the model forecasts and methods to quantify the uncertainty margins.RAND Europe is an independent not-for-profit policy research organisation that serves the public interest by improving policymaking and informing public debate. This report has been peer-reviewed in accordance with RAND's quality assurance standards (see

11 The severity of road congestion not only depends on the relation between traffic volumes and network 12 capacity, but also on the distribution of car traffic among different time periods during the day. A new error 13 components logit model for the joint choice of time of day and mode is presented, estimated on stated 14 preference data for car and train travellers in The Netherlands. The results indicate that time of day choice 15 in The Netherlands is sensitive to changes in peak travel time and cost and that policies that increase these 16 peak attributes will lead to peak spreading. In the Netherlands, the Dutch National Model System for traffic and transport (LMS) has been 21 used since 1990 to predict the responses of travellers to a wide range of developments, such as 22 changing travel times (e.g. from congestion) or the imposition of time-dependent road user 23 charging. One of the results of these simulations has been that the choice of when to travel (time of 24 day choice) greatly affects the amount of congestion on the road network and that policies aiming 25 at spreading out peak travel can be effective instruments to relieve congestion. U N C O R R E C T E D P R O O F 26However, these results rely to a large extent on a time of day choice sub-model within the 27 Dutch National Model System, which is more than 10 years old. Moreover, this sub-model uses a 28 rather simple and restrictive specification: only three time periods are distinguished within a 29 working day, there are no links between the outward and inward leg of the same tour, and the 30 model is multinomial logit (MNL). Since then, congestion has increased considerably, casting 31 doubt about whether the old specifications will still hold, while modelling capabilities also im-32 proved. 33In this paper, a new model for the joint choice of mode and time of day is presented and es-34 timated on new stated preference data. The model is not restricted to shifts between large time 35 periods and follows the error components logit (EClogit; also called mixed MNL) specification. 36 Using this type of model, one can take account of the different degrees of substitution between 37 time periods (e.g. greater substitution between nearby periods than between periods further away 38 from each other) and between time of day alternatives and alternative modes. It is a tour-based 39 model, in which outbound time of travel, duration of the activity at the destination and mode 40 choice are determined simultaneously. 41This new model was developed to become the basis of a new time of day choice sub-module of 42 the Dutch National Model System. It also covers public transport users, whereas the old module 43 only referred to the time of day choice of car drivers. Most empirical studies into the choice of time of day have considered only the demand of 52 travellers for travel at different points of time or periods in time (mostly using discrete time pe-53 riods) for given travel time and/or travel cost. Impacts on congestion and feedback to ...

A method based on iterative proportional fitting (IPF) is developed for generating synthetic populations for the application of Albatross, a rule-based and activity-based model of travel demand. The Dutch Travel Survey (OVG) provides sample data for populations to be synthesized by zone. A method is proposed to generate synthetic households on the basis of data on distributions of individuals. This method uses the concept of relation matrices to convert distributions of individuals to distributions of households in a preprocessing step. Furthermore, a method is proposed to address differences in populations that relate to locational characteristics.

Oriented Simulation System is summarized. This activity-based model of activity-travel behavior is derived from theories of choice heuristics that consumers apply when making decisions in complex environments. The model, one of the most comprehensive of its kind, predicts which activities are conducted when, where, for how long, and with whom, and the transport mode involved. In addition, various logical, temporal, spatial, spatial-temporal, and institutional constraints are incorporated in the model. The conceptual underpinnings of the model, its architecture, the functionality of its key agents, data collection, and model performance are discussed.