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The Netherlands national model: a review of seven years of application
Pergamon
0969---6016(93)E0012-4
Int. Trans. Opl Res. Vol. I, No. 2, pp. 125 133, 1994 Elsevier Science Ltd. Copyright ~c 1994 IFORS Printed in Great Britain. All rights reserved 0969-6016/94 $6.00 + 0.00
The Netherlands National Model" a Review of Seven Years of Application HUGH GUNN Hague Consulting Group, The Netherlands Disaggregate modelling is now firmly established as a powerful and practical alternative to the traditional four-stage models originally developed in the sixties. The disaggregate methodology was originally pioneered in the United States, but much important development has taken place in Europe in the 1980s. The basis of the modelling and the scope of the models both broadened and developed. A substantial advance was made by establishing a link between the models and classical theories of micro-economics, allowing the development of 'behavioural' models consistent with rational decision-making. The competitive, or sometimes complementary, roles of other modes of travel have been recognised and brought into the modelling framework. In recent years, forecasts of travel demand have been generated in studies in a number of countries in Northern Europe. These studies have encountered a common problem, which is that the assumptions and capabilities of the standard methodology have not been appropriate to address the problems of planning facilities in the early twenty-first century. Amongst the principal difficulties are: I. the population base is expected to change radically in terms of its age distribution - this the legacy of the Second World War, increased life expectancy and the aftermath of altered behaviour concerning family formation, linked to an increased participation of women in the work force; 2. the work force itself is expected to be radically different, also due to increased female participation; 3. there is an increasing pressure to suppress travel by private car, by any means politically feasible, in the anticipation of growing damage to the environment; 4. in consequence to the previous remark, there is the emergence of new types of travel (in particular, organised car-pooling) and new types of regulation of movement ('demand management' measures to control car commuters, and road-pricing policies to reduce peak-hour demand). Increasingly, the modellers are asked to look at very different futures to the present day, and the models themselves are required to perform a role very much more demanding than the mere extrapolation of present day trends. This paper reviews the performance of one particular disaggregate demand model system, the Netherlands National Model, used over a period of seven years to address the problem of producing forecasts appropriate to these new circumstances. The emphasis is on the results of the work, and the lessons that have been learned in the application of the system. Some discussion is given around the extension planned to the system in coming years. Key words: transport, planning, modelling, forecasting, microcomputers, simulation
INTRODUCTION Many northern European countries are again facing an expansion of demand for personal accessibility, with a strong preference for private mobility, i.e. car ownership and use. The trends are mainly prompted by increasing wealth, increasing numbers of adults and the increasing adoption of travel habits which are relatively extravagant in terms of both time and money spent on travel. Catering for this demand in terms of providing corresponding increases in roadspace and parking capacity will have serious consequences in terms of economic and environmental costs. Failing to cater for the demand would be likely to prove no less dangerous, with sharply rising economic and social costs of time lost in congested traffic conditions. The Netherlands is one such country, and has since 1985 mounted a series of national reviews and planning exercises which have sought to quantify the problem and evaluate combinations of policies designed to achieve a politically acceptable solution. Computer simulations of travel demand have played a central role in this work. There is, of course, a long history of the use of computer simulation in transportation planning. Classically, forecasts of traffic levels have been based on simulations of the processes leading to the demand for travel between origins and destinations and subsequent simulations of the routes chosen through the networks.
Correspondence: H. Gunn. Ha.que Consulting Group. Surinamestraat 4. 2585 GJ Den Haao. The Netherlands
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Early models, built in the 1950s to the 1970s, relied on simple correlations between aggregate statistics and on historic trends (Martin et al., 1961). In recent years, disaggregate modelling techniques have been developed to provide an alternative basis for the models (Ben-Akiva and Lerman, 1985; Hensher and Johnston, 1981; Ortuzar and Willumsen, 1990). Amongst the advantages that these techniques offer are that they allow the identification of highly structured models, which have the advantage of being more directly presentable in terms of individual decision-making (i.e. more easily communicated to the decision-maker) and of surviving various tests of transferability to other contexts (i.e. more credible to the professional planner, Gunn et al., 1985). The purpose of this paper is to give an overview of the experience gained during seven years of use of the Netherlands National Traffic Model, a large-scale disaggregate model travel-demand forecasting application system, to discuss the lessons learned and offer some comments on the future development of such systems.
BACKGROUND In the early 1970s, the Netherlands Ministry of Transport, Public Works and Water Management initiated a research programme designed to further the state of the art in applied disaggregate modelling for forecasting travel demand (Richards and Ben-Akiva, 1975). The early studies achieved considerable success in that they demonstrated new ability to specify complex model systems, highly parametrised and interacting between different levels of decisionmaking (short-term/long-term, household/individual), and to estimate the parameters from survey data of the sort available to the planning authorities. However, a remaining problem was that the model systems, whilst satisfying in a scientific sense in terms of rationale and achievable accuracy of measurement, were not directly suitable to derive origin-destination trip matrices. When the input data was not itself disaggregate, but zonal averages, the systems could be adjusted to cope, but did not perform to their full potential. In times when road-building is a major policy response to rising demand, predictions of loads on future networks are vital, and origin-destination matrices must be produced. For this reason (amongst others) traditional aggregate model systems remained in use as the standard methodology for regional planning studies in The Netherlands, as elsewhere. In the early 1980s, work was initiated to investigate the stability and transferability of the disaggregate models derived in the 'Zuidvleugel' study (Daly and van Zwam, 1983), and to develop applications systems which would be capable of generating trip matrices. Central to this effort was the 'Overdraagbaarheid' study (OVD) (Gunnet al., 1985; Gunn and Pol, 1985), during which the idea of prototypical sampling (described below) was adopted as the means by which the disaggregate models were used to generate area-wide forecasts. Models of driving licence ownership, carownership, trip generation and mode and destination choice were developed, all of the general Iogit form. In 1983, preparatory work began on models to support the Second Netherlands Transport Structure Plan (Netherlands Ministry of Transport, 1990), and a new design was proposed for the simulation system (Daly and Gunn, 1985). Essentially, the idea was to use the strengths of the best available models, aggregate and disaggregate, together with any available data. Crucially, semi-aggregate dynamic models of licence holding and car-ownership were imported to control the corresponding disaggregate models, and a 'pivot point' approach was taken for the models of choice of mode and destination, using the OVD models to predict changes in demand which were then used to adapt a best-available base matrix of origin-destination flows. The result was the first version of the Netherlands National Model System (NMS). It has been in continual use since 1986, with the core models unchanged, but with many adaptations and extensions as political interest has focussed on new ideas and complex inter-related sets of policy strategies.
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DESCRIPTION OF THE BASIC SYSTEM The National Model System (NMS) is designed to be a system of models to predict long-term developments in travel by all land-based travel modes under various explicit hypotheses about developments in national socio-economic structures, land-use patterns and transport policy scenarios (increased fuel taxes, vehicle purchase taxes, improvement of public transport, etc.). The major innovations in the system, which is now a microcomputer-based application, are that: (a) it is assembled from a series of sub-modules, each of which is based on model structures compatible with rational individual (and household) decision-making, and which are calibrated using disaggregate data on individuals and households; (b) it includes a specially designed stage at which forecast year populations are simulated in detail prior to any travel demand prediction, using local planning data; (c) it addresses the problem of the change of attitudes towards the ownership and use of private vehicles as between the older generations and the younger, incorporating predictions based on a 'cohort-replacement' model of aggregate driving licence ownership; (d) it uses home-based tours as the primary unit of travel, allowing a more direct linkage to participation in out-of-home activities (the true causal demand behind travel) and ensuring coherence of mode-usage throughout the tour; (e) it addresses an exceptionally comprehensive set of travel-related decisions, including driving licence acquisition, car-ownership, tour frequency, mode and destination choice, departure time and route choice; (fl in application, all of the trip-related features are allowed to interact (i.e. an equilibrium is made over route choice, departure time choice and mode and destination choice); (g) it exploits the stability of a 'best-estimate' base year matrix, generating forecasts using this as a pivot-point; (h) the time-of-day module has been extended with Stated Preference data to simulate the effects of peak-hour specific traffic speed and/or pricing measures; (i) a novel assignment algorithm has been developed to deal with the problems of estimating bottlenecks and delays on severely over-capacity networks. To be accepted by the policy makers, the system must be seen to deal realistically with the variety of external factors which will contribute to the changing nature of travel demand in the coming years. Thus, not only future trends in prices, income, population, and the number of households must be considered; the composition of households in terms of age/sex and occupation patterns is crucial to the mix of out-of-home activities which are undertaken. For forecasts over l0 25 year horizons, there is evidence that changes in travel patterns due to such compositional changes can dominate any income-related changes (Vrolijk et al., 1987). Thus, it is necessary to retain a high level of disaggregation in the forecasting - not only to capture such changes in types of households, but also to be able to isolate the benefits and disbenefits of specific control policies for different subgroups of the population. It is also necessary that the model system takes account of all land-based modes of travel within a comprehensive framework, since policies which affect the attractiveness of one mode affect the relative attractiveness of all. The structure of the resulting model system is depicted in Fig. I. The components, numbered in Fig. 1, are each described in the Appendix.
EVOLVING USE AND MODEL D E V E L O P M E N T Originally, the NMS had broad strategic aims, in particular to assist in identifying corridors which should be reserved for future network development (road and rail). The first versions of the model had a very simple time-of-day module (fixed fractions), and used conventional equilibrium assignment capabilities from standard packages. However, when the basic system was 'completed' and the first forecasts of overall demand generated, an immediate problem was encountered. The predicted levels of growth in car traffic were much higher than anticipated, prompting discussion that brought in planners from many disciplines.
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Aggregate driving licence projections
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Fig. 1. Structure of the model system.
Old debates were reopened - what role does the network development play? Is land-use the key to controlling demand? Are income levels reasonable'? Various trial runs of the models were performed to test performance, and to put the growth into context. Of particular use in understanding the system were demonstration runs in which mixtures of input assumptions from base year and forecast year were used. For instance, a base year (originally 1983) run could be made with 2010 levels of car-ownership, or 2010 runs with 1983 networks and so on. Some results from these tests are given in Gunn et al. (1987): others are given in Vrolijk et al. (1987). The fact that the growth could be broken down into components proved a great asset to presentation. In particular, the first forecasts were of little or no growth in the public transport sector, despite the large increase in the number of older people and commuters. This proved much more credible when it was demonstrated that it was the penetration of licence ownership and car availability in these groups which kept them out of public transport. Arguments were diverted away from the 'overall system' towards the licence and car-ownership modelling, and in particular to the credibility of the cohort-based aggregate totals. The NMS survived the examination. At much the same time, suggestions were made that called for the development of extra model capabilities, addressing factors not usually taken into account in regional transport planning. Two examples of these are the effect of chamjing work practices and the effect ~/'telematica (teleworking, teleshopping etc.). In respect of each of these, the suggestion was made that the model must overestimate growth by neglecting the trends.
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The response of the study team was to attempt to extend the system to take the criticism into account. An existing National Travel Survey provided information about the trip/tour rates and travel purposes of workers who spent different hours at work, and on the basis of this evidence a module was added which re-calculated annual average trip rates conditional on input assumptions about the numbers of holidays given per year, and the length of the working day. The effects of telematica could not be judged from available survey data, but there were available 'acceptable' Delphi-oracle style forecasts at an overall national level. The model was used to identify a likely set of commuters who might take advantage of that style of work (high-income travellers with difficult journeys to urban areas) and the trip matrices were reduced accordingly. The net impact of these extensions was not large, but the fact that an accommodation could be found for these new ideas brought extra interest in the system itself. Yet another problem emerged when the matrices were to be assigned to the networks; in the 'base' scenario for the year 2010, growth was so large that the networks became virtually saturated; multiple bottlenecks appeared after assignment of the matrices. Many of these bottlenecks were unrealistic, and were only generated because the assignment procedures were static, and took no account of the fact that traffic trapped in one bottleneck could not simultaneously be trapped in several others further downstream. Here too, the reaction of the study team was to develop a new procedure. This was the 'Q-net' algorithm (Hungerink, 1989), a heuristic approach which implemented a check on the feasibility of immediately up-stream links actually delivering sufficient traffic to cause a bottleneck. Whilst the original algorithm only checked and corrected backwards until the first feasible case was found, in practice it removed most of the problems of the multiple bottlenecks, and demonstrated a satisfactory performance in locating the queues in the base year national network. By this stage in the development, the overall system had been quite widely accepted, and was in use in the course of several major national planning studies. A consequence was that the scientific work had to proceed at a pace dictated by the needs of the political debate, i.e. totally independent of the difficulty of the new problems that were emerging. However, much more development was still needed. The growth in vehicle traffic under existing policies had been deemed unacceptable by the standards set at the outset of the Transport Structure Plan, and by the objectives of the parallel National Environment Plan. New policies were needed. There followed the introduction of intensely pro-public transport scenarios high speed, low cost, wider access. The results were disappointing; public transport demand grew, but few car-drivers changed their mode of travel. Once again a major debate ensued as to the model's credibility and completeness. The resolution of the problem finally involved the demonstration of the high proportion of trips being made between zone pairs for which a public transport alternative was absent or totally uncompetitive for journey time reasons. Imported evidence on cross-elasticities, applied unselectively across The Netherlands, was simply misleading. Improving public transport did indeed generate more public transport kilometres but mainly from the traditional users (Baanders et al., 1991). This conclusion strengthened the need to investigate Road-User-Charging measures, and in turn to extend the model system to deal with new aspects of behaviour. In particular, a focus came tirst on time-of-day choice and modal split under different road-pricing and congestion-level scenarios. As with telematica, no historical evidence was available on which to base model extensions; here, Stated Preference techniques (HCG/SDG, 1990) were used to investigate sensitivities. Two major studies were launched, one focussing on cost sensitivities in the presence of congestion, the other focussing on congestion effects directly. Early ideas about Road-User-Charging systems were based on the notion of charging proportional to kilometrage driven at particular times of day on particular parts of the network. However, later policies considered a wide variety of other options, some involving complex methods of payment; here too Stated Preference experiments were used to establish likely demand, and the system extended to include these options (Daly et al., 1990). By this time, the Second Transport Structure Plan was coming due for delivery. The major concern, for both environmental and economic reasons, was to reduce private car use. However, no level of charging had been identified which was judged both sufficiently effective and politically feasible, even taken in tandem with large increases in fuel costs. Improved engine performance played ITOR I:2-B
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a double role here - on the one hand mitigating the deterrant effects of the fuel price rises on car-use, on the other reducing the emissions of toxic gases thus reducing the amount by which car-use had to be suppressed (Bovy and Gunn, 1991 ). Additional policies were brought into the package, and into the model system. One set of such policies was related to a more general spatial policy very typical of The Netherlands, which in broad terms may be thought of as aiming at matching suitable businesses with suitable locations. In terms of transport policy, this general policy aim can be pursued by means of restrictions on parking spaces, matching numbers of spaces with the type of location measures according to the quality of accessibility to public transport. Three types were distinguished, being: • 'A-locations', with high quality public transport, for which employment of specific sorts were identified; those employers requiring low numbers of parking spaces per employee would be suitable for these locations, • 'B-locations', with high quality accessibility by both public transport and car, for which other specifically suitable employment types were identified having a somewhat higher requirement for spaces per employee, • 'C-locations', with low quality public transport and high quality car accessibility, also matched with specific employment types, and having no restrictions on parking spaces. Two different scenarios were investigated, simulating ways in which the restrictions might affect mode choice. The first policy was simulated by simply 'capping' the number of car-trips entering a number of key zones (chosen for high public transport accessibility) and spreading the displaced drivers amongst their alternative modes as if car-driver was unavailable as an option. The second allowed the alternative of parking in a nearby zone and walking to the final destination. The first, which imitates some Californian Demand Management schemes, proved a very powerful policy tool; the second was much less effective, demonstrating again the relative superiority of the car-driver mode to those who have it available. At this stage, the Structure Plan was completed; a package of policies were identified, including road-pricing, fuel price rises, demand management measures, public transport improvements, improvements in accessibility by bicycle (a popular mode in The Netherlands). The package actually failed to suppress car-use to the degree required by the stated objectives (Bovy and Gunn, 1991 ); the imperatives of the political timetable then dictated that the Plan be released assumin9 the imposition of 'extra policy measures' which would jointly achieve the required reduction. These measures were to be sought in variant road-user-charging schemes, separate facilities for high-occupancy vehicles, subsidised carpool facilities and so on. At present, the NMS is being extended yet again to evaluate these new proposals, and in an attempt to identify the necessary policy package.
PARALLEL D E V E L O P M E N T S Above, the existing model system has been outlined, and put in the context of the prolonged period of development during which it has grown. Space precludes a fuller description, but some mention of the parallel developments around the model is appropriate. Firstly, there is the issue of the interaction between land-use and the transport system. During the course of the various National Plans, there has been examination of variants of land-use patterns provided by the agencies responsible. There has been dialogue, in the sense that forecasts of travel patterns and accessibility levels have been brought back to the land-use planners and they have been able to incorporate the information in their procedures and thinking. However, there is no explicit connection between land-use and accessibility. Valid criticism has been made of this; for example, with a high number of wealthy retired with no job ties and many workers in 'mobile' high-tech industries, would not an area-selective road-pricing scheme which focussed on the developed West and Central areas lead to re-housing in the North and East? Of course, at the margin it will but to what extent'? In the attempt to answer this sort of question, a formal link was established between the NMS and
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the land-use component of a commercially available land-use/transport package. This experiment demonstrated the feasibility of the link-up, and the availability of the necessary data, but the land-use module was unsuccessful in generating credible forecasts. Further research is now proposed, taking a more pragmatic approach. A second major parallel development is the construction of Regional versions of the NMS. Following the completion of the Structure Plan, the execution of a large proportion of the adopted policies rests with the regional authorities. Locally adapted versions of the NMS are being built to provide more sensitive forecasting tools to assist in this process. Finally, in recent years, Hague Consulting Group has used the NMS as a blueprint for models in Norway, Sweden and France as well as The Netherlands; in addition, the general approach has been tailored to apply to travel demand forecasting for fixed-link investment; models have been built for traffic across the English Channel, for traffic in the corridor between Sydney and Melbourne and for traffic across the Danish Great Belt. The general approach has proved flexible and capable of adaptation to different circumstances, accepting different component models (often based on Stated Preference data) as needed.
CONCLUSIONS The major success of the Netherlands NMS has been in being widely accepted as a reasonable basis for constructive planning over a seven year period. This level of acceptance has been achieved by demonstrating a credible scientific basis for the models, and in providing persuasive explanation for those results which were challenged. Disaggregation and the use of models compatible with rational decision-making have been the key to this, in terms of the quality of the intellectual basis for the work, in terms of the comprehensibility of the explanation for unexpected results, and in terms of avoiding intrinsically illogical results due to model misspecification (so-called 'pathological errors' ). The model is now approaching a period in which the validity of all of its components as medium/long range forecasting tools can be tested. With the data now available, a further modelling exercise is now possible. This could establish a stronger basis for some of the behavioural models within the system, to introduce time-dynamic effects into all of the models which can be improved with such treatment (not just the licence holding and car-ownership aggregate models) and to incorporate some treatment of the consequences of changing attitudes. This seems likely to become increasingly important in a population which is increasingly being exposed to pro-public transport of a sort previously used only for selling cars and fuel, and indeed for the first time to focussed anti-car-use advertising. Meanwhile, the general structure of the model is finding increasing use as an alternative to more traditional travel demand forecasting systems. Acknowledgements The work described here was funded by the Transportation and Traffic Research Division of the Ministry of Transport, Public Works and Water Management, who collaborated closely with Hague Consulting Group throughout the development of the NMS and indeed in the earlier disaggregate modelling st udies which paved the way for this application. Views expressed in this paper do not necessarily reflect those of the Transportation and Traffic Research Division. Responsibility for all errors and omissions remains with the author.
REFERENCES Baanders, A., Bovy, P. & van der Hoorn, A. (I 991 J. Substitution of travel demand. Proceedings of the PTRC Summer Annual Meeting, PTRC Education and Research Services Limited, London, UK. Ben-Akiva, M. & Lerman, S. (1985j. Discrete Choice Analysi.s. MIT Press: Cambridge, MA, USA. Bovy, P. & Gunn, H. (1991). The SVV in numbers. Vervoersplanologisch, Colloquium, Den Haag. Broecke, A. v.d. (1987). Netherlands Car-Ownership Forecasts. Den H aag: Rijkswaterstaat. Daly, A. J. & Zwam, H. v. d. (1983). Application of Disaggregate Models for a Regional Transport Study in The Netherlands. Presented to the World Conference on Transport Research, Hamburg. Daly, A.J. & Gunn, H. (1985). Cost-effective methods for national-level demand forecasting. Presented to the Conference on Behavioural Research for Transport Policy, Noordinyk, Netherlands. Daly, A. J., Gunn, H. F., Hungerink, G. & Vrolijk, P. (1990). Applications of the Netherlands National Model to support investigations of transport policy. Proceedings of thc PTRC Summer Annual Meeting. PTRC Education and Research
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Services Limited, London, UK. Gunn, H. & Pol, H. D. P. (1985). The OVD regional travel demand forecasting system. Proceedings of the PTRC Summer Annual Meeting, PTRC Education and Research Services Limited, London, UK. Gunn, H.F., Daly, A.J. & Hoorn, A.v.d. {1987). Long-range, country-wide forecasts of travel demand from models of individual choice. Conference on Travel Behaviour, Aix-en-Provence. INRETS, France. Gunn, H, F., Ben-Akiva, M. & Bradley, M. (1985). Tests of the scaling approach to transferring disaggregate travel demand models. Transportation Research Record, Washington, No. 1037. HCG/SDG (1991). Stated Preference Techniques, 2rid edn. The Hague: Hague Consulting Group; London: Steer Davies Gleave. Hensher, D.A. & Johnston, L.W. (1981). Applied Discrete Choice Modellinff. London: Croom Helm. Hungerink, G. (1989). Q-net: assignment on over-congested networks by link inflow constraint. Proceedings of the Capri Conference. Martin, B. V., Memmott, F. W. & Bone, A.J. (1961). Principles and techniques of predicting future demand for urban area transportation. Research Report number R63-1. Massachusetts Institute of Technology, Cambridge, Massachusetts. Ortuzar, J. d. D. & Willumsen, L.G. (1990). Modellino Transport. Chichester: John Wiley & Sons. Richards, M. & Ben-Akiva, M. (1975). A Disao,qreqate Travel Demand Model. Lexington, MA: D.C. Heath. Vrolijk, P., Gunn, H. F. & Hoorn, A. v. d. (1987). Waar komt de groei vandaan? Proceedings of the 14th CVS Colloquium, Den Haag.
APPENDIX:
COMPONENTS
O F T H E N M S (see Fig. 1)
As illustrated in Fig. 1, there are six main components to the system.
I. A 'prototypical' sample of households The framework for the forecasts is a sample of roughly 1000 households from an existing data source (The Netherlands National Travel Survey). Based on the characteristics of each household and its members, expansion factors are assigned so that the total expanded sample matches a number of 'target values' corresponding to the demographic/economic scenario and the forecast year of interest. This is done separately.for each o["345 zones coverinq The Netherlands.
2. A99regate projections of population 9rowth and drivin9 licence acquisition Important background trends in demographics and assumptions about economic growth are simulated into the future to project the aggregate evolution of population growth and driving licence penetration. The simulation uses the "cohort" approach, following groups of the same age and sex over time, and using models calibrated on the National Travel Survey.
3. Disayyreyate models of household drivin9 licence holdin9 Sensitivity of licence holding to policy variables is provided through the use of household-level choice models. Logit models were estimated, and are applied to predict the number of licences within each household in the artificial sample. The results can then be checked and adjusted against the aggregate licence-holding projections, which contain trend effects not captured in the cross-sectional models.
4. Disa(49reoate models of household car ownership Car ownership and usage are expected to be the key household decisions which will influence personal mobility. A model was estimated which predicts car ownership in a consistent microeconomic framework. This model is applied to each household in the sample, separately for each zone in the country, conditional on the predicted licence holding. The model is sensitive to two key control policy variables - fixed and variable car costs.
5. Disaggregate models of travel Based on purpose-designed surveys, using both cordon/screenline data and household interviews, models were cstimatcd of the frequency of making journeys for various purposes, and of the choice of mode and destination of travel (car driver, car passenger, public transport, or walk/cycle). These models are applied to each person in the prototypical sample, which at this stage is broken down into some I 132 subzones to improve the estimates of local accessibility. The models thus have access to full demographic details of the 'prototypical individuals', and of their other household members, and their geographical
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location. Further, the models can be conditioned on the predicted car ownership and licence holding. These models are sensitive to policy variables such as car and public transport times and costs (including parking, tolls, road-pricing), and to changes in land use (including the availability of space for parking, the location of employment places and education places, etc.).
6. Disay~tregate models of time-of-day choice A feature of the model system is the development of a time-of-day module sensitive to times and costs, which proves an estimate of the impact of peak-hour congestion and/or road-pricing on the usual pattern of departure times associated with different travel purposes. This model was derived from two purpose-designed Stated Preference experiments. In operation, these models are applied within an interactive loop, equilibrating time-of-day profiles with network costs by repeatedly assigning traffic, noting costs and applying the time-of-day model. This loop is itself a sub-loop of a primary feedback between the network costs and the person-level travel models.
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Int. Trans. Opl Res. Vol. I, No. 2, pp. 125 133, 1994 Elsevier Science Ltd. Copyright ~c 1994 IFORS Printed in Great Britain. All rights reserved 0969-6016/94 $6.00 + 0.00
The Netherlands National Model" a Review of Seven Years of Application HUGH GUNN Hague Consulting Group, The Netherlands Disaggregate modelling is now firmly established as a powerful and practical alternative to the traditional four-stage models originally developed in the sixties. The disaggregate methodology was originally pioneered in the United States, but much important development has taken place in Europe in the 1980s. The basis of the modelling and the scope of the models both broadened and developed. A substantial advance was made by establishing a link between the models and classical theories of micro-economics, allowing the development of 'behavioural' models consistent with rational decision-making. The competitive, or sometimes complementary, roles of other modes of travel have been recognised and brought into the modelling framework. In recent years, forecasts of travel demand have been generated in studies in a number of countries in Northern Europe. These studies have encountered a common problem, which is that the assumptions and capabilities of the standard methodology have not been appropriate to address the problems of planning facilities in the early twenty-first century. Amongst the principal difficulties are: I. the population base is expected to change radically in terms of its age distribution - this the legacy of the Second World War, increased life expectancy and the aftermath of altered behaviour concerning family formation, linked to an increased participation of women in the work force; 2. the work force itself is expected to be radically different, also due to increased female participation; 3. there is an increasing pressure to suppress travel by private car, by any means politically feasible, in the anticipation of growing damage to the environment; 4. in consequence to the previous remark, there is the emergence of new types of travel (in particular, organised car-pooling) and new types of regulation of movement ('demand management' measures to control car commuters, and road-pricing policies to reduce peak-hour demand). Increasingly, the modellers are asked to look at very different futures to the present day, and the models themselves are required to perform a role very much more demanding than the mere extrapolation of present day trends. This paper reviews the performance of one particular disaggregate demand model system, the Netherlands National Model, used over a period of seven years to address the problem of producing forecasts appropriate to these new circumstances. The emphasis is on the results of the work, and the lessons that have been learned in the application of the system. Some discussion is given around the extension planned to the system in coming years. Key words: transport, planning, modelling, forecasting, microcomputers, simulation
INTRODUCTION Many northern European countries are again facing an expansion of demand for personal accessibility, with a strong preference for private mobility, i.e. car ownership and use. The trends are mainly prompted by increasing wealth, increasing numbers of adults and the increasing adoption of travel habits which are relatively extravagant in terms of both time and money spent on travel. Catering for this demand in terms of providing corresponding increases in roadspace and parking capacity will have serious consequences in terms of economic and environmental costs. Failing to cater for the demand would be likely to prove no less dangerous, with sharply rising economic and social costs of time lost in congested traffic conditions. The Netherlands is one such country, and has since 1985 mounted a series of national reviews and planning exercises which have sought to quantify the problem and evaluate combinations of policies designed to achieve a politically acceptable solution. Computer simulations of travel demand have played a central role in this work. There is, of course, a long history of the use of computer simulation in transportation planning. Classically, forecasts of traffic levels have been based on simulations of the processes leading to the demand for travel between origins and destinations and subsequent simulations of the routes chosen through the networks.
Correspondence: H. Gunn. Ha.que Consulting Group. Surinamestraat 4. 2585 GJ Den Haao. The Netherlands
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Early models, built in the 1950s to the 1970s, relied on simple correlations between aggregate statistics and on historic trends (Martin et al., 1961). In recent years, disaggregate modelling techniques have been developed to provide an alternative basis for the models (Ben-Akiva and Lerman, 1985; Hensher and Johnston, 1981; Ortuzar and Willumsen, 1990). Amongst the advantages that these techniques offer are that they allow the identification of highly structured models, which have the advantage of being more directly presentable in terms of individual decision-making (i.e. more easily communicated to the decision-maker) and of surviving various tests of transferability to other contexts (i.e. more credible to the professional planner, Gunn et al., 1985). The purpose of this paper is to give an overview of the experience gained during seven years of use of the Netherlands National Traffic Model, a large-scale disaggregate model travel-demand forecasting application system, to discuss the lessons learned and offer some comments on the future development of such systems.
BACKGROUND In the early 1970s, the Netherlands Ministry of Transport, Public Works and Water Management initiated a research programme designed to further the state of the art in applied disaggregate modelling for forecasting travel demand (Richards and Ben-Akiva, 1975). The early studies achieved considerable success in that they demonstrated new ability to specify complex model systems, highly parametrised and interacting between different levels of decisionmaking (short-term/long-term, household/individual), and to estimate the parameters from survey data of the sort available to the planning authorities. However, a remaining problem was that the model systems, whilst satisfying in a scientific sense in terms of rationale and achievable accuracy of measurement, were not directly suitable to derive origin-destination trip matrices. When the input data was not itself disaggregate, but zonal averages, the systems could be adjusted to cope, but did not perform to their full potential. In times when road-building is a major policy response to rising demand, predictions of loads on future networks are vital, and origin-destination matrices must be produced. For this reason (amongst others) traditional aggregate model systems remained in use as the standard methodology for regional planning studies in The Netherlands, as elsewhere. In the early 1980s, work was initiated to investigate the stability and transferability of the disaggregate models derived in the 'Zuidvleugel' study (Daly and van Zwam, 1983), and to develop applications systems which would be capable of generating trip matrices. Central to this effort was the 'Overdraagbaarheid' study (OVD) (Gunnet al., 1985; Gunn and Pol, 1985), during which the idea of prototypical sampling (described below) was adopted as the means by which the disaggregate models were used to generate area-wide forecasts. Models of driving licence ownership, carownership, trip generation and mode and destination choice were developed, all of the general Iogit form. In 1983, preparatory work began on models to support the Second Netherlands Transport Structure Plan (Netherlands Ministry of Transport, 1990), and a new design was proposed for the simulation system (Daly and Gunn, 1985). Essentially, the idea was to use the strengths of the best available models, aggregate and disaggregate, together with any available data. Crucially, semi-aggregate dynamic models of licence holding and car-ownership were imported to control the corresponding disaggregate models, and a 'pivot point' approach was taken for the models of choice of mode and destination, using the OVD models to predict changes in demand which were then used to adapt a best-available base matrix of origin-destination flows. The result was the first version of the Netherlands National Model System (NMS). It has been in continual use since 1986, with the core models unchanged, but with many adaptations and extensions as political interest has focussed on new ideas and complex inter-related sets of policy strategies.
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DESCRIPTION OF THE BASIC SYSTEM The National Model System (NMS) is designed to be a system of models to predict long-term developments in travel by all land-based travel modes under various explicit hypotheses about developments in national socio-economic structures, land-use patterns and transport policy scenarios (increased fuel taxes, vehicle purchase taxes, improvement of public transport, etc.). The major innovations in the system, which is now a microcomputer-based application, are that: (a) it is assembled from a series of sub-modules, each of which is based on model structures compatible with rational individual (and household) decision-making, and which are calibrated using disaggregate data on individuals and households; (b) it includes a specially designed stage at which forecast year populations are simulated in detail prior to any travel demand prediction, using local planning data; (c) it addresses the problem of the change of attitudes towards the ownership and use of private vehicles as between the older generations and the younger, incorporating predictions based on a 'cohort-replacement' model of aggregate driving licence ownership; (d) it uses home-based tours as the primary unit of travel, allowing a more direct linkage to participation in out-of-home activities (the true causal demand behind travel) and ensuring coherence of mode-usage throughout the tour; (e) it addresses an exceptionally comprehensive set of travel-related decisions, including driving licence acquisition, car-ownership, tour frequency, mode and destination choice, departure time and route choice; (fl in application, all of the trip-related features are allowed to interact (i.e. an equilibrium is made over route choice, departure time choice and mode and destination choice); (g) it exploits the stability of a 'best-estimate' base year matrix, generating forecasts using this as a pivot-point; (h) the time-of-day module has been extended with Stated Preference data to simulate the effects of peak-hour specific traffic speed and/or pricing measures; (i) a novel assignment algorithm has been developed to deal with the problems of estimating bottlenecks and delays on severely over-capacity networks. To be accepted by the policy makers, the system must be seen to deal realistically with the variety of external factors which will contribute to the changing nature of travel demand in the coming years. Thus, not only future trends in prices, income, population, and the number of households must be considered; the composition of households in terms of age/sex and occupation patterns is crucial to the mix of out-of-home activities which are undertaken. For forecasts over l0 25 year horizons, there is evidence that changes in travel patterns due to such compositional changes can dominate any income-related changes (Vrolijk et al., 1987). Thus, it is necessary to retain a high level of disaggregation in the forecasting - not only to capture such changes in types of households, but also to be able to isolate the benefits and disbenefits of specific control policies for different subgroups of the population. It is also necessary that the model system takes account of all land-based modes of travel within a comprehensive framework, since policies which affect the attractiveness of one mode affect the relative attractiveness of all. The structure of the resulting model system is depicted in Fig. I. The components, numbered in Fig. 1, are each described in the Appendix.
EVOLVING USE AND MODEL D E V E L O P M E N T Originally, the NMS had broad strategic aims, in particular to assist in identifying corridors which should be reserved for future network development (road and rail). The first versions of the model had a very simple time-of-day module (fixed fractions), and used conventional equilibrium assignment capabilities from standard packages. However, when the basic system was 'completed' and the first forecasts of overall demand generated, an immediate problem was encountered. The predicted levels of growth in car traffic were much higher than anticipated, prompting discussion that brought in planners from many disciplines.
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Fig. 1. Structure of the model system.
Old debates were reopened - what role does the network development play? Is land-use the key to controlling demand? Are income levels reasonable'? Various trial runs of the models were performed to test performance, and to put the growth into context. Of particular use in understanding the system were demonstration runs in which mixtures of input assumptions from base year and forecast year were used. For instance, a base year (originally 1983) run could be made with 2010 levels of car-ownership, or 2010 runs with 1983 networks and so on. Some results from these tests are given in Gunn et al. (1987): others are given in Vrolijk et al. (1987). The fact that the growth could be broken down into components proved a great asset to presentation. In particular, the first forecasts were of little or no growth in the public transport sector, despite the large increase in the number of older people and commuters. This proved much more credible when it was demonstrated that it was the penetration of licence ownership and car availability in these groups which kept them out of public transport. Arguments were diverted away from the 'overall system' towards the licence and car-ownership modelling, and in particular to the credibility of the cohort-based aggregate totals. The NMS survived the examination. At much the same time, suggestions were made that called for the development of extra model capabilities, addressing factors not usually taken into account in regional transport planning. Two examples of these are the effect of chamjing work practices and the effect ~/'telematica (teleworking, teleshopping etc.). In respect of each of these, the suggestion was made that the model must overestimate growth by neglecting the trends.
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The response of the study team was to attempt to extend the system to take the criticism into account. An existing National Travel Survey provided information about the trip/tour rates and travel purposes of workers who spent different hours at work, and on the basis of this evidence a module was added which re-calculated annual average trip rates conditional on input assumptions about the numbers of holidays given per year, and the length of the working day. The effects of telematica could not be judged from available survey data, but there were available 'acceptable' Delphi-oracle style forecasts at an overall national level. The model was used to identify a likely set of commuters who might take advantage of that style of work (high-income travellers with difficult journeys to urban areas) and the trip matrices were reduced accordingly. The net impact of these extensions was not large, but the fact that an accommodation could be found for these new ideas brought extra interest in the system itself. Yet another problem emerged when the matrices were to be assigned to the networks; in the 'base' scenario for the year 2010, growth was so large that the networks became virtually saturated; multiple bottlenecks appeared after assignment of the matrices. Many of these bottlenecks were unrealistic, and were only generated because the assignment procedures were static, and took no account of the fact that traffic trapped in one bottleneck could not simultaneously be trapped in several others further downstream. Here too, the reaction of the study team was to develop a new procedure. This was the 'Q-net' algorithm (Hungerink, 1989), a heuristic approach which implemented a check on the feasibility of immediately up-stream links actually delivering sufficient traffic to cause a bottleneck. Whilst the original algorithm only checked and corrected backwards until the first feasible case was found, in practice it removed most of the problems of the multiple bottlenecks, and demonstrated a satisfactory performance in locating the queues in the base year national network. By this stage in the development, the overall system had been quite widely accepted, and was in use in the course of several major national planning studies. A consequence was that the scientific work had to proceed at a pace dictated by the needs of the political debate, i.e. totally independent of the difficulty of the new problems that were emerging. However, much more development was still needed. The growth in vehicle traffic under existing policies had been deemed unacceptable by the standards set at the outset of the Transport Structure Plan, and by the objectives of the parallel National Environment Plan. New policies were needed. There followed the introduction of intensely pro-public transport scenarios high speed, low cost, wider access. The results were disappointing; public transport demand grew, but few car-drivers changed their mode of travel. Once again a major debate ensued as to the model's credibility and completeness. The resolution of the problem finally involved the demonstration of the high proportion of trips being made between zone pairs for which a public transport alternative was absent or totally uncompetitive for journey time reasons. Imported evidence on cross-elasticities, applied unselectively across The Netherlands, was simply misleading. Improving public transport did indeed generate more public transport kilometres but mainly from the traditional users (Baanders et al., 1991). This conclusion strengthened the need to investigate Road-User-Charging measures, and in turn to extend the model system to deal with new aspects of behaviour. In particular, a focus came tirst on time-of-day choice and modal split under different road-pricing and congestion-level scenarios. As with telematica, no historical evidence was available on which to base model extensions; here, Stated Preference techniques (HCG/SDG, 1990) were used to investigate sensitivities. Two major studies were launched, one focussing on cost sensitivities in the presence of congestion, the other focussing on congestion effects directly. Early ideas about Road-User-Charging systems were based on the notion of charging proportional to kilometrage driven at particular times of day on particular parts of the network. However, later policies considered a wide variety of other options, some involving complex methods of payment; here too Stated Preference experiments were used to establish likely demand, and the system extended to include these options (Daly et al., 1990). By this time, the Second Transport Structure Plan was coming due for delivery. The major concern, for both environmental and economic reasons, was to reduce private car use. However, no level of charging had been identified which was judged both sufficiently effective and politically feasible, even taken in tandem with large increases in fuel costs. Improved engine performance played ITOR I:2-B
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a double role here - on the one hand mitigating the deterrant effects of the fuel price rises on car-use, on the other reducing the emissions of toxic gases thus reducing the amount by which car-use had to be suppressed (Bovy and Gunn, 1991 ). Additional policies were brought into the package, and into the model system. One set of such policies was related to a more general spatial policy very typical of The Netherlands, which in broad terms may be thought of as aiming at matching suitable businesses with suitable locations. In terms of transport policy, this general policy aim can be pursued by means of restrictions on parking spaces, matching numbers of spaces with the type of location measures according to the quality of accessibility to public transport. Three types were distinguished, being: • 'A-locations', with high quality public transport, for which employment of specific sorts were identified; those employers requiring low numbers of parking spaces per employee would be suitable for these locations, • 'B-locations', with high quality accessibility by both public transport and car, for which other specifically suitable employment types were identified having a somewhat higher requirement for spaces per employee, • 'C-locations', with low quality public transport and high quality car accessibility, also matched with specific employment types, and having no restrictions on parking spaces. Two different scenarios were investigated, simulating ways in which the restrictions might affect mode choice. The first policy was simulated by simply 'capping' the number of car-trips entering a number of key zones (chosen for high public transport accessibility) and spreading the displaced drivers amongst their alternative modes as if car-driver was unavailable as an option. The second allowed the alternative of parking in a nearby zone and walking to the final destination. The first, which imitates some Californian Demand Management schemes, proved a very powerful policy tool; the second was much less effective, demonstrating again the relative superiority of the car-driver mode to those who have it available. At this stage, the Structure Plan was completed; a package of policies were identified, including road-pricing, fuel price rises, demand management measures, public transport improvements, improvements in accessibility by bicycle (a popular mode in The Netherlands). The package actually failed to suppress car-use to the degree required by the stated objectives (Bovy and Gunn, 1991 ); the imperatives of the political timetable then dictated that the Plan be released assumin9 the imposition of 'extra policy measures' which would jointly achieve the required reduction. These measures were to be sought in variant road-user-charging schemes, separate facilities for high-occupancy vehicles, subsidised carpool facilities and so on. At present, the NMS is being extended yet again to evaluate these new proposals, and in an attempt to identify the necessary policy package.
PARALLEL D E V E L O P M E N T S Above, the existing model system has been outlined, and put in the context of the prolonged period of development during which it has grown. Space precludes a fuller description, but some mention of the parallel developments around the model is appropriate. Firstly, there is the issue of the interaction between land-use and the transport system. During the course of the various National Plans, there has been examination of variants of land-use patterns provided by the agencies responsible. There has been dialogue, in the sense that forecasts of travel patterns and accessibility levels have been brought back to the land-use planners and they have been able to incorporate the information in their procedures and thinking. However, there is no explicit connection between land-use and accessibility. Valid criticism has been made of this; for example, with a high number of wealthy retired with no job ties and many workers in 'mobile' high-tech industries, would not an area-selective road-pricing scheme which focussed on the developed West and Central areas lead to re-housing in the North and East? Of course, at the margin it will but to what extent'? In the attempt to answer this sort of question, a formal link was established between the NMS and
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the land-use component of a commercially available land-use/transport package. This experiment demonstrated the feasibility of the link-up, and the availability of the necessary data, but the land-use module was unsuccessful in generating credible forecasts. Further research is now proposed, taking a more pragmatic approach. A second major parallel development is the construction of Regional versions of the NMS. Following the completion of the Structure Plan, the execution of a large proportion of the adopted policies rests with the regional authorities. Locally adapted versions of the NMS are being built to provide more sensitive forecasting tools to assist in this process. Finally, in recent years, Hague Consulting Group has used the NMS as a blueprint for models in Norway, Sweden and France as well as The Netherlands; in addition, the general approach has been tailored to apply to travel demand forecasting for fixed-link investment; models have been built for traffic across the English Channel, for traffic in the corridor between Sydney and Melbourne and for traffic across the Danish Great Belt. The general approach has proved flexible and capable of adaptation to different circumstances, accepting different component models (often based on Stated Preference data) as needed.
CONCLUSIONS The major success of the Netherlands NMS has been in being widely accepted as a reasonable basis for constructive planning over a seven year period. This level of acceptance has been achieved by demonstrating a credible scientific basis for the models, and in providing persuasive explanation for those results which were challenged. Disaggregation and the use of models compatible with rational decision-making have been the key to this, in terms of the quality of the intellectual basis for the work, in terms of the comprehensibility of the explanation for unexpected results, and in terms of avoiding intrinsically illogical results due to model misspecification (so-called 'pathological errors' ). The model is now approaching a period in which the validity of all of its components as medium/long range forecasting tools can be tested. With the data now available, a further modelling exercise is now possible. This could establish a stronger basis for some of the behavioural models within the system, to introduce time-dynamic effects into all of the models which can be improved with such treatment (not just the licence holding and car-ownership aggregate models) and to incorporate some treatment of the consequences of changing attitudes. This seems likely to become increasingly important in a population which is increasingly being exposed to pro-public transport of a sort previously used only for selling cars and fuel, and indeed for the first time to focussed anti-car-use advertising. Meanwhile, the general structure of the model is finding increasing use as an alternative to more traditional travel demand forecasting systems. Acknowledgements The work described here was funded by the Transportation and Traffic Research Division of the Ministry of Transport, Public Works and Water Management, who collaborated closely with Hague Consulting Group throughout the development of the NMS and indeed in the earlier disaggregate modelling st udies which paved the way for this application. Views expressed in this paper do not necessarily reflect those of the Transportation and Traffic Research Division. Responsibility for all errors and omissions remains with the author.
REFERENCES Baanders, A., Bovy, P. & van der Hoorn, A. (I 991 J. Substitution of travel demand. Proceedings of the PTRC Summer Annual Meeting, PTRC Education and Research Services Limited, London, UK. Ben-Akiva, M. & Lerman, S. (1985j. Discrete Choice Analysi.s. MIT Press: Cambridge, MA, USA. Bovy, P. & Gunn, H. (1991). The SVV in numbers. Vervoersplanologisch, Colloquium, Den Haag. Broecke, A. v.d. (1987). Netherlands Car-Ownership Forecasts. Den H aag: Rijkswaterstaat. Daly, A. J. & Zwam, H. v. d. (1983). Application of Disaggregate Models for a Regional Transport Study in The Netherlands. Presented to the World Conference on Transport Research, Hamburg. Daly, A.J. & Gunn, H. (1985). Cost-effective methods for national-level demand forecasting. Presented to the Conference on Behavioural Research for Transport Policy, Noordinyk, Netherlands. Daly, A. J., Gunn, H. F., Hungerink, G. & Vrolijk, P. (1990). Applications of the Netherlands National Model to support investigations of transport policy. Proceedings of thc PTRC Summer Annual Meeting. PTRC Education and Research
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Services Limited, London, UK. Gunn, H. & Pol, H. D. P. (1985). The OVD regional travel demand forecasting system. Proceedings of the PTRC Summer Annual Meeting, PTRC Education and Research Services Limited, London, UK. Gunn, H.F., Daly, A.J. & Hoorn, A.v.d. {1987). Long-range, country-wide forecasts of travel demand from models of individual choice. Conference on Travel Behaviour, Aix-en-Provence. INRETS, France. Gunn, H, F., Ben-Akiva, M. & Bradley, M. (1985). Tests of the scaling approach to transferring disaggregate travel demand models. Transportation Research Record, Washington, No. 1037. HCG/SDG (1991). Stated Preference Techniques, 2rid edn. The Hague: Hague Consulting Group; London: Steer Davies Gleave. Hensher, D.A. & Johnston, L.W. (1981). Applied Discrete Choice Modellinff. London: Croom Helm. Hungerink, G. (1989). Q-net: assignment on over-congested networks by link inflow constraint. Proceedings of the Capri Conference. Martin, B. V., Memmott, F. W. & Bone, A.J. (1961). Principles and techniques of predicting future demand for urban area transportation. Research Report number R63-1. Massachusetts Institute of Technology, Cambridge, Massachusetts. Ortuzar, J. d. D. & Willumsen, L.G. (1990). Modellino Transport. Chichester: John Wiley & Sons. Richards, M. & Ben-Akiva, M. (1975). A Disao,qreqate Travel Demand Model. Lexington, MA: D.C. Heath. Vrolijk, P., Gunn, H. F. & Hoorn, A. v. d. (1987). Waar komt de groei vandaan? Proceedings of the 14th CVS Colloquium, Den Haag.
APPENDIX:
COMPONENTS
O F T H E N M S (see Fig. 1)
As illustrated in Fig. 1, there are six main components to the system.
I. A 'prototypical' sample of households The framework for the forecasts is a sample of roughly 1000 households from an existing data source (The Netherlands National Travel Survey). Based on the characteristics of each household and its members, expansion factors are assigned so that the total expanded sample matches a number of 'target values' corresponding to the demographic/economic scenario and the forecast year of interest. This is done separately.for each o["345 zones coverinq The Netherlands.
2. A99regate projections of population 9rowth and drivin9 licence acquisition Important background trends in demographics and assumptions about economic growth are simulated into the future to project the aggregate evolution of population growth and driving licence penetration. The simulation uses the "cohort" approach, following groups of the same age and sex over time, and using models calibrated on the National Travel Survey.
3. Disayyreyate models of household drivin9 licence holdin9 Sensitivity of licence holding to policy variables is provided through the use of household-level choice models. Logit models were estimated, and are applied to predict the number of licences within each household in the artificial sample. The results can then be checked and adjusted against the aggregate licence-holding projections, which contain trend effects not captured in the cross-sectional models.
4. Disa(49reoate models of household car ownership Car ownership and usage are expected to be the key household decisions which will influence personal mobility. A model was estimated which predicts car ownership in a consistent microeconomic framework. This model is applied to each household in the sample, separately for each zone in the country, conditional on the predicted licence holding. The model is sensitive to two key control policy variables - fixed and variable car costs.
5. Disaggregate models of travel Based on purpose-designed surveys, using both cordon/screenline data and household interviews, models were cstimatcd of the frequency of making journeys for various purposes, and of the choice of mode and destination of travel (car driver, car passenger, public transport, or walk/cycle). These models are applied to each person in the prototypical sample, which at this stage is broken down into some I 132 subzones to improve the estimates of local accessibility. The models thus have access to full demographic details of the 'prototypical individuals', and of their other household members, and their geographical
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location. Further, the models can be conditioned on the predicted car ownership and licence holding. These models are sensitive to policy variables such as car and public transport times and costs (including parking, tolls, road-pricing), and to changes in land use (including the availability of space for parking, the location of employment places and education places, etc.).
6. Disay~tregate models of time-of-day choice A feature of the model system is the development of a time-of-day module sensitive to times and costs, which proves an estimate of the impact of peak-hour congestion and/or road-pricing on the usual pattern of departure times associated with different travel purposes. This model was derived from two purpose-designed Stated Preference experiments. In operation, these models are applied within an interactive loop, equilibrating time-of-day profiles with network costs by repeatedly assigning traffic, noting costs and applying the time-of-day model. This loop is itself a sub-loop of a primary feedback between the network costs and the person-level travel models.