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Disaggregate demand models—Promises and prospects
Transpn Res.-A Vol. 16A, No. 5-.6, pp. 339-344, 1982 Printed in Great Britain.
0191-2607/82/050339-03503.00/0 © 1982 Pergamon Press Ltd.
DISAGGREGATE DEMAND MODELS--PROMISES AND PROSPECTS MARTIN RICHARDS Martin and Voorhees Associates, 112 Strand, London, England Abstract--For nearly two decades it has been recognised that there are serious deficiencies in the traditional aggregate modelling approach to travel demand analysis. It was hoped that many, if not all, of these deficiencies would be overcome through the development of disaggregate models. Yet nearly 10 years after some of the first major research projects, it has been suggested that they have not yet been successfully applied in any major planning study. There can be little doubt that disaggregate model techniques do offer the scope for major improvements over aggregate models, yet there would seem to be a growing groundswell of doubt about their ability to live up to the expectations which were cultivated during the early '70s. The crucial question to most of those concerned with planning and policy development is, even if disaggregate models are not the panacea many of us hoped them to be, whether they can still improve our forecasting ability, or not. 1. INTRODUCTION port sector than in many other countries due, in part, to It is now two decades since the pioneering work of the emphasis placed by the Government on the economic Warner (1962), and Oi and Shuldiner (1962) showed that appraisal of major capital investment projects in the there were some serious shortcomings in the aggregate decision making process. As a consequence of the amtransportation models which had been developed largely bitious plans for major new transport facilities which over the preceding decade. It is nearly one decade since typified British urban planning in the Sixties and early the Williamsburg Conference, the Proceedings of which Seventies, significant advances in travel demand modelreported: ling were made relative to what was generally viewed as "the sense of excitement felt by the conference parti- the standard US approach. While some of these adcipants about the new era into which travel forecasting vances related to aggregate modelling, including Wilson's is entering. They could see emerging a stronger work on distribution and modal split models (e.g. Wilson behavioral basis for travel demand models, a et al. 1969), others used some form of disaggregate coherence and a unity of the apparently diverse direc- model, or analysis procedures. The emphasis placed on tions of current work, and a tremendous improvement economic analysis meant that there was a need to value in the practical capabilities for travel forec~isting" time savings, and most of the empirical work on value of (Brand and Manheim, 1973). time has used some form of disaggregate analysis techMany of the deficiencies of "traditional" aggregate nique (e.g. Watson, 1967). Disaggregate analysis has also models had been identified, and new hope had been been extensively used in the development of trip pinned on the use of disaggregate modelling techniques. production models, and the associated local car ownerIndeed, it could be said that disaggregate models were s h i p models. More recently, the development of the presented if not as a Utopia, certainly as a panacea. RHTM car ownership model (Bates, Gunn and Roberts, There can be no doubt that during the seventies 1978) and the subsequent work on car ownership foresignificant progress has been made on a number of casting (Martin and Voorhees Associates, 1980) made fronts. The techniques now available to analysts are extensive use of disaggregate procedures. considerably more sophisticated than they were 10 years Notwithstanding these developments, and the role of ago, and our understanding of the advantages and dis- travel demand analysis in decision-making in the transadvantages of those techniques has advanced portation sector, there seems to have been little serious significantly. Yet have the hopes which the Williamsburg interest in widening the scope of disaggregate models, Proceedings described been fully realised? If not, what and in seeking their introduction throughout the comhave we managed to achieve thus far, and where should plete demand forecasting system. This contrasts strongly we be directing our energies in the coming years? with the extensive financial support provided by In this paper, which was written as the introductory Government in the U.S. and in some European countries, paper to the Leeds Conference on Research and Ap- notably the Netherlands, for the development and applications of Disaggregate Travel Demand Models, the plication of disaggregate travel demand models. British scene is first set, since this provides necessary The seeming reluctance to commit major resources to background to some of the subsequent tenets. the replacement of established aggregate models by disaggregate models could reflect a view that despite their 2. THE BRITISH SCENE deficiencies the former are sufficiently satisfactory for Travel demand forecasting is more extensively used in the main purposes for which they are used. It could Britain in decision-making for investment in the trans- reflect serious reservations as to the ability of dis-
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aggregate models to provide economically a significant improvement. It could reflect an inadequate appreciation of either the deficiencies of aggregate models or the potential of disaggregate models. It could reflect a change in needs: the rundown in capital investment, and major transportation studies, has largely paralleled in time the development of many of the current techniques of disaggregate analysis. It could also represent a conservatism, a reluctance to risk such limited funds as are available on the new and untried. In fact, it probably reflects a combination of all of these considerations. Whatever the reason, the fact is that modelling techniques such as logit and probit have not yet made a major impact on the travel demand analysis scene in Britain. Neither have many of the quantitative social research techniques with which the American literature abounds. Fortunately, there is no reason to suspect that this is indicative of a fundamental aversion to the use of new techniques because, as we have to face up to new problems, as we have to determine the impacts of changes beyond the range of previous experience, it is important that we have the most relevant tools at our disposal. We therefore need to obtain and, moreover, justify the confidence of transportation planners in central and local government, as well as decision-makers, in the case for the development and application of new procedures.
3. SOME ISSUES OF RELEVANCE TO TIIE DECISION-MAKERS
At the Williamsburg Conference, Binder and Bouchard, between them, gave a good account of the needs of decision-makers (Brand and Manheim, 1973); the discussion on this topic was continued through three of the four of the International Conferences on Behavioural Travel Demand held thus far (Stopher and Meyburg, 1974: Hensher and Stopher, 1979: Stopher, Meyburg and Br6g, 1981). These conferences identified a wide range of characteristics, such as timeliness and cost effectiveness, to which the analyst must pay heed if his work is to be of any real value in decision-making. Despite documentation of the needs of decision-makers, there would still seem to be a serious gap between the needs and attitudes of those close to, or directly involved in, decision-making and those of what can best be described as the typical researcher. This was highlighted during discussions of the workshop concerned with policy at the 1979 (Grainau) conference (Richards, 1981). While this is by no means a new problem, it does seem to be one which is serving to limit the application of new techniques of travel demand analysis; and one which we should be working hard to overcome. In doin.g this we must recognise it as a two-way problem. If we want to properly influence decision-makers, we must demonstrate clearly that we understand and appreciate the environment within which they operate. Easy though it is to say this, the evidence is that it is exceedingly difficult to achieve. Part of this problem is a problem of communication; a problem not helped by the use of jargon. As the variety of disciplines involved in transportation planning has
widened, so the problems of communication both within the profession and with its customers have increased. The particular field of travel demand analysis is no exception. Most of those concerned with the development and appraisal of plans and policies are civil engineers and economists with little formal training in mathematics, statistics or psychology. The development of new techniques of travel demand analysis has, however, been spearheaded by people expert in these skills. The use of jargon familiar to people with such expertise but foreign to others and unreasonable assumptions about the datum level of understanding, both serve to alienate rather than produce the advance sought. Specialists must learn to recognise this more fully than they appear to at present. Another aspect of the problem of communication is professional honesty, or openness. Demand analysis and forecasting are not, and are unlikely to become, exact sciences. Our data, let alone the completeness of our theories, prevent that. Yet results are too often presented as though they were the product of an exact science, and discussions about accuracy are carefully avoided. It would seem far preferable to openly acknowledge the uncertainties involved in our work, while at the same time devising ways to enable decision-makers to make better, more effective use of results. Fortunately, encouraged by the Leitch Report (1978) which advised "(The Department) should never put itself in the position of appearing to defend a single figure as if it were uniquely correct", practice in Britain is moving towards greater frankness. One advantage of this approach is that it encourages analysts to look more carefully at each element of the total system, and thus to appreciate those elements which have a major impact on the value of the results, and those which are less crucial. One of the requirements of an analysis or forecasting process is that information is provided which is pertinent to the decision-makers needs. There would seem to be little virtue in developing "Bogit", the ultimate in disaggregate demand models, if we still fail to satisfy the decision-makers' requirements with regard to the provision of information, however unreasonable we, as professional analysts, may consider those requirements to be. Yet many towards the decision-making end of the spectrum would say that over the last decade too much emphasis has been placed on striving to achieve Bogit, the ultimate, and in selling the theoretical capabilities of both Bogit and its antecedents and not enough on providing immediate, useful and reliable improvements to current methodology, albeit with some as yet unresolved theoretical deficiencies. Here we clearly have a conflict. On the one hand we have a considerable economic and political investment in and commitment to, established techniques and on the other hand we have the belief that it is possible to replace those techniques by others which are significantly superior in many key aspects. Whether we like it or not, we have to recognise that a crucial consideration here is whether the introduction of the "new" would raise serious questions with regard to the validity of earlier decisions. Thus for those with an established commitment to the "old", it may be far
Disaggregatedemand models--promises and prospects preferable to seek incremental improvements to current procedures, than to abandon those in favour of something totally different. Certainly, the risks of "failure" are very much less, but so are the possibilities of major improvements. Nevertheless, unless we are prepared to risk investment in the development of totally new techniques we are most unlikely to overcome some of the key deficiencies inherent in the "old". Unfortunately, it would seem that the capabilities of disaggregate models have been unnecessarily oversold. The hope held out at Williamsburg that "Significant improvements in travel forecasting capabilities can be achieved within a period of 3 years and, in some respects, even 1 year", (Brand and Manheim, 1973) has proved optimistic and those who wish to can point at a number of "failures", sufficient to justify to themselves and others any decision to avoid the use of disaggregate models. Failure in technical development generally serves to set the development programme back significantly, due to the time required to overcome the cause of the failure and, more important in relation to decision-making, to rebuild the confidence of the customers--the planners and decision-makers. At the end of the day the product may be vastly improved, but by then many opportunities may have been lost; the history of the de Havilland Comet provides a most effective illustration of this. Thus we must strive to consolidate progress as it is made, to avoid seeking to make advances greater than the system can properly assimilate and, most particularly, to avoid promising more than we can be reasonably sure of achieving. Confronted with a choice between an imperfect but seemingly reliable approach and another which, while potentially much better, has not yet stood the test of time, the decision-maker is more likely to opt for the first. The terms of reference for the development of the Department of Transport's Regional Highway Traffic Model illustrate this. Given the timescale originally set for the development of the model, (a national traffic model, to be used in .the appraisal and design of major inter-urban highway schemes) the Department decided that it should be based on the use of "proven techniques" (Alastair Dick and Associates, 1976) and this view was shared by the Leitch Committee (Leitch, 1978). In fact, the irony of this is that what is generally regarded as the most successful part of the whole model, the car ownership model, was based on the use of largely innovative techniques, while the distribution model, for which largely standard techniques were used, has never been satisfactorily calibrated (Leitch, 1980). So failure tends to breed conservatism and caution, retrenchment to that which you know, warts and all. If we believe those warts to be serious and damaging, ihen we must also recognise the need to progress carefully, not utilising new techniques or major developments of existing techniques on large studies, or studies in the political eye, until we can be reasonably assured of success. If your starting point is essentially one of reasoned cynicism, as is probably largely the case in the tit is possible that I too have been guilty of this!
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UK, then failure is more damaging than if the environment is more positive., 4. ~
RECORD TO DATE
The deficiencies of the conventional multi-stage aggregate transportation model have been well documented, and there is no need here to repeat those. Many, if not all, of these deficiencies are widely recognised within the planning profession and there is no doubt that most planners would willingly adopt alternative techniques if they could be reasonably assured that, in putting aside one set of problems, they are not adopting a different but an equally troublesome approach. The record to date would seem to suggest that planners serving local or central government have not yet been convinced that disaggregate models do offer them a better alternative for most of their demand forecasting needs. This is, in part, a question of availability of adequately proven techniques and in part one of education and training. Over recent years, not enough emphasis has been put on consolidating the fruits of earlier work to provide practising planners with some reasonably robust techniques, nor to providing the training necessary to ensure the proper use of those techniques. It is most unfortunate that the terms "disaggregate" and "behavioural" have come to be used by many who should know better as synonymst, because they are not. A disaggregate model can be as deficient in its behavioural basis as any aggregate model; it has no inherent property which ensures that it is behavioural. In fact, few, if any, of the published disaggregate travel demand models can be properly classified as 'behavioural'. Very many of them use essentially the same variables as used, and measured, in their aggregate counterparts. Thus, for example, for mode choice they use service variables like time, distance and cost; socioeconomic factors like car-ownership and income. They assume that travellers make choices which maximise their utility, that they have full information on all alternatives, and that they make a decision independent of past decisions and experience each time they make a trip. But models like these are not behavioural, even though they can often be used to replicate aggregated movements fairly accurately (not a particularly demanding criterion). Thus we need to distinguish between models of individual behaviour in which we endeavour to identify and replicate the actual decision process in making a travel choice, and those in which we endeavour to simply synthesise the results of that process. It is only more recently that attention has I~een turned seriously towards ' the former, given that disaggregate travel demand models based on the theory of utility maximisation and using a Logit or Probit type model formulation are of the second type. Models of the latter type cannot be expected to provide accurate forecasts over a wide range of circumstances. Indeed, there are many cases in which factors which have been shown to influence travel decisions do not satisfactorily enter the specification; travel cost is one of the best documented of these.
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One of the other deficiencies with much of the work to date relates to the measurement of the variables. Aggregate models use aggregate variables and it is widely supposed that disaggregate models use disaggregate variables, or "individual data", but that is not always so, not by any means. There are, for example, many published studies in which some level of data aggregation has been used. This would seem to be particularly common with level of service data, which are frequently acquired from zonal networks. There may be good reasons for doing this, but we need to recognise that when this is done a significant part of the positive attributes of disaggregate models is foregone. One of the early claims for disaggregate models was that because of the limited number of observations required, the greater appropriateness of their structure and the use of more efficient estimation procedures, it would be possible to develop models more quickly and economically than had previously been the case. Regrettably there would seem to be insufficient evidence to suggest the timing claim has yet been substantiated. One of the reasons put forward is that the preparation of good individual data calls for quality standards significantly higher than those previously considered adequate for aggregate models. There is a lot of truth in this argument; and there should be little doubt that data standards in transportation studies have generally been too lax. But making allowance for this, there would not seem to be any convincing evidence which would suggest that any of the Williamsburg promises of "more timely, valid and useful results at lower cost" (Brand and Manheim, 1973) are likely to be achieved as quickly as was hoped, if, indeed, they will ever be achieved in the context of model development for large scale general planning studies. The experiences of San Francisco, Amsterdam and most recently, the "Zuid Vleugel" study in the Netherlands certainly offer little evidence to the contrary. In saying that, there is no intention to castigate or attribute blame; the purpose here is simply to state that problems, of whatever nature and for whatever reason, appear to have arisen in most, if not all, of the attempts to use disaggregate techniques to develop large scale comprehensive transportation demand models and that these have served to prevent those studies fully satisfying the original expectations. Clearly, there are still major problems with disaggregate destination choice models and there is little evidence to date of early prospects of major improvements to the conventional household or person based trip end models. Another difficult area is that of assignment. If disaggregate demand modelling techniques used in the context of large scale comprehensive models have yet to fulfil early expectations, is this also true when they are applied in more limited circumstances? The answer to this must be "no". The problems of obtaining appropriate data of choice set definition and other issues which appear to seriously affect large scale modelling projects, can be more readily reduced, if not overcome, when dealing with more specialised problems and there are many examples of adequate models being developed for such circumstances; certainly, better models than
could have been readily developed using conventional aggregate approaches. In fact, it would seem reasonable to postulate that the state-of-the-art in disaggregate demand modelling is much more appropriate for use in specific, problem related, models than in broad brush general planning models. Models of mode choice and car ownership are two applications in which disaggregate modelling techniques have proved successful. It is in the context of specific, limited, applications such as these that the Williamsburg promises are closer to fulfilment. Although current disaggregate models are rarely truly behavioural, there is a very real need to develop models which are behavioural, and there would appear to be a growing appreciation in decision-making circles that appropriate information of relevance to many of the issues of current concern cannot be provided through the use of conventional demand models, be they aggregate or disaggregate. This was certainly the view of the workshop on policy issues at the Grainau Conference (Richards, 1981). However, before we can formalise measurement of demand in a mathematical models, we need to improve our theories of travel behaviour, particularly those relating to responses to major change in the system; this too was a conclusion of the Grainau Conference. Fortunately, our understanding of travel behaviour is improving and much useful work has been done over recent years. There is also a growing awareness of the issues which have to be resolved before we can expect to have an adequate set of reasonably robust theories. But we have not yet achieved the "substantial improvement in the understanding of travel behaviour" which the Williamsburg Conference considered "essential to improving the accuracy and validity of forecasting methods" and "research is (still) needed into how consumers perceive travel opportunities, particularly the attributes they consider and their choice processes". (Brand and Manheim, 1973.) Thus, 8 years after Williamsburg, a reasonable conclusion is that despite the availability of considerable resources and the dedicated commitment of some excellent researchers, the hopes and promises described at that Conference have not yet been achieved and failure to live up to promises, however good the reasons for those failures may be, adversely affects credibility. That is most unfortunate, because we do urgently need improved techniques of travel demand forecasting and anything which lowers the confidence of decision-makers in the ability of the research community to develop appropriate procedures is damaging to the profession as a whole. 5. WHERENEXT?
In the coming decade, we need to emphasise the development of techniques which can be readily used by planners to evaluate the consequences of changes in the quality of the transportation system and the cost of services, many of which are outside the range of previous experience. We need to develop techniques which can be applied within the resource restraints which apply to political decision-making, so that decision-makers arrive at their conclusions on the basis of as good information as it is reasonably possible to provide. We
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also need to improve our understanding of travel improve our understanding of behaviour, we could also behaviour, and to develop robust theories of travel improve those "conventional demand models". Take, for behaviour, on which to base the development of really example, the simple level of service variable time, There behavioural demand models. These should be the topics are at least four different definitions of time of relevance to which research and development resources should be to travel demand studies. There is the "perceived" time directed in the next few years. on which the traveller's decision is made. Secondly there On the modelling side we now have an impressive is the "reported" time, which is the information usually array of techniques, an array which is bewildering and " elicited in a survey; this is not necessarily the same as confusing to the average transportation planner. What is the "perceived" time and could well be very different needed is a planner's guide to available disaggregate from it, although in many cases the two are unjustifiably modelling techniques which clearly and concisely des- assumed to be the same. Thirdly, there is the "actual" cribes the characteristics and capabilities (including time and here we have the complication that there is known and expected limitations) of each technique, rarely a unique value of the actual travel time between provides information on how and for what each tech- two points, due to variations in the system. The "actual" nique can (or should) be applied and, ideally, should time for any particular trip is therefore drawn from a include illustrative case-studies. There are, of course, distribution, which is probably skewed to the right. many problems and disadvantages associated with the Finally, we have the "network" time; this is the time we preparation of such a manual. Not least of these is the extract from our abstracted networks and is the only danger of providing misleading information through the value we normally have for the future situation for which need to simplify and to present in relatively lay terms, demand forecasts are required. Thus, the only two values material which is inherently fairly sophisticated. Yet of real relevance to demand forecasting are the "perunless there is comprehensive "state-of-the-art" docu- ceived" and "network" times, and we can only directly mentation, we will continue to fail to take full ad- quantify the latter. If we understood travel behaviour vantage of the very real accomplishments to date. The more fully than we do at present, we could improve our greatest problem, however, is in finding authors who mapping of the one with respect to the other and we have sufficient understanding of the needs and abilities could thus undoubtedly improve conventional demand of, typically, county (or state) planners as well the models, in terms of both their specification and the theories underlying recent research and. development quality of the data used for their estimation. work. Improving our understanding of travel behaviour is One major aspect of demand modelling to which there desirable if we are to improve the quality of our foreis a need to devote considerably more attention is that of casts of the effects of marginal changes in the system. data, a point already referred to. We generally accord far We know for instance that there are lags in response to too little attention to data collection, paying scant atten- system changes and that these are not reflected by contion to the quality of the information on which our ventional models. We also know that behaviour is not seemingly sophisticated models are based. In particular solely influenced by the "explanatory" variables norwe concern ourselves much too little with quality control mally used; but by far the greatest need for an improved throughout the data collection and processing phases; far understanding of behaviour relates to the estimation of too many data collection agencies are selected on the responses to major changes in the system which serve to basis of price rather than ability to achieve the requisite significantly extend or restrict traveller's choices; quality standards. Indeed, it is likely that many of the "threshold changes" which cause a major re-appraisal of problems encountered in model development can be travel related decisions. If we are to improve our underattributed to data errors rather than deficiencies in the standing of behaviour, and thus responses to major model specification or estimation procedures. Thus, there change, we need to exploit to the fullest extent possible is a real need to take a long hard look at data collection, the research opportunities provided by any such with a view to improving the relevance, the quality and changes. However, such changes are not a common • the cost effectiveness of data collection for travel occurrence, and therefore we must develop techniques demand studies. In doing this we need to address the which enable us to estimate the effects of threshold problems of maintaining quality standards in the field, changes, possibly by studying a series of marginal recognising that the needs of the transportation analyst changes some of which may, in fact, represent a are very different to the needs of the product or market- "threshold" change to specific individuals. ing manager, the main client for most market research 6. CONCLUSION agencies. Hopefully the growing political sensitivity to survey work, an attitude which those of us who have a The statement made in the proceedings of the Wilgenuine need for data must find disturbing, may at least liamsburg Conference that "Improvements in travel lead to greater attention to quality. forecasting methods are urgently required to supply Improving the relevance of data collection is necessary more effectively the information required for pressing whether we utilise conventional demand models, models transport decisions at all levels of government" (Brand which relate demand largely to level of service data and and Manheim, 1973) is as true today as it was in 1972. other simple characteristics of the system and the Equally valid is the statement "present procedures need traveller, be they aggregate or disaggregate, or true simplification or streamlining to improve their ability, behaviourat analysis techniques. Indeed, if we were to their policy sensitivity and their validity". Indeed one
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can go on through the conclusions of that conference and find that many of the statements are just as applicable in 1980 as they were in 1972. So, after a decade of research and development in disaggregate travel model analysis, can we share the optimism voiced at Williamsburg? The answer must be a cautiously qualified "yes". The deficiencies of aggregate models have not lessened with the passage of time, although there has been an improvement in the intelligence with which they are used which serves to reduce the effects of some of their deficiencies. During the same period we have come to realise that disaggregate models also have their problems and they do not yet offer a full substitute to aggregate models for large scale general planning studies, although they can be utilised for more limited applications. We have also increasingly come to appreciate the inadequacies of our understanding of travel behaviour and the theories on which our demand models, aggregate or disaggregate, are based. The highest research priority today is to improve those theories of behaviour, and we can only do this by studying individual behaviour, by using disaggregate analysis techniques. So disaggregate analysis can improve our forecasting abilities. The use of the word "analysis" here rather than "model" is important because the term "model" is often viewed in a narrow sense, Logit or Probit, rather than the wider sense of a formalised representation of a phenomenon. If we take the narrower sense, then there can be little doubt that under specific circumstances disaggregate models can improve our forecasting ability, though care and intelligence is required in their application, as it is with aggregate models. Provided we exercise that care and have the intelligence, there are very many situations in which the use of disaggregate models can bring about significant improvements in the timeliness, the cost-effectiveness and the accuracy of some travel demand forecasts, specifically those concerned with well defined choice situations in which the major explanatory variables can be identified and measured. However, we must direct our efforts more towards providing planners with a set of procedures which they can understand and apply so that they can make proper use of disaggregate models in appropriate circumstances. Research and development priorities for the 1980's should be in two main directions. The first is to consolidate and make more widely available existing modelling technology. The second is to improve our understanding of travel behaviour and from that to develop demand forecasting techniques appropriate for evaluating major, or "threshold", changes in the total transportation related system. It cannot be good use of in-
creasingly scarce research resources to direct a significant share of these resources to the mathematical, or econometric, aspects of model specification and estimation unless further work is found necessary to support the behavioural research. A third aspect needing attention is that of communications. Communications and understanding, between the research community, practising planners and decision-makers (bureaucrats and politicians) must be improved, if research is to fulfil its rightful role in the socially and economically important field of transport. Acknowledgements--In preparing this paper, I generally chose to avoid mention of specific pieces of work because I felt it insidious to single out individualpieces of work for comment when my own view of this developingfield is limited and when some of my information is based on hearsay than documentation. Nevertheless, it would be wrong not to acknowledge that there is a large and honourable body of work from which I have drawn my own conclusions; clearly it would be surprising if all the contributors to that work were to share all of my conclusions.
REFERENCES Alastair Dick and Associates (1976). Regional Highway Traffic Model: Project Report. Department of the Environment, London. Bates J., Gunn H. and Roberts M. 0978) A Disaggregate Model of Household Car Ownership. Department of Transport Res. Rep. 20, HMSO, London. Brand D. and Manheim M. L. (eds) (1973) Urban Travel Demand Forecasting. Spec. Rep. 143, High. Res. Board, Washington D.C. Hensher D. A. and Stopher P. R. (eds.) (1979) Behavioural Travel Modelling. Croom Helm, London. Leitch Sir George (1978) Report of the Advisory Committee on Trunk Road Assessment. HMSO, London. Leitch Sir George (1980) Forecasting Tragic on Trunk Roads: A Report on the Regional Highway Traffic Model. HMSO, London. Martin and Voorhees Associates (1980) Car Ownership Research Project: Final Report. Department of Transport, London. Oi, K. I. Y. and Shuldiner P. W. (1%2) An Analysis of Urban Travel Demands. Northwestern University Press, Evanston, Ill. Richards, M. G. (1981) Issues in Policy Sensitivity of Behavioural Models, In (Edited by P. R. Stopher, A. H. Meyburg and W. Br6g) New Horizons in Travel Behaviour Research. D.C. Heath, Lexington, Mass. Stopher P. R. and Meyburg A. H. (eds.) (1974) Behavioural Demand Modelling and Valuation of Travel Time. Spec. Rep. 149, Transpn Res. Board, Washington 13(2. Stopher P. R., Meyburg A. H. and Br6g, W. (eds.) (1981) New Horizons in TraveI-Behaviour Research. D.C. Heath, Lexington, Mass. Warner S. L. (1%2) Strategic Choice of Mode in Urban Travel: A Study in Binary Choice. Northwestern University Press, Evanston, Ill. Watson P. L. (1974) The Value of Time; Behavioural Models of Mode Choice. D.C. Heath, Lexington, Mass. Wilson A. G., Hawkins A. F, Hill G. J. and Wagon D. J. (1%9) Calibration and Testing the SELNEC Transport Model. Reg. Stud., 3 (3), 337-350.
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DISAGGREGATE DEMAND MODELS--PROMISES AND PROSPECTS MARTIN RICHARDS Martin and Voorhees Associates, 112 Strand, London, England Abstract--For nearly two decades it has been recognised that there are serious deficiencies in the traditional aggregate modelling approach to travel demand analysis. It was hoped that many, if not all, of these deficiencies would be overcome through the development of disaggregate models. Yet nearly 10 years after some of the first major research projects, it has been suggested that they have not yet been successfully applied in any major planning study. There can be little doubt that disaggregate model techniques do offer the scope for major improvements over aggregate models, yet there would seem to be a growing groundswell of doubt about their ability to live up to the expectations which were cultivated during the early '70s. The crucial question to most of those concerned with planning and policy development is, even if disaggregate models are not the panacea many of us hoped them to be, whether they can still improve our forecasting ability, or not. 1. INTRODUCTION port sector than in many other countries due, in part, to It is now two decades since the pioneering work of the emphasis placed by the Government on the economic Warner (1962), and Oi and Shuldiner (1962) showed that appraisal of major capital investment projects in the there were some serious shortcomings in the aggregate decision making process. As a consequence of the amtransportation models which had been developed largely bitious plans for major new transport facilities which over the preceding decade. It is nearly one decade since typified British urban planning in the Sixties and early the Williamsburg Conference, the Proceedings of which Seventies, significant advances in travel demand modelreported: ling were made relative to what was generally viewed as "the sense of excitement felt by the conference parti- the standard US approach. While some of these adcipants about the new era into which travel forecasting vances related to aggregate modelling, including Wilson's is entering. They could see emerging a stronger work on distribution and modal split models (e.g. Wilson behavioral basis for travel demand models, a et al. 1969), others used some form of disaggregate coherence and a unity of the apparently diverse direc- model, or analysis procedures. The emphasis placed on tions of current work, and a tremendous improvement economic analysis meant that there was a need to value in the practical capabilities for travel forec~isting" time savings, and most of the empirical work on value of (Brand and Manheim, 1973). time has used some form of disaggregate analysis techMany of the deficiencies of "traditional" aggregate nique (e.g. Watson, 1967). Disaggregate analysis has also models had been identified, and new hope had been been extensively used in the development of trip pinned on the use of disaggregate modelling techniques. production models, and the associated local car ownerIndeed, it could be said that disaggregate models were s h i p models. More recently, the development of the presented if not as a Utopia, certainly as a panacea. RHTM car ownership model (Bates, Gunn and Roberts, There can be no doubt that during the seventies 1978) and the subsequent work on car ownership foresignificant progress has been made on a number of casting (Martin and Voorhees Associates, 1980) made fronts. The techniques now available to analysts are extensive use of disaggregate procedures. considerably more sophisticated than they were 10 years Notwithstanding these developments, and the role of ago, and our understanding of the advantages and dis- travel demand analysis in decision-making in the transadvantages of those techniques has advanced portation sector, there seems to have been little serious significantly. Yet have the hopes which the Williamsburg interest in widening the scope of disaggregate models, Proceedings described been fully realised? If not, what and in seeking their introduction throughout the comhave we managed to achieve thus far, and where should plete demand forecasting system. This contrasts strongly we be directing our energies in the coming years? with the extensive financial support provided by In this paper, which was written as the introductory Government in the U.S. and in some European countries, paper to the Leeds Conference on Research and Ap- notably the Netherlands, for the development and applications of Disaggregate Travel Demand Models, the plication of disaggregate travel demand models. British scene is first set, since this provides necessary The seeming reluctance to commit major resources to background to some of the subsequent tenets. the replacement of established aggregate models by disaggregate models could reflect a view that despite their 2. THE BRITISH SCENE deficiencies the former are sufficiently satisfactory for Travel demand forecasting is more extensively used in the main purposes for which they are used. It could Britain in decision-making for investment in the trans- reflect serious reservations as to the ability of dis-
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aggregate models to provide economically a significant improvement. It could reflect an inadequate appreciation of either the deficiencies of aggregate models or the potential of disaggregate models. It could reflect a change in needs: the rundown in capital investment, and major transportation studies, has largely paralleled in time the development of many of the current techniques of disaggregate analysis. It could also represent a conservatism, a reluctance to risk such limited funds as are available on the new and untried. In fact, it probably reflects a combination of all of these considerations. Whatever the reason, the fact is that modelling techniques such as logit and probit have not yet made a major impact on the travel demand analysis scene in Britain. Neither have many of the quantitative social research techniques with which the American literature abounds. Fortunately, there is no reason to suspect that this is indicative of a fundamental aversion to the use of new techniques because, as we have to face up to new problems, as we have to determine the impacts of changes beyond the range of previous experience, it is important that we have the most relevant tools at our disposal. We therefore need to obtain and, moreover, justify the confidence of transportation planners in central and local government, as well as decision-makers, in the case for the development and application of new procedures.
3. SOME ISSUES OF RELEVANCE TO TIIE DECISION-MAKERS
At the Williamsburg Conference, Binder and Bouchard, between them, gave a good account of the needs of decision-makers (Brand and Manheim, 1973); the discussion on this topic was continued through three of the four of the International Conferences on Behavioural Travel Demand held thus far (Stopher and Meyburg, 1974: Hensher and Stopher, 1979: Stopher, Meyburg and Br6g, 1981). These conferences identified a wide range of characteristics, such as timeliness and cost effectiveness, to which the analyst must pay heed if his work is to be of any real value in decision-making. Despite documentation of the needs of decision-makers, there would still seem to be a serious gap between the needs and attitudes of those close to, or directly involved in, decision-making and those of what can best be described as the typical researcher. This was highlighted during discussions of the workshop concerned with policy at the 1979 (Grainau) conference (Richards, 1981). While this is by no means a new problem, it does seem to be one which is serving to limit the application of new techniques of travel demand analysis; and one which we should be working hard to overcome. In doin.g this we must recognise it as a two-way problem. If we want to properly influence decision-makers, we must demonstrate clearly that we understand and appreciate the environment within which they operate. Easy though it is to say this, the evidence is that it is exceedingly difficult to achieve. Part of this problem is a problem of communication; a problem not helped by the use of jargon. As the variety of disciplines involved in transportation planning has
widened, so the problems of communication both within the profession and with its customers have increased. The particular field of travel demand analysis is no exception. Most of those concerned with the development and appraisal of plans and policies are civil engineers and economists with little formal training in mathematics, statistics or psychology. The development of new techniques of travel demand analysis has, however, been spearheaded by people expert in these skills. The use of jargon familiar to people with such expertise but foreign to others and unreasonable assumptions about the datum level of understanding, both serve to alienate rather than produce the advance sought. Specialists must learn to recognise this more fully than they appear to at present. Another aspect of the problem of communication is professional honesty, or openness. Demand analysis and forecasting are not, and are unlikely to become, exact sciences. Our data, let alone the completeness of our theories, prevent that. Yet results are too often presented as though they were the product of an exact science, and discussions about accuracy are carefully avoided. It would seem far preferable to openly acknowledge the uncertainties involved in our work, while at the same time devising ways to enable decision-makers to make better, more effective use of results. Fortunately, encouraged by the Leitch Report (1978) which advised "(The Department) should never put itself in the position of appearing to defend a single figure as if it were uniquely correct", practice in Britain is moving towards greater frankness. One advantage of this approach is that it encourages analysts to look more carefully at each element of the total system, and thus to appreciate those elements which have a major impact on the value of the results, and those which are less crucial. One of the requirements of an analysis or forecasting process is that information is provided which is pertinent to the decision-makers needs. There would seem to be little virtue in developing "Bogit", the ultimate in disaggregate demand models, if we still fail to satisfy the decision-makers' requirements with regard to the provision of information, however unreasonable we, as professional analysts, may consider those requirements to be. Yet many towards the decision-making end of the spectrum would say that over the last decade too much emphasis has been placed on striving to achieve Bogit, the ultimate, and in selling the theoretical capabilities of both Bogit and its antecedents and not enough on providing immediate, useful and reliable improvements to current methodology, albeit with some as yet unresolved theoretical deficiencies. Here we clearly have a conflict. On the one hand we have a considerable economic and political investment in and commitment to, established techniques and on the other hand we have the belief that it is possible to replace those techniques by others which are significantly superior in many key aspects. Whether we like it or not, we have to recognise that a crucial consideration here is whether the introduction of the "new" would raise serious questions with regard to the validity of earlier decisions. Thus for those with an established commitment to the "old", it may be far
Disaggregatedemand models--promises and prospects preferable to seek incremental improvements to current procedures, than to abandon those in favour of something totally different. Certainly, the risks of "failure" are very much less, but so are the possibilities of major improvements. Nevertheless, unless we are prepared to risk investment in the development of totally new techniques we are most unlikely to overcome some of the key deficiencies inherent in the "old". Unfortunately, it would seem that the capabilities of disaggregate models have been unnecessarily oversold. The hope held out at Williamsburg that "Significant improvements in travel forecasting capabilities can be achieved within a period of 3 years and, in some respects, even 1 year", (Brand and Manheim, 1973) has proved optimistic and those who wish to can point at a number of "failures", sufficient to justify to themselves and others any decision to avoid the use of disaggregate models. Failure in technical development generally serves to set the development programme back significantly, due to the time required to overcome the cause of the failure and, more important in relation to decision-making, to rebuild the confidence of the customers--the planners and decision-makers. At the end of the day the product may be vastly improved, but by then many opportunities may have been lost; the history of the de Havilland Comet provides a most effective illustration of this. Thus we must strive to consolidate progress as it is made, to avoid seeking to make advances greater than the system can properly assimilate and, most particularly, to avoid promising more than we can be reasonably sure of achieving. Confronted with a choice between an imperfect but seemingly reliable approach and another which, while potentially much better, has not yet stood the test of time, the decision-maker is more likely to opt for the first. The terms of reference for the development of the Department of Transport's Regional Highway Traffic Model illustrate this. Given the timescale originally set for the development of the model, (a national traffic model, to be used in .the appraisal and design of major inter-urban highway schemes) the Department decided that it should be based on the use of "proven techniques" (Alastair Dick and Associates, 1976) and this view was shared by the Leitch Committee (Leitch, 1978). In fact, the irony of this is that what is generally regarded as the most successful part of the whole model, the car ownership model, was based on the use of largely innovative techniques, while the distribution model, for which largely standard techniques were used, has never been satisfactorily calibrated (Leitch, 1980). So failure tends to breed conservatism and caution, retrenchment to that which you know, warts and all. If we believe those warts to be serious and damaging, ihen we must also recognise the need to progress carefully, not utilising new techniques or major developments of existing techniques on large studies, or studies in the political eye, until we can be reasonably assured of success. If your starting point is essentially one of reasoned cynicism, as is probably largely the case in the tit is possible that I too have been guilty of this!
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UK, then failure is more damaging than if the environment is more positive., 4. ~
RECORD TO DATE
The deficiencies of the conventional multi-stage aggregate transportation model have been well documented, and there is no need here to repeat those. Many, if not all, of these deficiencies are widely recognised within the planning profession and there is no doubt that most planners would willingly adopt alternative techniques if they could be reasonably assured that, in putting aside one set of problems, they are not adopting a different but an equally troublesome approach. The record to date would seem to suggest that planners serving local or central government have not yet been convinced that disaggregate models do offer them a better alternative for most of their demand forecasting needs. This is, in part, a question of availability of adequately proven techniques and in part one of education and training. Over recent years, not enough emphasis has been put on consolidating the fruits of earlier work to provide practising planners with some reasonably robust techniques, nor to providing the training necessary to ensure the proper use of those techniques. It is most unfortunate that the terms "disaggregate" and "behavioural" have come to be used by many who should know better as synonymst, because they are not. A disaggregate model can be as deficient in its behavioural basis as any aggregate model; it has no inherent property which ensures that it is behavioural. In fact, few, if any, of the published disaggregate travel demand models can be properly classified as 'behavioural'. Very many of them use essentially the same variables as used, and measured, in their aggregate counterparts. Thus, for example, for mode choice they use service variables like time, distance and cost; socioeconomic factors like car-ownership and income. They assume that travellers make choices which maximise their utility, that they have full information on all alternatives, and that they make a decision independent of past decisions and experience each time they make a trip. But models like these are not behavioural, even though they can often be used to replicate aggregated movements fairly accurately (not a particularly demanding criterion). Thus we need to distinguish between models of individual behaviour in which we endeavour to identify and replicate the actual decision process in making a travel choice, and those in which we endeavour to simply synthesise the results of that process. It is only more recently that attention has I~een turned seriously towards ' the former, given that disaggregate travel demand models based on the theory of utility maximisation and using a Logit or Probit type model formulation are of the second type. Models of the latter type cannot be expected to provide accurate forecasts over a wide range of circumstances. Indeed, there are many cases in which factors which have been shown to influence travel decisions do not satisfactorily enter the specification; travel cost is one of the best documented of these.
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One of the other deficiencies with much of the work to date relates to the measurement of the variables. Aggregate models use aggregate variables and it is widely supposed that disaggregate models use disaggregate variables, or "individual data", but that is not always so, not by any means. There are, for example, many published studies in which some level of data aggregation has been used. This would seem to be particularly common with level of service data, which are frequently acquired from zonal networks. There may be good reasons for doing this, but we need to recognise that when this is done a significant part of the positive attributes of disaggregate models is foregone. One of the early claims for disaggregate models was that because of the limited number of observations required, the greater appropriateness of their structure and the use of more efficient estimation procedures, it would be possible to develop models more quickly and economically than had previously been the case. Regrettably there would seem to be insufficient evidence to suggest the timing claim has yet been substantiated. One of the reasons put forward is that the preparation of good individual data calls for quality standards significantly higher than those previously considered adequate for aggregate models. There is a lot of truth in this argument; and there should be little doubt that data standards in transportation studies have generally been too lax. But making allowance for this, there would not seem to be any convincing evidence which would suggest that any of the Williamsburg promises of "more timely, valid and useful results at lower cost" (Brand and Manheim, 1973) are likely to be achieved as quickly as was hoped, if, indeed, they will ever be achieved in the context of model development for large scale general planning studies. The experiences of San Francisco, Amsterdam and most recently, the "Zuid Vleugel" study in the Netherlands certainly offer little evidence to the contrary. In saying that, there is no intention to castigate or attribute blame; the purpose here is simply to state that problems, of whatever nature and for whatever reason, appear to have arisen in most, if not all, of the attempts to use disaggregate techniques to develop large scale comprehensive transportation demand models and that these have served to prevent those studies fully satisfying the original expectations. Clearly, there are still major problems with disaggregate destination choice models and there is little evidence to date of early prospects of major improvements to the conventional household or person based trip end models. Another difficult area is that of assignment. If disaggregate demand modelling techniques used in the context of large scale comprehensive models have yet to fulfil early expectations, is this also true when they are applied in more limited circumstances? The answer to this must be "no". The problems of obtaining appropriate data of choice set definition and other issues which appear to seriously affect large scale modelling projects, can be more readily reduced, if not overcome, when dealing with more specialised problems and there are many examples of adequate models being developed for such circumstances; certainly, better models than
could have been readily developed using conventional aggregate approaches. In fact, it would seem reasonable to postulate that the state-of-the-art in disaggregate demand modelling is much more appropriate for use in specific, problem related, models than in broad brush general planning models. Models of mode choice and car ownership are two applications in which disaggregate modelling techniques have proved successful. It is in the context of specific, limited, applications such as these that the Williamsburg promises are closer to fulfilment. Although current disaggregate models are rarely truly behavioural, there is a very real need to develop models which are behavioural, and there would appear to be a growing appreciation in decision-making circles that appropriate information of relevance to many of the issues of current concern cannot be provided through the use of conventional demand models, be they aggregate or disaggregate. This was certainly the view of the workshop on policy issues at the Grainau Conference (Richards, 1981). However, before we can formalise measurement of demand in a mathematical models, we need to improve our theories of travel behaviour, particularly those relating to responses to major change in the system; this too was a conclusion of the Grainau Conference. Fortunately, our understanding of travel behaviour is improving and much useful work has been done over recent years. There is also a growing awareness of the issues which have to be resolved before we can expect to have an adequate set of reasonably robust theories. But we have not yet achieved the "substantial improvement in the understanding of travel behaviour" which the Williamsburg Conference considered "essential to improving the accuracy and validity of forecasting methods" and "research is (still) needed into how consumers perceive travel opportunities, particularly the attributes they consider and their choice processes". (Brand and Manheim, 1973.) Thus, 8 years after Williamsburg, a reasonable conclusion is that despite the availability of considerable resources and the dedicated commitment of some excellent researchers, the hopes and promises described at that Conference have not yet been achieved and failure to live up to promises, however good the reasons for those failures may be, adversely affects credibility. That is most unfortunate, because we do urgently need improved techniques of travel demand forecasting and anything which lowers the confidence of decision-makers in the ability of the research community to develop appropriate procedures is damaging to the profession as a whole. 5. WHERENEXT?
In the coming decade, we need to emphasise the development of techniques which can be readily used by planners to evaluate the consequences of changes in the quality of the transportation system and the cost of services, many of which are outside the range of previous experience. We need to develop techniques which can be applied within the resource restraints which apply to political decision-making, so that decision-makers arrive at their conclusions on the basis of as good information as it is reasonably possible to provide. We
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also need to improve our understanding of travel improve our understanding of behaviour, we could also behaviour, and to develop robust theories of travel improve those "conventional demand models". Take, for behaviour, on which to base the development of really example, the simple level of service variable time, There behavioural demand models. These should be the topics are at least four different definitions of time of relevance to which research and development resources should be to travel demand studies. There is the "perceived" time directed in the next few years. on which the traveller's decision is made. Secondly there On the modelling side we now have an impressive is the "reported" time, which is the information usually array of techniques, an array which is bewildering and " elicited in a survey; this is not necessarily the same as confusing to the average transportation planner. What is the "perceived" time and could well be very different needed is a planner's guide to available disaggregate from it, although in many cases the two are unjustifiably modelling techniques which clearly and concisely des- assumed to be the same. Thirdly, there is the "actual" cribes the characteristics and capabilities (including time and here we have the complication that there is known and expected limitations) of each technique, rarely a unique value of the actual travel time between provides information on how and for what each tech- two points, due to variations in the system. The "actual" nique can (or should) be applied and, ideally, should time for any particular trip is therefore drawn from a include illustrative case-studies. There are, of course, distribution, which is probably skewed to the right. many problems and disadvantages associated with the Finally, we have the "network" time; this is the time we preparation of such a manual. Not least of these is the extract from our abstracted networks and is the only danger of providing misleading information through the value we normally have for the future situation for which need to simplify and to present in relatively lay terms, demand forecasts are required. Thus, the only two values material which is inherently fairly sophisticated. Yet of real relevance to demand forecasting are the "perunless there is comprehensive "state-of-the-art" docu- ceived" and "network" times, and we can only directly mentation, we will continue to fail to take full ad- quantify the latter. If we understood travel behaviour vantage of the very real accomplishments to date. The more fully than we do at present, we could improve our greatest problem, however, is in finding authors who mapping of the one with respect to the other and we have sufficient understanding of the needs and abilities could thus undoubtedly improve conventional demand of, typically, county (or state) planners as well the models, in terms of both their specification and the theories underlying recent research and. development quality of the data used for their estimation. work. Improving our understanding of travel behaviour is One major aspect of demand modelling to which there desirable if we are to improve the quality of our foreis a need to devote considerably more attention is that of casts of the effects of marginal changes in the system. data, a point already referred to. We generally accord far We know for instance that there are lags in response to too little attention to data collection, paying scant atten- system changes and that these are not reflected by contion to the quality of the information on which our ventional models. We also know that behaviour is not seemingly sophisticated models are based. In particular solely influenced by the "explanatory" variables norwe concern ourselves much too little with quality control mally used; but by far the greatest need for an improved throughout the data collection and processing phases; far understanding of behaviour relates to the estimation of too many data collection agencies are selected on the responses to major changes in the system which serve to basis of price rather than ability to achieve the requisite significantly extend or restrict traveller's choices; quality standards. Indeed, it is likely that many of the "threshold changes" which cause a major re-appraisal of problems encountered in model development can be travel related decisions. If we are to improve our underattributed to data errors rather than deficiencies in the standing of behaviour, and thus responses to major model specification or estimation procedures. Thus, there change, we need to exploit to the fullest extent possible is a real need to take a long hard look at data collection, the research opportunities provided by any such with a view to improving the relevance, the quality and changes. However, such changes are not a common • the cost effectiveness of data collection for travel occurrence, and therefore we must develop techniques demand studies. In doing this we need to address the which enable us to estimate the effects of threshold problems of maintaining quality standards in the field, changes, possibly by studying a series of marginal recognising that the needs of the transportation analyst changes some of which may, in fact, represent a are very different to the needs of the product or market- "threshold" change to specific individuals. ing manager, the main client for most market research 6. CONCLUSION agencies. Hopefully the growing political sensitivity to survey work, an attitude which those of us who have a The statement made in the proceedings of the Wilgenuine need for data must find disturbing, may at least liamsburg Conference that "Improvements in travel lead to greater attention to quality. forecasting methods are urgently required to supply Improving the relevance of data collection is necessary more effectively the information required for pressing whether we utilise conventional demand models, models transport decisions at all levels of government" (Brand which relate demand largely to level of service data and and Manheim, 1973) is as true today as it was in 1972. other simple characteristics of the system and the Equally valid is the statement "present procedures need traveller, be they aggregate or disaggregate, or true simplification or streamlining to improve their ability, behaviourat analysis techniques. Indeed, if we were to their policy sensitivity and their validity". Indeed one
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can go on through the conclusions of that conference and find that many of the statements are just as applicable in 1980 as they were in 1972. So, after a decade of research and development in disaggregate travel model analysis, can we share the optimism voiced at Williamsburg? The answer must be a cautiously qualified "yes". The deficiencies of aggregate models have not lessened with the passage of time, although there has been an improvement in the intelligence with which they are used which serves to reduce the effects of some of their deficiencies. During the same period we have come to realise that disaggregate models also have their problems and they do not yet offer a full substitute to aggregate models for large scale general planning studies, although they can be utilised for more limited applications. We have also increasingly come to appreciate the inadequacies of our understanding of travel behaviour and the theories on which our demand models, aggregate or disaggregate, are based. The highest research priority today is to improve those theories of behaviour, and we can only do this by studying individual behaviour, by using disaggregate analysis techniques. So disaggregate analysis can improve our forecasting abilities. The use of the word "analysis" here rather than "model" is important because the term "model" is often viewed in a narrow sense, Logit or Probit, rather than the wider sense of a formalised representation of a phenomenon. If we take the narrower sense, then there can be little doubt that under specific circumstances disaggregate models can improve our forecasting ability, though care and intelligence is required in their application, as it is with aggregate models. Provided we exercise that care and have the intelligence, there are very many situations in which the use of disaggregate models can bring about significant improvements in the timeliness, the cost-effectiveness and the accuracy of some travel demand forecasts, specifically those concerned with well defined choice situations in which the major explanatory variables can be identified and measured. However, we must direct our efforts more towards providing planners with a set of procedures which they can understand and apply so that they can make proper use of disaggregate models in appropriate circumstances. Research and development priorities for the 1980's should be in two main directions. The first is to consolidate and make more widely available existing modelling technology. The second is to improve our understanding of travel behaviour and from that to develop demand forecasting techniques appropriate for evaluating major, or "threshold", changes in the total transportation related system. It cannot be good use of in-
creasingly scarce research resources to direct a significant share of these resources to the mathematical, or econometric, aspects of model specification and estimation unless further work is found necessary to support the behavioural research. A third aspect needing attention is that of communications. Communications and understanding, between the research community, practising planners and decision-makers (bureaucrats and politicians) must be improved, if research is to fulfil its rightful role in the socially and economically important field of transport. Acknowledgements--In preparing this paper, I generally chose to avoid mention of specific pieces of work because I felt it insidious to single out individualpieces of work for comment when my own view of this developingfield is limited and when some of my information is based on hearsay than documentation. Nevertheless, it would be wrong not to acknowledge that there is a large and honourable body of work from which I have drawn my own conclusions; clearly it would be surprising if all the contributors to that work were to share all of my conclusions.
REFERENCES Alastair Dick and Associates (1976). Regional Highway Traffic Model: Project Report. Department of the Environment, London. Bates J., Gunn H. and Roberts M. 0978) A Disaggregate Model of Household Car Ownership. Department of Transport Res. Rep. 20, HMSO, London. Brand D. and Manheim M. L. (eds) (1973) Urban Travel Demand Forecasting. Spec. Rep. 143, High. Res. Board, Washington D.C. Hensher D. A. and Stopher P. R. (eds.) (1979) Behavioural Travel Modelling. Croom Helm, London. Leitch Sir George (1978) Report of the Advisory Committee on Trunk Road Assessment. HMSO, London. Leitch Sir George (1980) Forecasting Tragic on Trunk Roads: A Report on the Regional Highway Traffic Model. HMSO, London. Martin and Voorhees Associates (1980) Car Ownership Research Project: Final Report. Department of Transport, London. Oi, K. I. Y. and Shuldiner P. W. (1%2) An Analysis of Urban Travel Demands. Northwestern University Press, Evanston, Ill. Richards, M. G. (1981) Issues in Policy Sensitivity of Behavioural Models, In (Edited by P. R. Stopher, A. H. Meyburg and W. Br6g) New Horizons in Travel Behaviour Research. D.C. Heath, Lexington, Mass. Stopher P. R. and Meyburg A. H. (eds.) (1974) Behavioural Demand Modelling and Valuation of Travel Time. Spec. Rep. 149, Transpn Res. Board, Washington 13(2. Stopher P. R., Meyburg A. H. and Br6g, W. (eds.) (1981) New Horizons in TraveI-Behaviour Research. D.C. Heath, Lexington, Mass. Warner S. L. (1%2) Strategic Choice of Mode in Urban Travel: A Study in Binary Choice. Northwestern University Press, Evanston, Ill. Watson P. L. (1974) The Value of Time; Behavioural Models of Mode Choice. D.C. Heath, Lexington, Mass. Wilson A. G., Hawkins A. F, Hill G. J. and Wagon D. J. (1%9) Calibration and Testing the SELNEC Transport Model. Reg. Stud., 3 (3), 337-350.