integrated land use — transport models

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Integrated land use — transportmodelsRoger L. Mackett aa Institute for Transport Studies , University of Leeds ,Leeds, LS2 9JT, UKPublished online: 13 Mar 2007.

To cite this article: Roger L. Mackett (1985) Integrated land use — transport models,Transport Reviews: A Transnational Transdisciplinary Journal, 5:4, 325-343, DOI:10.1080/01441648508716610

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TRANSPORT REVIEWS, 1985, VOL. 5, No. 4, 325-343

Integrated land use—transport models

By ROGER L. MACKETT

Institute for Transport Studies, University of Leeds, Leeds, LS2 9JT, UK

ABSTRACT

This paper is a review of computer models of the interrelationships betweenland use and transport, particularly of the long-term effects of changes intransport costs on cities and the consequent effects on travel demand. The natureof this relationship is examined in terms of empirical evidence, and a set of criteriaagainst which the models can be evaluated is defined. Four major types of modelare examined: regression, mathematical programming, aggregate spatial interac-tion and individual choice. Each type is considered in terms of operationalexamples and the strengths and weaknesses of each approach are identified.However, it is recognized that few of the models are capable of representing themajor social and technological changes that are currently influencing urbandevelopment, and that this is where emphasis should be put if this type of model isto be useful for policy-making in the future.

§1. INTRODUCTION

The focus of this review is the computer model that represents the long-termeffects on the city of a change in transport cost. A change in transport costs might bean increase in petrol price or the provision of free buses, and the consequent impacton the city might be in terms the location of population, housing, economic activity,and so on. Clearly, a change in the pattern of population or economic activity will, inturn, influence the demand for transport.

There is a large number of models that are relevant to these issues. It is not thepurpose of this paper to describe every model that represents some aspect but toevaluate the various approaches, using suitable examples so the emphasis will be onmodels that have been made operational.

To make explicit the kind of issue that can be addressed with this type of model,the nature of the relationship between transport and land use, and how it can bemodelled, will be discussed in the next section. ('Land use' is taken to mean thelocation of population, housing, economic activity and so on, as well as the amount ofland utilized by the various activities.) Criteria will be identified against which themodels can be assessed. The following four sections include descriptions of:regression analysis, mathematical programming, spatial interaction and individualchoice models. Many of the models are combinations of techniques, and the variousapproaches are related to one another, but. it is convenient to structure thedescription of the models.

§2. T H E LAND USE—TRANSPORT RELATIONSHIP

Cities may be considered in terms of their functions or their forms. The functionsof the cities are the aggregations of the actions of the population, including residing,shopping, working, and travelling. The form of a city consists of its buildings andother physical infrastructure. The components of the two concepts may be allocated

326 R. L. Mackett

The four-way classification of cities

Forms Functions

Land use Buildings ActivitiesTransport Channels Flows

to two sets: land use and transport. Thus, the four-way classification shown in thetable may be specified (McLoughlin 1969).

Models are used to study the impact of change of these various factors becausethey offer a means of investigating possible alternative futures, of of seeing theimpact of a policy. Models of the relationship between land use and transport can bevery useful for studying cities because physical infrastructure can respond onlyslowly to a stimulus, whereas the change in the activities and flows can be almostinstantaneous. For example, the building of houses and roads can take many years,whereas the number of people working in a factory or the flow of cars on a road canchange much more quickly. However, the objectives of plans are often conceived interms of the activities and aspirations of individuals, while the planners control thelocation and size of the physical infrastructure which may be difficult to match to therequirements of the population because of the differential response rates. Themismatches manifest themselves as unemployment, congestion, overcrowding,vacant jobs, and so on. In order to be useful a model representing the city in thismanner needs to include both infrastructure and activities and to represent a longenough time period for a policy to have a significant effect. In modelling studies thisperiod is typically taken to be about twenty years.

Activities have a cyclical relationship with transport because activities, such aswork and shopping, generate trips among themselves, and the ease of access affectsthe level of land use activity, as shown in the figure. The left-hand side is representedby the 'conventional' transport model with its stages of trip generation, tripdistribution, modal split and assignment. This form of model has been used since thepioneering studies of Detroit and Chicago. No doubt many transport schemes havebeen implemented on the basis of forecasts developed from models of this type.However, the link from transport to land use is either ignored altogether, or used in acrude way to make the land-use forecasts. These forecasts are then input to thetransport model, which is used to represent several transport policies which mayaffect the spatial distribution of activities; yet there is no feedback to the land-usepattern (see, for example, WYTCONSULT, 1977). This weakness of the modelmay have led to some of the erroneous forecasts in the past.

LAND USE• ACTIVITIES'

TRIPS GENERATED ACCESSIBILITIES

TRANSPORT FLOWS

Integrated land use—transport models 111

Because there is a well accepted methodology for representing the effects of achange in land use on the transport system, the emphasis in this paper is on theconverse relationship, for which there is no accepted form of model representation.Indeed, there is not even a concensus on what the effects are; for example, how anincrease in the cost of travel will affect the spatial distribution of population andemployment in a city. There have been some studies of the impact of newinvestment, for example studies of the effects of new rapid transit in the Bay Area inSan Francisco (BART) and in Munich. BART was found to have had little impact onregional population and employment patterns (Dyett and Escudero 1978), confirm-ing evidence collected by Knight and Trygg (1977), and suggesting the need for thepresence of other favourable factors (Knight 1980). However, it may be that thechanges were more subtle than were anticipated in the BART study. For example, inMunich (Kreibich 1978) it was found that high-income families tended to moveoutwards, and so exacerbated the separation of home and jobs, while reducingcongestion. There have been very few comparable studies on new road links, partlybecause of the problems of measuring the attributes of the journeys being made; forexample, the research on the impact of the M62 motorway (Judge 1983) did notinclude assessment of the land-use implications.

An alternative approach to understanding the influence of transport costs on landuse is to examine the extent to which individuals take transport costs into accountwhen making locational decisions. Most of the work has been on the residentiallocation process, where a variety of factors are important. Sociologists have tended toconcentrate on the effects of stage in the life cycle (McCarthy 1976) and the housingneeds of the family (Rossi 1955). Stegman (1969) offers empirical evidence thatneighbourhood characteristics are more important than accessibility to work indetermining the residential location. However, people asked to list their reasons fortheir choice of dwelling may not quote ease of access to work as a reason because theyregard it an an essential quality for any feasible choice. Several researchers havefound direct evidence of the relationship, for example Brown (1975) used householddata for the Bay Area to establish that those with changes of workplace are morelikely to change residential location and that a household is more likely to move if anew workplace requires an increase in work trip length. Mackett and Johnson (1985)found that access to work was the single most important reason given for the choiceof residential location amongst a set of commuters to central London choosing a newdwelling, and De Langen and Verster (1978) found that the opening of a new tunnelhad a substantial impact on the location of households.

To sum up, there is a cyclical relationship between land use and transport. Theeffect of the former on the latter has been well specified and modelled many times.There is evidence that transport costs have an effect on land use, but there is noaccepted method of representing the relationship. Consequently, some of the ways inwhich the relationship has been modelled will be examined in the following sections.These will be considered in terms of a set of criteria, so that the usefulness of themodels for an analysis of the impact of transport on the city can be assessed. Giventhe variety and complexity of the problems, which make it impracticable to lay downexact criteria (for example, the number of modes of travel represented), the modelswill be examined in terms of the following questions:

How comprehensive is the model, in terms of the number of components of theurban system that are represented?

328 R. L. Mackett

What is the mechanism for the representation of the effects of transport on landuse?How is modal choice represented?Is assignment to the network included, or could it be?Can the model cope with decline, or is it merely allocating increments of growth?Is there any form of social or income group disaggregation? How is time treated?Is the model operational and tested?To what extent does the model structure permit the testing of transport and landuse planning policies?

It is not an easy task to evaluate models against these criteria for several reasons.Firstly, the models are often very complex, and so it is difficult to comprehendexactly what form the mechanism takes; secondly, authors rarely state what is not inthe models; thirdly, the models may have been developed for the examination ofparticular policies and so are described in rather different terms from the main themeof this paper; and fourthly, it is not always possible to establish whether the ideaswere developed into operational models or remained long-term objectives whichwere never fulfilled.

There is a further issue which follows from the previous comments: if a particularmodel does not meet a criterion, it is important to consider whether this is because itis fundamentally impossible, given the nature of the model, or simply because thiswas not relevant to the particular study. Consequently, a variety of models will beconsidered to prevent rejection of a particular style of model simply because aparticular example does not meet the criteria.

§3. REGRESSION MODELS

A regression model takes the form

where D is the value of the independent variable which is being estimated, Ik is thevalue of the £th independent variable which determines the value of D, <xk is a set ofcoefficients which express the relationship between the independent and dependentrelationship and ß is a constant. The coefficients are estimated by minimizing thedeviation between the estimates from the right-hand side of the above equation and aknown set of values of D. The values of the coefficients are assumed to be stable overtime. There are many standard computer programs for the estimation of thecoefficients in the regression model. The model shown here implies a linearrelationship between the dependent and independent variables, but transformationscan be used to represent non-linearities.

A regression model can be used to find the spatial distribution of land uses bymaking the dependent variable the population, employment or housing in each zone,and the independent variables the set of variables that determine the required spatialdistribution. One or more of these variables can be an accessibility measure, perhapsof the Hansen-type (Hansen 1959), so that transport costs can be incorporated; thusregression analysis can be used to determine the influence of transport on land use.The inter-dependence between the location of activities, for example population andemployment, can be represented by simultaneous equations. There is, however, adanger of instability in the solution if simultaneous equations are used (Seidman

1969). For example, there may be no unique solution, with the spatial distribution ofpopulation generated by employment different from that which has determined theemployment pattern.

The nature of the technique means that functional relationships can be definedeasily and tested on the available data. Consequently, the method can be used tolocate a wide range of topics, for example, housing, population, sales in shops,employment, and so on. By using simultaneous equations, a complex model can bebuilt up; a good example is the INTRA-I model (Putman 1970), developed for theNorth East Corridor Study, in which regression equations were used for tenemployment sections and the components of population change of birth, death andmigration, as well as personal incomes and land values.

The effects of transport costs on land use are usually represented by regressioncoefficients applied to Hansen-type accessibility measures. Because land use modelsbased on regression analysis determine the amount of activity in a zone directly, nointerzonal trip matrix is produced. Consequently, no modal choice mechanism isrepresented, and there is no trip matrix for assignment to the road network.

Decline can be represented provided that suitable data are available representingthe required relationship.

As already implied, the model can be used for the population and employmentdisaggregated into groups. For example Lakshmanan (1968) used three incomegroups, and in EMPIRIC (Hill 1965) two social groups were used for the populationlocation. In the INTRA-I model, ten industrial sectors were used. However, thedifferent sectors may be competing for the same resources and it would be verydifficult to represent this within the regression framework. The tempting, but ratherunsatisfactory, solution of treating the final category as a residual or scaling the sumover all categories would violate the initial assumptions about the functionalrelationships.

Regression models can be made temporally recursive by lagging the variablesover time, as in INTRA, or by considering the change over a time period, as inEMPIRIC. Time could be incorporated directly as an independent variable, but thepreference seems to have been for the two methods mentioned above.

EMPIRIC is claimed to be the land use model that has been most widely used inthe United States (Pack 1975). This can be attributed to the ease with which it can beimplemented. Consequently, it may be regarded as operational. However, theforecasting ability of the model raises serious doubts about its usefulness. Themethod for estimating the coefficients implies the production of best fit between themodel and the data. Stokes (1974) found that, whilst the model could replicate thedata against which it was calibrated fairly well, it failed very badly when used forforecasting. This was attributed to the assumption of constancy in the regressioncoefficients, which prevents the effects of new factors or variations in the interactionbetween the variables being taken into account.

Regression models permit the testing of planning policies as long as they can beincorporated as independent variables. This implies that the functional relationshipbetween the independent variable and the policy is known; otherwise the effects maybe misinterpreted.

In conclusion, regression models are simple to use, and can be very flexible, sinceno theoretical functional relationships have to be specified. Hence, complex modelscontaining many policy-sensitive variables can easily be built up and quickly madeoperational. The major weaknesses of the approach are the lack of theoretical

330 R. L. Mackett

underpinning, the assumption that the coefficients are stable over time, and thepossible instability in the solution. The method is not applicable to a fully integratedland use—transport model because no trip matrix is produced and modal choice isnot represented. This is a major weakness because many people's response to achange in transport costs might be a change of mode rather than to relocate. Themodel says nothing about this. Consequently, regression analysis does not seem to bea very useful approach to the development of complex models of the interrelation-ships between land use and transport. It may, however, have a part to play as acomponent representing particular relationships within an eclectic approach to theproblem.

§4. MATHEMATICAL PROGRAMMING MODELSA mathematical programming model is designed to produce the optimal

allocation of a particular quantity, subject to a set of constraints. The quantity beingallocated may be the amount of extra activity in each zone in a time period or thenumber of households in a particular type of housing. The quantity being allocated isincorporated into an objective function which is optimized, subject to the con-straints, which, in general, ensure that all the quantity being optimized is allocated,that no supply side constraints are violated and that the allocations are non-negative.The optimal solution is usually the one that minimizes costs or maximizes benefits.

An early example is the model of Herbert and Stevens (1960) which wasoriginally developed as part of the Penn-Jersey Transportation Study. The modelallocates households to sites by maximizing aggregate rent-paying ability subject tothe constraints that all households must be located and that the total land used in eachzone must not exceed the area available. The cost of travel to work is included withinthe locational budget term used in the model. Unfortunately the model requiredsuch vast quantities of data that it was never made operational. More recently, Los(1978) incorporated congestion into the Herbert and Stevens model, making the finaldistribution of households responsive to detailed transport policy issues, but thatmodel was even more demanding on data.

A programming model that has been made operational is TOPAZ (Techniquefor the Optimal Placement of Activities in Zones) which was developed in Australia(Brotchie et al. 1980) and applied to a variety of problems such as finding the optimalgrowth patterns for Melbourne (Sharpe et al. 1975) and to find the effects ofupgrading the Melbourne-Sydney link (Dickey and Sharpe 1974). Basically themodel is used to find the allocation of activities to zones by minimizing the total costof establishment and travel, subject to the constraints that all activities are locatedand that all zones are filled (including vacant land as an 'activity'). Accessibility isincluded directly in the form of a gravity model used to find the total cost of travel.

TRANSLOC, a model somewhat similar to TOPAZ has been developed inSweden (Lundqvist 1975) and applied to the Stockholm region and used to examinevarious investment programmes for the area.

This type of model has been developed to assist in the preparation of plans bydemonstrating how to achieve a particular objective rather than by purporting torepresent behavioural response over time. This limits the value of the approach forthe particular problem considered here. Transport can only be represented in theobjective function fairly crudely. In the SALOC model (Lundqvist 1984), which is asuccessor to the TRANSLOC model, accessibility measures are included asindependent variables but there is no representation of consequent travel demand.

The model could be used to determine the spatial allocation of land uses, to which aconventional transport model could be applied, although it might be difficult toensure consistency with the transport component of the objective function.

The mathematical programming approach is essentially a simple one. All thecomponents of the urban system that are being represented in the model must berelated within the objective function, which means that the functional relationshipshave to be aggregated together. This limits the number of components that can berepresented, and so prevents the development of a comprehensive urban programm-ing model.

In general, this type of model is allocating growth, and so not really suitable forthe representation of a declining area (although it might be possible to allocatenegative growth). A social or income-group disaggregation could be incorporated,but the interaction between them might be rather crude, with assumptions aboutfixed relationships between them, unless the model is representing a specificcomponent of the urban system such as the housing market. This type of model istemporally cross-sectional, giving the allocation at one point in time. Dynamicprogramming techniques offer scope for the representation of allocations at differentpoints in time but they are more appropriate for integer problems. The modelTOPAZ has been applied many times, but the Herbert and Stevens model was nevermade operational because of the problems of obtaining suitable data. Programmingmodels have been designed for the analysis of planning policy but the examplesdiscussed here are not very useful for the analysis of the long-term impact of changesin transport costs on cities.

By definition, use of a programming model produces an optimal solution to thedefined problem. Nothing is said about how that solution is achieved, or about thecompetition between individuals for houses and jobs, and between activities for land.This type of model is best used for identifying feasible solutions, rather than as aforecasting tool, since time can be represented only by forecasting certain valuesoutside the model and then applying it for the appropriate time point. The allocationof activities to zones cannot take into account economies of scale since every unit ofactivity allocated to a zone must be at the same cost and cannot be subjected to upperand lower limits in a particular zone. The only interrelationships between activitiesare those explicitly defined in the objective function or constraints and so localinterdependencies, for example between particular industries, may be ignored.

Despite these weaknesses, programming models do have a useful role, particu-larly in identifying extreme solutions for problems and examining the effects ofdifferent weightings on the solution.

§5. SPATIAL INTERACTION MODELS

A spatial interaction (or gravity) model represents the flows of trips betweenzones. These are functions of measures of the cost of travel between each pair ofzones and measures of the level of activity at each end of the trip. If the total numberof trips entering or leaving a zone is known, a constraint can be introduced to ensurethat the sum of all the trips generated from, or attracted to a zone, is equal to theknown total. In other cases, some form of attraction factor is used and the number oftrips leaving, or arriving at, each zone can be found by aggregation. In the latter case,the model may be termed a 'locational model', and used to estimate the level ofactivity represented by the total trips at the zonal level.

332 R. L. Mackett

The spatial interaction function described above, has been incorporated intoseveral land use—transport models, of which the best known is the Model ofMetropolis, developed by Lowry (1964), which has the dual advantages of beingconceptually simple and, by including population, employment, retailing and land,comprehensive. In essence the model may be described as follows: basic employ-ment is distributed between zones exogenously; the workers have places ofresidence, so a gravity model is used to locate them; the dependent families of theseworkers are generated by applying the inverse activity rate (defined as the ratio oftotal population to total employment); the population demands services, and thesedemands are met by non-basic employment (for example, in shops) and theseworkers are located by means of another gravity model; these workers and dependentfamilies are located in a similar manner, and also generate non-basic activities. Thusfurther non-basic workers and dependent families are generated until the incrementsare insignificant.

The model is comprehensive, since it includes housing, residential location,employment, shopping, the journey to work, the journey to shop, and landallocation. In the original application to Pittsburgh, interzonal costs were represen-ted by airline distances, so transport costs were not represented explicitly. However,the population and non-basic employment location equations were gravity models.Because there was no road network the trip matrices could not be assigned. Themodel was originally used to produce a city given the spatial distribution of basicemployment. Consequently, it was seen as a model for the allocation of growth, andso could not be used directly to examine the effects of decline. There was no social orincome group disaggregation, although there were three non-basic sectors. Since themodel was devised to represent the relationships between the various components ata single point in time, the responses were not lagged temporally. The original modelwas operational, being part of the Pittsburgh Community Renewal Program. In itsoriginal form the model could be used for testing a variety of land use policies, butnot transport policies because of the use of airline distances.

The comprehensive nature, yet relatively simple structure of the Lowry modelhas led to many derivative versions (Goldner 1971). One of the first of these wasTOMM (Time Oriented Metropolitan Model) (Steger 1965), with householdsdisaggregated by income and quasi-dynamics introduced by locating marginalgrowth. A later version of the Lowry model was the Projective Land Use Model(PLUM) (Goldner et al. 1972), which grew out of the Bay Area Simulation Study(BASS I) used to test the impact of industrial location (Goldner 1971). In PLUMnetwork times were calculated from skim-trees for the private mode only and thenumber of residential opportunities were incorporated as attraction factors. Anincremental version of PLUM (IPLUM) was developed into the IntegratedTransport and Land Use Package (ITLUP) by Putman (1980). In this model, theeffects of congestion on the road network are incorporated by using capacity restraintassignment. The model was revised to improve the calibration of the residentiallocation submodel by the introduction of the Disaggregated Residential AllocationModel (DRAM) (Putman 1977). The model is disaggregated into four incomegroups, and now includes public transport (Putman 1984).

During the late 1960s there was an awakening of interest in land-use models inBritain, largely stimulated by the theoretical underpinning using entropy maximiz-ation methods given to the spatial interaction model by Wilson (1967), who defined a'family of spatial interactions models' containing four versions of the model

according to whether the number of zonal origins, the number of zonal destinations,both, or neither, is known. This work coincided with a series of subregional studieswhich included forecasting zonal allocations of population and employment within

. the subregion. Models of the Lowry type were seen as an important input to theforecasting procedure, possibly because of the wide gulf between land use andtransport planning processes at the time. Consequently, the early applications of thistype of model rarely included modal choice or capacity restraint assignment.

The first application in Britain of a model of the Lowry type was by Cripps andFoot (1969) who applied the model to Bedfordshire, using intervening opportunitiesin the residential location submodel, but travel times in the non-basic submodel.The model was used to examine the impact of two potential sites for a third Londonairport (Cripps and Foot 1970).

Several other studies using the Lowry model were carried out in the late sixtiesand early seventies, for example in Central Lancashire, to test the impact of a newtown (Batty 1970 a), in the South Hampshire study (Rhodes 1969) and in theNottinghamshire-Derbyshire area, as part of the subregional study (Batty 1970 b).Because these versions of the Lowry model included interzonal travel costs, theseapplications contain crude representations of the transport system. However, theycontained no modal split, and basic employment was located exogenously.

Similar models were developed by researchers in the Martin Centre at theUniversity of Cambridge, and applied to Reading (Echenique et al. 1972). In theseapplications, only one mode of transport was used and basic employment was locatedexogenously. More recently more advanced versions of these models have beenapplied in South America (Echenique 1984), Iran (Garnett 1980) and Spain (Burgos1984).

Another example of British work is the Leeds Integrated Land Use-TransportModel (LILT) which has been used to examine the impact of a variety of transportpolicies for the city of Leeds (Mackett 1983 b, 1984 b).

There are a number of other examples of spatial interaction models of land useand transport that have been applied around the world. However, few of them arestill being used either within the planning process or as research tools.

The criteria being used to assess the models can now be applied to the variousderivatives of the Lowry model to see how far the modifications made overcome theproblems identified above.

The original Lowry model may be regarded as comprehensive. The modifiedversions have tended to improve the transport side, by incorporating modal split,congestion and (in the case of LILT) car ownership. In general the models usesophisticated spatial interaction mechanisms to represent the effects of accessibilityon the location of activities, either using an input-output model (Echenique model)or multiplicative attraction factors (ITLUP and LILT). As mentioned above, modalchoice and assignment are represented within these models. In general, the modelsare quasi-dynamic, working over time, starting from a well specified data base forone year and allocating exogenously defined totals of population and jobs for eachfuture time horizon. A decline would be represented by a lower total at one timehorizon than the previous one. These models could represent this for at least some ofthe sectors, particularly LILT which was developed for a city in decline. All theseexamples include disaggregation by income or social group, particularly in theresidential location procedure. As mentioned above, the models work over time,typically in five-year periods, with lag mechanisms to link the various land use and

334 R. L. Mackett

transport elements at various points in time. These models are all operational, havebeen tested, and have been used to test a variety of planning policies.

The original model devised by Lowry has provided a basis for a number ofderivatives. These meet the criteria in various ways, having been developed fairlypragmatically for their application to the examination of policies in a variety of studyareas. These models can, however, require quite large data-bases and take quitesubstantial amounts of computing resources. Clearly, in any application a balancemust be reached between the degree of complexity and the level of availableresources.

§6. MODELS OF INDIVIDUAL BEHAVIOURThe models discussed above are aggregate in nature, that is, they are concerned

with groups of people, rather than individuals. During the 1970s, models of thebehaviour of individuals, often called disaggregate models, became popular in thefield of travel demand analysis, particularly for the representation of modal choice(for example, Richards and Ben-Akiva 1975). The commonest form of model is themultinomial logit model which was proposed as a theory of psychological choice byLuce (1959) and has been formulated as a model of micro-economic choice theory oftravel by McFadden (1973, 1976). This type of model tends to be efficient in thesense that all the data values, rather than just averages, are used.

As mentioned above, this type of model has been used mainly for the study ofmodal choice, but has also been used in the study of locational choice by Lerman(1976), Louviere (1979) and Anas (1982). In Lerman's model, the choice ofresidential location, type of housing, car ownership and mode of travel to work areestimated simultaneously; Louviere's work is concentrated on housing choice, andAnas's work on the mode to work and residential location. The nature of this type ofmodel, concentrating on the characteristics of individuals and the choices availableto them, means that the model cannot be very comprehensive for two reasons: first,the number of combinations of choices grows very rapidly as new components areadded and, secondly, some of the characteristics of the behavioural units have toremain fixed to determine the decisions being made; for example, a model thatrepresents the choice of job and its location, type of home and its location, numberof cars, shopping destination, and mode of travel to work and to shop would have ahuge number of choices available, and would imply that all these choices were beingmade simultaneously which, in general, they are not. This type of model isessentially cross-sectional in time, although it would be possible to introduce timelags by using attributes at an earlier point in time.

The effects of transport costs on the choices made, and hence land use, isincorporated through the regression coefficients. The modal choice mechanism canbe incorporated in the model for as many modes as desired, and a trip distributionmatrix obtained for the people represented. However, for computational and datacollection reasons, only a limited number of people are represented (usually a fewthousand), so that the interaction with all others in the city cannot be represented.

The response of individuals to decline, for example, fewer houses, can berepresented, as can any form of social or income group disaggregation. Indeed, theability to represent different market segments is one of the strengths of this

approach. The models cited above have been made operational and tested against thecalibration data. One of the advantages that is claimed for this type of model is thatthe coefficients can be transferred to other study areas without recalibration(Koppelman and Rose 1984). This may be true for modal choice—where thecharacteristics of the supply side do not vary all that much from one area to another—but the spatial and temporal stability of parameters of locational and housing choicehas yet to be demonstrated. Policies can be tested using this type of model, providingthat they can be incorporated within the attributes of the choice set.

These models offer some useful insights into the response of the demand side tochanges, but there are a number of methodological problems that are stilloutstanding (Lerman 1984). Two major ones (that the alternatives being consideredmust be independent and the number of alternatives must be manageable) have beensolved theoretically by McFadden (1978). A major issue is the definition of thechoices that are regarded as available. In a modal split model, the choices availableare well defined, but when the choice of residential location is being considered, thereare many possible choices and not all will be considered by a person choosing a newlocation. Some people may choose an area and then search for a suitable dwellingsystematically, others may consider houses of a particular type in several areas.There has been some useful work on the generation of choice sets from search theory(Meyer 1980, Richardson 1980), and this may be the way forward.

There are several other problems associated with using this approach formodelling land use-transport interactions; for example, the extent to whichlocational decision-makers actually take travel costs into account, the interrelation-ships between decisions, the influence of other activities on travel, and the definitionof the decision unit (persons or households) (Mackett 1983 a).

An alternative approach at the individual level is microanalytic simulation (ormicrosimulation), whereby the behaviour of individuals over time is representedexplicitly on a computer using Monte Carlo simulation to determine the outcomes ofdecision processes for individuals. This approach has been used by Orcutt et al.(1961, 1976) and by Clarke et al. (1981) for the analysis of policy in the labour andhousing markets, but taking little account of transport costs. Wegener (1982) isdeveloping a multi-level, multi-activity composite model including an equilibrium-type transport model, accessibility-based location models, and a microanalyticsimulation model of migration and the housing market (Wegener 1983). Amicroanalytic simulation model that explicitly represents travel and locationaldecisions is being developed by Mackett (1984 a). While both these models are stillbeing developed, they do suggest that such an approach may be very fruitful, simplybecause the models are very flexible. The basic requirement is that processes can bedefined in terms of the individual decisions, with all the possible outcomes linked tothe characteristics of the relevant decision unit (person or household). The modelscan be comprehensive, include social and income disaggregation, represent modalchoice, produce trips than can be assigned to the network, and can cope with decline.They represent processes over time, often using quite short time periods (one or twoyears). The models are suitable for the testing of policies, and are currently beingmade operational and tested. The disadvantages of such models are that they requiredata on individuals (although this can be estimated from aggregate data) and theyrequire large computing resources. There are also a number of research issues still tobe resolved about the appropriate sequence for the decision processes and the degreeof interaction between the decision-making units.

336 R. L. Mackett

To sum up, models of individual behaviour offer the opportunity of examiningthe interaction between transport and land use in terms of the decision processes ofindividuals. The approach can be very flexible, but there is a need for substantial dataand computing resources.

§7. ASSESSMENTThe various model types discussed above meet the criteria to varying extents, and

it is not the objective of this paper to claim that one approach is better than another.In fact the classification into the four model types is rather misleading for tworeasons: first some models are based on more than one methodology and secondly,the approaches are, in fact, all related. For example, Anas (1983) has shown that theentropy maximization approach of Wilson (1967) and the behavioural travel demandmethodology based on stochastic utility maximization, are equivalent views of thesame problem, and Wilson and Senior (1974) have shown that there is a closerelationship between mathematical programming and entropy maximizing appro-aches for residential location modelling. However, even if the models are related,"there is the question as to the comparability of the results from the variousapproaches. A study, organized by the Transport and Road Research Laboratory, isnow being carried out to compare a number of these models. The models are beingexamined by the International Study Group on Land Use-Transport Interaction(ISGLUTI) by comparing the forecasts from each model in terms of a standardizedset of outputs, for a common set of test inputs. From this exercise it will be possible todetermine the extent to which the models produce similar forecasts and, if they donot, why the results differ.

The ISGLUTI study is based on nine models, which is only a small fraction of allthe models that have been developed. There seem to have been many modelsdeveloped in the past which are no longer being used. If the models have served theirfunction, then it is not a serious problem, but if the models have been abandonedbecause they were too inaccurate, or required too great resources, then it is worrying.

All the models considered here reflect the nature of society in the recent past.However, the form of cities and travel demand are likely to be influencedsignificantly by a number of factors. Two major developments are societal changeand technological change. Some of the trends in society include: increasing rates ofhousehold formation, growth in numbers of 'unconventional' households, increas-ing female participation in the labour force, and greater flexibility of working hours.Trends in technological change include the growth in telecommunications, linkedwith falling costs and the shift from manufacturing sectors to the information andpersonal service sectors. The changes in society are likely to have significant directeffects on the demand for both public and private modes, while at the same timecausing major changes in the housing and job markets with feedback to traveldemand. The technological changes will similarly influence the amount of travel,with some trips replaced by telecommunications (for example with more home-working), but probably many more stimulated. There will also be secondary effectsassociated with the changes in urban form engendered by industries associated withnew computing technology, the decentralization of administrative functions withimproved telecommunication links, and the reduction in the need for access betweenhomes and jobs. The majority of the models identified in this paper cannot wellrepresent either of these areas of change. In terms of the changes in society, most ofthe models do not even distinguish between males and females or represent the

decline in average household size. In fact many do not represent car ownershipexcept, perhaps, as an exogenous input, yet the growth in the use of the car has hadsignificant effects on travel patterns, and subsequently on land use patterns. Asdiscussed in §2, the locational responses to investment in transport infrastructuremay be quite subtle, and it is doubtful whether many of the models are capable ofshowing this type of response. Microanalytic simulation is one approach that canrepresent at least some of these processes. The changes in technology are even moredifficult to represent in this type of model because such changes will be outside therange of behaviour the models represent, so only very limited scenarios can beexamined (Mackett and Lodwick 1984).

§8. CONCLUSIONS

The issues than can be represented by integrated land use-transport models areimportant ones. There are a number of techniques that can be used, each of whichhas its strengths and weaknesses, and the decision as to the most appropriatemethodology should be determined on the basis of the objectives of the study and theavailability of data and other resources. There is no shortage of examples of modelsthat have been developed and, in many cases, applied. The relatively small numberthat are still being used may well be a reflection of the exaggerated claims for somemodels and their misuse in the past, rather than faults in the models themselves. Itcan be argued that the presentations, 'International Symposium on New Directionsin Urban Modelling' (Hutchinson et al. 1984) held at Waterloo, Canada in July 1983reflected the state of the art at the time. If this is so, researchers in the field of landuse—transport modelling have become much more modest in their aims and aretackling more specific issues. However, as implied in the previous section, there are anumber of changes in the real world that need to be reflected in this type of work ifland use and transport models are going to be useful to policy-makers, and to make avalid contribution to our understanding of the nature of change in urban areas.

FOREIGN SUMMARIES

L'article porte sur les modèles informatisés d'interrelations entre l'usage du sol et lestransports et, tout particulièrement, sur les incidences à long terme des modifications dans lescoûts de transport sur la structure urbaine et les effets en retour sur la demande de transport.C'est sur une base empirique que l'on s'interroge sur la nature de cette interrelation, enévaluant les modèles par rapport à un ensemble de critères de référence. Les modèles sontregroupés en quatre grandes catégories: la régression, la programmation mathématique,l'interaction spatiale agrégée et le choix individuel. Pour chacune d'entr'elles, l'examend'applications opérationnelles permet de mettre en évidence ses forces et ses faiblesses. Enfait, il n'existe guère de modèles qui soient en mesure de formaliser les transformationssociales et techniques fondamentales qui agissent sur le développement urbain; c'est doncdans cette voie qu'il conviendrait de pousser les recherches si on veut rendre des modèles de cegenre utilisables dans la préparation des politiques urbaines de l'avenir.

Dieses Papier gibt einen Überblick über Rechenmodelle zur Beschreibung der Abhängig-keiten zwischen Flächennutzung und Verkehr unter besonderer Berücksichtigung län-gerfristiger Auswirkungen von Veränderungen der Verkehrskosten auf die Städte und darausresultierend auf die Verkehrsnachfrage. Die Struktur dieser Abhängigkeiten wird anhand vonempirischen Befunden untersucht; es wird ein Kriterienkatalog definiert, mit dem dieModelle bewertet werden können. Vier Haupttypen von Modellen werden untersucht:Regression, mathematische Programmierung, aggregierte räumliche Interaktion und indiv-iduelle Verhaltensmodelle. Jeder Modelltyp wird anhand von praktischen Beispielenvorgestellt; Vor- und Nachteile jeder Vorgehensweise werden herausgearbeitet. Es wird

338 R. L. Mackett

dargestellt, daß nur wenige der Modelle imstande sind, die wesentlichen sozialen undtechnologischen Veränderungen, welche die Stadtentwicklung heute beeinflussen, ab-zubilden, daß man aber gerade hierauf besonderes Augenmerk lenken muß, wenn einModelltyp für politische Entscheidungen über die Zukunft nützlich sein soll.

Este trabajo es una revisión de modelos computacionales de la interrelación entre uso desuelo y transporte, en particular de los efectos de cambios en los costos de transporte enciudades y sus consecuencias en la demanda por viajes en el largo plazo. Se examina lanaturaleza de esta relación a la luz de evidencia empirica, y se define un conjunto de criteriospara evaluar los distintos modelos propuestos.

Se consideran 4 tipos principales de modelos: regresión, programación matemática,interacción espacial agregada y elección individual. Cada tipo se analiza haciendo referencia aejemplos operacionales, identificándose las ventajas y debilidades de cade enfoque. Sinembargo, se llega a la conclusión de que pocos de estos modelos son capaces de representar losprincipales cambios sociales y tecnológicos que influencian el desarrollo urbano en laactualidad; por esta razón, se recomienda que este área se enfatice si se desea que este tipo demodelo llegue a ser de utilidad como herramienta de toma de decisiones en el futuro.

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GOLDNER, W., 1971, The Lowry model heritage. Journal of the American Institute of Planners,37, 100.

GOLDNER, W., ROSENTHAL, S. R., and MEREDITH, J. R., 1972, Theory and Application:Projective Land Use Model, Vol. 2, Institute of Transportation and Traffic Engineering,University of California, Berkeley.

HANSEN, W. G., 1959, How accessibility shapes land use. Journal of the American Institute ofPlanners, 25, 73.

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HUTCHINSON, B., NIJKAMP, P., and BATTY, M., 1984, Optimization and Discrete Choice inUrban Systems (Springer-Verlag).

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KNIGHT, R. L., 1980, The impact of rail transit on land use: evidence and a change ofperspective. Transportation, 9, 3.

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KREIBICH, V., 1978, The successful transportation system and the regional planning problem:an evaluation of the Munich rapid transit system in the context of urban and regionalplanning policy. Transportation, 7, 137.

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LUNDQVIST, L., 1984, Analyzing the impacts of energy factors on urban form. The Future ofUrban Form, edited by J. F. Brotchie, P. W. Newton, P. Hall and P. Nijkamp(Croom Helm).

MACKETT, R. L., 1983 a, The inter-relationships between travel and locational decisions. InRecent Advances in Travel Demand Analysis, edited by S. M. Carpenter and P. M. Jones(Gower), p. 79.

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MACKETT, R. L., 1984 b, The impact of transport policy on the city. Supplementary Report821, Transport and Road Research Laboratory, Crowthorne, Berkshire.

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PUTMAN, S. H., 1970, Developing and testing an intra-regional model. Regional studies, 4,473.PUTMAN, S. H., 1977, Calibrating a disaggregated residential allocated model. Alternative

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Technical Supplement, Vol. A, Philadelphia, Pennsylvania.SHARP, E. R., BROTCHIE, J. F., and AHERN, P. A., 1975, Evaluation of alternative growth

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STEGMAN, M. A., 1969, Accessibility models and residential location. Journal of the AmericanInstitute of Planners, 35, 22.

STOKES, E. B., 1974, The evaluation of an EMPIRIC model for an urban area. Environmentand Planning, 6, 703.

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WEGENER, M., 1983, The Dortmund housing market model: a Monte Carlo Simulation of aregional housing market. Arbeitspaper number 7, Institut fuer Raumplanung, Univer-sität Dortmund.

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WILSON, A. G., and SENIOR, M. L., 1974, Some relationships between entropy-maximisingmodels, mathematical programming models and their duels. Journal of RegionalScience, 14, 207.

WYTCONSULT, 1977, Design year planning data and car ownership forecasts, Report 209,Wakefield.

EDITORIAL SUGGESTIONS FOR FURTHER READING

BLAND, B. H., 1982, Land-Use patterns and travel, Transport and Road ResearchLaboratory, LR 1092.

The LUTE model of travel by car, bus and on foot has been used to predict travelin a set of hypothetical towns with a wide range of sizes, shapes and populationdensities, subject to the average time spent travelling per day and the number of tripsbeing held constant, the fares revenue covering a specified proportion of theoperating cost, and allowing for the finite seating capacity of the buses. The resultsare intended to be of interest to bus operators concerned about future levels ofservice, fares, patronage and profitability as car ownership increases and residentialdensities decline, to transport planners interested in the behaviour of the transportsystem as a whole, and to planners who wish to assess the travel and accessibilityconsequences of alternative development patterns. The predictions are in goodagreement with what is observed: bus competes with walking, but if a car is availablewhen a trip is made bus is rarely used. Above the critical population density at whichfares revenue first becomes sufficient to cover operating costs, the operator has a widechoice of fares and frequencies, and if these are chosen so as to maximise patronagethen the elasticity of demand with respect to service headway should be equal to orsmaller than that with respect to fares. Use per car varies little with land-use patternor bus service levels, although car ownership is lower where incomes are lower, orwhere congestion and parking difficulties or good access by public transport or onfoot make car ownership less worthwhile. Walking remains an important mode in allareas, both in its own right and as an indispensable component of bus and car travel.Neither public transport subsidies nor higher densities seem to be effective ways ofreducing car ownership or use. The suggestion that travel can be reduced by movinghomes and jobs closer together is not supported by the modelling.

(Author)

BLUNDEN, W. R., and BLACK, J. A., 1983, The Land Use/Transport System. Secondedition (Oxford: Pergamon).

This book advances an integrated planning approach to land use and multi-modal transport at the local, urban and regional scales and is concerned with theanalysis which underpins such planning. The book breaks new ground in formalis-ing land-use and transport interactions, especially the notions of feedback, equilib-rium and steady-state performance. The authors have drawn heavily on the methodsand practice of operations research and urban modelling and emphasise the power ofthe quantitative doctrine: to define, measure and develop conceptual relationships.Tables and graphs are included to provide the reader with numerical values for manyessential constants and parameters.

(Author)

342 R. L. Mackett

DICKEY, J. W., and LEINER, C , 1983, Use of TOPAZ for transportation-land useplanning in a suburban county, Transportation Research Record, 931, pp. 20-26.

Techniques used to create and assess a variety of year 2000 joint land use andhighway network patterns for Prince William County, Virginia, are described. Theassessment has been done mainly in terms of travel and related impacts. The relatedimpacts include the overall cost of travel, congestion levels, fuel consumption, andair pollution emissions. Volume/capacity ratios on each highway link in the countywere also estimated. A sketch-planning procedure called Technique for theOptimum Placement of Activities in Zones (TOPAZ) was used to allocate expectedfuture land use activities to 11 districts in the county so as to minimize overall travelcost. Travel impacts were then analysed in more depth through separate and moredetailed models included in a model called Transportation Integrated ModellingSystems (TRIMS) used by the Metropolitan Washington Council of Governments.The results of these efforts led to several preliminary conclusions concerning notonly the techniques themselves but also their place in the comprehensive planningprocess: (a) residents of the county will be faced with an increase in overall travelcosts and congestion no matter which reasonable alternatives are implemented; (b)the most ambitious highway improvement program will reduce costs by about 9percent, and the proper organization of land use will reduce this by an additional 6percent; (c) future changes in external factors, such as population and fuel pricelevels, can have impacts on travel as substantial as those created through newhighway construction and proper land use organization in the county; and (d)although TOPAZ supported the Prince William County comprehensive planningeffort, it had relatively little direct impact on county decision makers, probablybecause it was not used at a time when citizens and local elected and appointedofficials began to examine the draft comprehensive plan. ( A h i

MACKETT, R. L., 1984, The impact of transport policy on the city, Transport andRoad Research Laboratory, SR 821.

One way to forecast the impact of transport policy in an urban area is to use acomputer simulation model such as the Leeds Integrated Land Use-Transport(LILT) model. This model has been used to make forecasts for the city of Leeds forthe period 1976 to 1991. A base forecast representing the 'most likely future' wasmade against which the policies were tested. Even with travel costs constant in realterms, the model predicted a continuation of the trends of rising car ownership anddecentralisation. The model predicted that increasing the price of fuel would slowdown the decentralisation of jobs, while increasing bus fares would accelerate theprocess. Making buses free would encourage the growth of jobs in the city centre,especially retail and service activity. The introduction of city centre restraint interms of a very high parking charge would accelerate the demise of the city centre andencourage suburban development.

Of the four policies designed to assist the inner city, only the one making shortbus trips cheaper was estimated to accelerate the outward movement of jobs. Theother three policies, namely, free parking, introducing better cross-town busservices and improving road access were all estimated to slow down the decline in thenumber of jobs in the inner city. All the policies had a variety of side-effects whichare discussed in the réport. .

(Author)

NAKAMURA, H., HAYASHI, Y., and MIYAMOTO, K., 1983, Land use-transportationanalysis system for a Metropolitan area, Transportation Research Record, 931,pp. 11-20.

Results are reported of a study conducted to develop a land use-transportanalysis system that will be useful for assessing impacts of transport improvements.The study consists of two major parts. The purpose of the first part is to developmodels that adequately describe the locational behaviour of land uses and conseq-uently forecast future land use patterns. The purpose of the second part is to developa computer-aided analysis system that makes it possible to manage vast amounts ofspatial data and to create an easy-to-use system to manage a complex array ofintegrated programs by man-machine interactive methods. The land use—transportmodel has a hierarchic structure that first allocates land use demand into city-sizezones and then into 1 km2 grids. The allocation model for the zone level has a Lowry-type structure, but each submodel for industrial, business, and residential use isbased on its own locational behaviour. The allocation model for the grid leveldescribes competition among land uses under constraints of zoning regulationsaccording to the principle of maximization of locational surplus. Transportconditions are determined by estimating trips generated from new locations. Thelocation of land uses and transport conditions interact in the model. The computer-aided system contains a data base system for data processing of land use—transportanalysis as well as an interactive operation system that uses computer graphics andhierarchic menu for programme execution. To illustrate this sytem, future changesof land use and transport in the Tokyo metropolitan area due to the proposed TokyoBay Bridge are forecast.

(Authors)

PUTMAN, S. H., 1983, Integrated Urban Models (London: Pion).

This book summarizes the research which Putman has carried out over the lastten years in Philadelphia. The first part of the volume reviews both theoretical andempirical evidence on residential-location .models, employment-location modelsand transport network assignment models. This forms the context against whichintegrated land use-transport models can be developed. The stages of modelformulation and initial testing are presented (with the case study of the SanFrancisco Bay Area), then the modifications and alternatives are covered togetherwith extensive testing of the different functional forms. Finally all the stages outlinedare brought together into an integrated chapter on the integrated model. Severaldifferent policies are examined, including changes in the transport system due toconstruction as well as to energy cost changes, toll changes and user changes. Thespatial consequences of regional growth or decline are also covered. The conclusionsto the book suggest further model developments such as the formulation of dynamicmodels and system behaviour, and there is a comment on the place of this type ofmathematical research in modern planning practice and society.

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