Handbook of regional science


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Handbook of regional science

  1. 1. 1SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013Land-Use Transport Interaction ModelsAbstractThe relationship between urban development and transport is not simple and one way but complex and two way and isclosely linked to other urban processes, such as macroeconomic development, interregional migration, ,demographyhousehold formation, and technological innovation. In this chapter, one segment of this complex relationship is discussed:the two-way interaction between urban land use and transport within urban regions. The chapter looks at integratedmodels of urban land use and transport, i.e., models that explicitly model the two-way interaction between land use andtransport to forecast the likely impacts of land use and transport policies for decision support in . Theurban planningdiscussion starts with a review of the main theories of land-use transport interaction from transport planning, urbaneconomics, and social geography. It then gives a brief overview of selected current operational urban models, therebydistinguishing between spatial-interaction location models and accessibility-based location models, and discusses theiradvantages and problems. Next, it reports on two important current debates about model design: are equilibrium modelsor dynamic models preferable, and what is the most appropriate level of spatial resolution and substantivedisaggregation? This chapter closes with a reflection of new challenges for integrated urban models likely to come up inthe future.IntroductionThe history of urban settlements is closely linked to transport. Cities appeared in human history when technologicalinnovation required the spatial between specialized crafts and agricultural labor and gave rise todivision of laborurban–rural travel and goods transport. Cities were established at trade routes, ports, or river crossings and becameorigins and destinations of trade flows. Cities were compact, as all movements were done on foot, until the railway andlater the automobile opened the way to today’s sprawling agglomerations.These brief notes already show that the relationship between urban development and transport is not simple and one waybut complex and two way. On the one hand, spatial division of labor, i.e., the separation of locations of human activities inspace, requires , i.e., travel and goods transport. On the other hand, the availability of transportspatial interactioninfrastructure, such as roads, railways, and airlines, makes locations attractive as residences or business locations and soaffects real estate markets and the choice of location of households and firms. Moreover, it becomes clear that therelationship between urban development and transport is closely linked to other urban processes, such asmacroeconomic development, interregional migration, demography and household formation, and technologicalinnovation.In this chapter, one segment of the complex relationship between urban development and transport is discussed: thetwo-way interaction between urban land use and transport within urban regions. The macroeconomic dimension dealingwith growth or decline of whole cities within urban systems is addressed in several other chapters, such as Interregional, Interregional Trade Models, and .Input–Output Models Urban GrowthThis chapter looks at models of urban land use and transport, i.e., models which explicitly model the two-wayintegratedinteraction between land use and transport to forecast the likely impacts of land-use policies, such as zoning or buildingdensity or height constraints, and of transport policies, such as transport infrastructure investments, public transportimprovements, or taxes or user charges, for decision support in urban planning. That excludes transport models per sewhich predict traffic patterns that result from different land-use configurations and land-use change models that predictlikely land-use changes that result from a particular transport system, as well as models that deal only with one urbansubsystem, such as housing or business location.The discussion proceeds from a review of the main theoretical approaches of land-use transport models and a briefoverview of operational models to current debates and new challenges that are likely to influence future development inthis field.There are in the literature several reviews of integrated land-use transport models, such as Wegener ( ) and Hunt et2004al. ( ).2005
  2. 2. 2SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013TheoryUrban land-use transport models originated in the United States in the 1960s as part of the diffusion of operationsresearch and systems theory into all fields of society. The first attempts to model the interaction between land use andtransport were initiated by transport planners who felt that predicting future traffic flows without taking account of theirimpacts on location was inadequate. Hansen ( ) showed for Washington, DC, that locations with good accessibility1959had a higher chance of being developed, and at a higher density, than remote locations (“how accessibility shapes landuse”). The recognition that mobility and location decisions co-determine each other and that therefore transport andland-use planning need to be coordinated led to the notion of the “land-use transport feedback cycle”. The set ofrelationships implied by this term can be summarized as follows (Wegener and Fürst , see Fig. 1):1999The distribution of , such as residential, industrial, or commercial, over the urban area determines theland useslocations of households and firms and so the locations of human such as living, working, shopping,activitieseducation, and leisure.The distribution of human in space requires spatial interactions or trips in the toactivities transport systemovercome the distance between the locations of activities.These spatial interactions are based on decisions of travelers about car availability, number of trips, destination,mode, and route. They result in and, in case of congestion, in increased travel times, trip lengths, andtraffic flowstravel costs.Travel times, trip lengths, and travel costs create opportunities for spatial interactions that can be measured as.accessibilityThe spatial distribution of accessibility influences, among other attractiveness indicators, location decisions ofinvestors and results in changes of the by demolition, upgrading, or new construction.building stockThese changes in building supply determine location and relocation decisions of households and firms and thusthe distribution of in space.activitiesFig. 1 The land-use transport feedback cycle (Wegener and Fürst , 6)1999This simple explanation pattern is used in many engineering-based and human-geography urban development theories.These start from origins and destinations, such as workers and workplaces, and from these infer trip volumes that bestreproduce observed trip frequency distributions. It had already been observed by Ravenstein ( ) and Zipf ( ) that1885 1949the frequency of human interactions, such as messages, trips, or migrations between two locations (cities or regions), isproportional to their size but inversely proportional to their distance. The analogy to the law of gravitation in physics isobvious.
  3. 3. 3SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013The gravity model was the first model. Its physical analogy has later been replaced by better foundedspatial-interactionformulations derived from statistical mechanics (Wilson ) or information theory (Snickars and Weibull ). Only1967 1977later did it become possible (Anas ) to link it via random (Domencich and McFadden ) to1983 utility theory 1975psychological models of human decision behavior.From the spatial-interaction model, it is only a small step to its application as a location model. If it is possible to drawconclusions from the spatial distribution of human activities to the interactions between them, it must also be possible toidentify the location of activities giving rise to a certain trip pattern. Wilson ( ) distinguishes four types of urban1970models: unconstrained models, production-constrained models, attraction-constrained models,spatial-interaction locationand doubly constrained models. Unconstrained models deal with households without fixed residence or workplace,production-constrained models with households looking for a job, and attraction-constrained models with householdslooking for a residence. The doubly constrained model is actually not a location model but the familiar transport model(see Travel Behavior and Travel Demand).To give an example, the production-constrained spatial-interaction model is written as follows:(1)(2)(3)where are trips between zone and zone , are trips generated by and trips attracted by , and is the travelTiji j Oii Djj cijtime or travel cost, or both, between and . The is a parameter indicating the sensitivity to travel cost; because of itsi j βnegative sign, more distant destinations are less likely to be selected. is the so-called balancing factor ensuring thatAitotal trips equal , and is the probability that a trip goes from to .Oipiji jA second set of theories focuses on the foundations of land use. A fundamental assumption of all spatialeconomiceconomic theories is that locations with good accessibility are more attractive and have a higher thanmarket valueperipheral locations. This assumption goes back to von Thünen ( ) and has since been varied and refined in many1826ways (see Classical Contributions). Probably the most influential example of the latter kind is the model of the urban landmarket by Alonso ( ). The basic assumption of the Alonso model is that firms and households choose that location at1964which their bid rent, i.e., the land price they are willing to pay, equals the asking rent of the landlord, so that the landmarket is in equilibrium. The bid rent of firms results from the cost structure of their , i.e., sales priceproduction functionminus production and transport costs plus profit divided by size of land (see Location and Land Use Theory). A firmhaving a higher per unit of land is therefore able to pay a higher price than a firm with less intensive landadded valueutilization, everything else being equal. So it is not surprising that, say, jewelers are found in the center, whereas truckingcompanies have their yards on the periphery. Alonso’s model has been the point of departure for a multitude ofurban-economics model approaches. In more advanced variations of the model, restrictive assumptions, such as themonocentric city or perfect competition and complete information, have been relaxed (e.g., Anas ).1982A third group of theories used in land-use transport models are theories. In social theories of urban development,socialthe spatial development of cities is the result of individual or collective of space. Based on an adaptation ofappropriationevolutionist thoughts from philosophy (Spencer) and biology (Darwin), the Chicago school of urban sociologistsinterpreted the city as a multispecies ecosystem, in which social and economic groups fight for ecological positions.Appropriation of space takes place in the form of immigration of different ethnic or income groups or tertiary activities intoresidential neighborhoods, and concepts of animal and plant ecology, such as “invasion,” “succession,” or “dominance,”are used to describe the phases of such displacement.Social geography theories go beyond the macro perspective of social ecology by referring to age-, gender-, orsocial-group specific activity patterns which lead to characteristic spatiotemporal behavior and hence to permanentlocalizations. Action space analyses (e.g., Chapin and Weiss ) identify the frequency of performance of activities1968reconstructed from daily space-time protocols as a function of distance to other activities and draw conclusions from this
  4. 4. 4SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013for the most probable allocation of housing, workplaces, shopping, and recreation facilities or, in other words, for the mostlikely level of spatial in cities.division of laborHägerstrand ( ) made these ideas operational by the introduction of “time budgets,” in which individuals, according to1970their social role, income, and level of technology (e.g., car ownership), command action spaces of different size andduration subject to three types of constraints: (i) , i.e., personal, nonspatial restrictions on mobility,capacity constraintssuch as monetary budgets, time budgets, availability of transport modes, and ability to use them; (ii) ,coupling constraintsi.e., restrictions on the coupling of activities by location and time schedules of facilities and other individuals; and (iii), i.e., restrictions of access to facilities by public or private regulations such as property, openinginstitutional constraintshours, entrance fees, or prices. Only locations within the action spaces can be considered as destinations or permanentlocations.On the basis of Hägerstrand’s action space theory, Zahavi ( ) proposed the hypothesis that individuals in their daily1974mobility decisions do not, as the conventional theory of travel behavior assumes, travel time or travel costminimizeneeded to perform a given set of activities but instead activities or opportunities that can be reached within theirmaximizetravel time and money budgets.Operational ModelsLowry’s ( ) was the first attempt to quantify the land-use transport feedback cycle in one1964 Model of Metropolisintegrated model. The model consists of two singly constrained spatial-interaction location models, a residential locationmodel and a service and retail employment location model, nested into each other. In modern notation, the two modelswould be written as(4)(5)where are work trips between residential zone and work zone and shopping trips between residential zone toTiji j Sijiretail facilities in zone . are workers in and population in to be distributed, and are dwellings in andj Ejj Pii Rii Wjshopping facilities in used as destinations in the two spatial-interaction models, and is the travel time between and .j ciji jIn the first iteration, only work trips to the workplaces of basic industries, i.e., industries exporting to other regions and notserving the local population, are modeled. The two spatial-interaction location models are linked by assumptions abouthow many people are supported by one worker and how many retail employees are supported by one resident. In eachsubsequent iteration, workers and residents are updated until they no longer change, i.e., until the system is inequilibrium.The Lowry model stimulated a large number of increasingly complex land-use transport models in the USA and not muchlater also in Europe. Many of these early models were not successful because of unexpected difficulties of data collectionand calibration and the still imperfect computer technology of the time. More important, however, was that the modelswere mainly oriented toward and the efficiency of the transport system and had nothing to say about theurban growthethnic and social conflicts arising in US cities at that time. Moreover, the models were committed to the paradigm ofsynoptic rationalism in planning theory, which was increasingly replaced by incremental, participatory forms of planning.In his “Requiem for Large Scale Models,” Lee ( ) accused the models of “seven sins”: hypercomprehensiveness,1973grossness, mechanicalness, expensiveness, hungriness, wrongheadedness, and complicatedness.But many of the technical problems of the early models were solved by better and faster computers. Thedata availabilityspatial and substantial resolution of the models was increased, and they were based on better theories, such as bid-renttheory (see Location and Land Use Theory), discrete (see Spatial Choice Modeling), and user equilibrium inchoice theorytransport networks (see Network Equilibrium for Urban Transport). In addition, better visualization techniques made theresults of the models better understood by citizens and policy makers. A new generation of models paid more attention toaspects of social equity.
  5. 5. 5SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013The 1990s brought a revival in the development of urban land-use transport models. New environmental legislation in theUSA required that cities applying for for transport investments demonstrate the likely impacts of theirfederal fundsprojects on land use. This had the effect that virtually all major metropolitan areas in the USA maintained an integratedland-use transport model. In Europe, the European Commission initiated a large research program ,The City of Tomorrowin which integrated land-use transport models were applied in several research projects (Marshall and ).Banister 2007Several integrated land-use transport models were applied in a growing number of metropolitan areas. Newdevelopments in data availability brought about by geographical information systems and further advances in(GIS)computer technology have removed former technical barriers.It is impossible to present here all operational integrated land-use transport models existing in the world today. Instead aclassification of models by the way they implement the feedback from transport to land use is proposed using a fewexamples, recognizing that in each group, there exists a great variety of approaches.Spatial-Interaction Location ModelsSpatial-interaction locations models retain the original Lowry concept by modeling the location of human activities asdestinations of trips using the production-constrained spatial-interaction model. The most prominent urban model of thiskind still operational today is the MEPLAN model developed by Echenique ( ) as well as its offsprings, TRANUS (de1985la Barra ) and PECAS (Hunt and Abraham ). All three models use a multi-industry, multiregional input–output1989 2005framework (see ) to predict the locations of production and consumption in the urbanInterregional Input–Output Modelsregion, where households of different types are treated as industries producing labor and consuming commodities. Byiterating between the land-use parts and the transport parts of the models, between transport costsgeneral equilibrium(including congestion) and land and commodity prices is achieved. The core equation of MEPLAN is(6)where are deliveries of industry from region to region , is the supply of goods of industry in and theXirsi r s Xiri r Zirdemand for such products in , and are unit production costs of such products in and their unit transport costss cirr girsfrom to . is the balancing factor as in Eq. (1) ensuring that total trade flows from region equal production in .r s Airr rThe great advantage of spatial-interaction location models is their firm foundation in economic theory with respect toproduction and consumption. One possible criticism is that households are treated as industries producing labor andconsuming commodities, with the consequence that residential location solely depends on workplace location, as ifworkers decided where to live on their way back from work.In his most recent model RELU-TRAN, Anas reverses the causal direction of the input–output framework by modeling thelocation choice of consumers (households), producers (firms), landlords, and developers separately by utility-basedproduction functions which include for households the costs of budget-constrained trips and for firms interindustry links asgenerated by the transport part of the model. As in the input–output models, by iterating between the land-use andtransport parts of the equilibrium between land use and transport is achieved (Anas and Liu ).model, general 2007Accessibility-Based Location ModelsThe second group of land-use transport models predicts not actual spatial interactions but the opportunity for spatialinteractions at potential locations. The indicator of opportunity for spatial interactions is called accessibility. Accessibilityindicators can take a wide range of forms, from simple accessibility indicators, such as distance to the nearest bus stationor motorway exit, to complex indicators measuring the ease of reaching all destinations of interest. The most frequentlyused complex accessibility indicator is potential accessibility or the total of all destinations of interest weighted by aninverse function of the effort to reach them measured in time or cost or a combination of both as “generalized cost”:(7)where is the potential accessibility of zone with respect to destinations of interest and is the generalized costs ofAii Djcijtravel between and . The inverse similarity with the balancing factor of Eq. (2) is obvious.i jExamples of operational accessibility-based location models in use today are IRPUD (Wegener ), RURBAN1982
  6. 6. 6SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013(Miyamoto and Udomsri ), MUSSA (Martinez ), DELTA (Simmonds ), and UrbanSim (Waddell ).1996 1996 1999 2002These models predict location choices of households and firms with discrete choice models using multi-attribute utilityfunctions in which accessibility indicators are combined with other attributes of potential locations to indicate theirattractiveness from the point of view of households looking for a residential location or firms looking for a businesslocation. In that respect, these models build on the bid-rent approach of Alonso ( ), although equilibrium between1964asking rents and bid rents on the land market is achieved only in MUSSA, whereas the other three models keep landprices fixed during a simulation period and defer the price response of landlords to the next simulation period.As an example of accessibility-based location choice, the allocation of housing demand to vacant residential land by amultinomial in the IRPUD model is shown (Wegener ):logit model 2011a(8)where ( , + 1) are new dwellings of type developers plan to build in the whole region between time and + 1, ( ,Ckt t k t t Cklit t+ 1) are dwellings of that type that will be built on land-use category in zone in that period, and is the capacity ofl i Lklivacant land for such dwellings given zoning and building density and height constraints. The parameters indicate theβkselectivity of developers with respect to the attractiveness ( ) of land-use category in zone for dwellings of housinguklit l itype :k(9)where ( ) is the attractiveness of zone as a location for housing type , ( ) is the attractiveness of land-use categoryukit i k ukltfor housing type , and u( )( ) is the attractiveness of the land price of land use category in zone in relation to thel k cklit l iexpected rent or price of the dwelling. The , , and 1 − − are multiplicative weights adding up to unity. The zonalvkwkvkwkattractiveness ( ) is multi-attribute and contains, besides other indicators of neighborhood quality, one or more types ofukitaccessibility indicators.The advantage of accessibility-based location models is that by inserting different types of accessibility indicators into theutility functions of different types of locators, the great diversity of accessibility needs reflecting different lifestyles andpreferences of households and different communication and transport needs of firms can be considered. Theirdisadvantage is that the actual travel and transport behavior, and hence actual travel times and transport cost, becomeknown only in the next iteration of the associated transport model, but this may be acceptable because they change overtime only gradually. The separation of the land-use and transport parts of the model by the accessibility interface makes iteasier to develop custom-tailored submodels of the location behavior of individual groups of actors, such as householdslooking for a dwelling, landlords looking for a tenant, developers considering upgrading of their housing stock or lookingfor vacant land for new residential buildings, or firms looking for vacant floorspace or for land to build new floorspace.This has important implications for the software organization of the models. While spatial-interaction location models asdescribed in the previous section tend to be “unified,” i.e., to consist of one single complex algorithm designed to achieve, the accessibility-based models described in this section tend to be “composite,” i.e., to consist ofgeneral equilibriumseveral interlinked modules each serving a specific purpose, modeling the behavior of a particular group of actors andusing the accessibility indicators most appropriate for that.Current DebatesThe urban models sketched so far represent the main model types coexisting until the end of the 1990s. However, fromthen on, the urban modeling scene has become increasingly fragmented along two dividing lines. The first divide runsbetween equilibrium modeling approaches and models that attempt to capture the dynamics of urban processes. Thesecond more recent divide runs between aggregate macro-analytic approaches and new microscopic agent-basedmodels.Equilibrium or Dynamics
  7. 7. 7SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013The first urban models were static equilibrium models, such as the Lowry model which generated an “instant metropolis”at a point in time in the future. This tradition was maintained and is still strong in urban-economics models based on thenotion that all markets, including urban housing, real estate, and transport markets, tend to move toward equilibriumbetween demand and supply and that therefore the equilibrium state is the most appropriate guidance for .urban planningIn contrast to this view, a different movement in urban modeling has become more interested in the adjustment processesgoing on in cities that may lead to equilibrium but more frequently do not. The proponents of this movement, influenced bysystems theory and complexity theory, argue that cities have evolved over a long time and display a strong inertia whichresists sudden changes toward a desired optimum or equilibrium (see ).Spatial Dynamics and Space-Time Data AnalysisFollowing this view, urban change processes can be classified as slow, medium speed, and fast (Wegener et al. ):1986Slow Processes: . Urban transport, communications, and utility are the most permanentConstruction networkselements of the physical structure of cities. The distribution is equally stable; it changes onlyland-useincrementally. have a of up to 100 years and take several years from planning to completion.Buildings life-spanMedium-Speed Processes: . The most significant kind ofEconomic, Demographic, and Technological Changechange are changes in the number and sectoral composition of employment. changeseconomic Demographicaffect population through births, ageing, and death and households through household formation and dissolution.change affects all aspects of urban life, in particular transport and communication. These changesTechnologicaldo not affect the physical structure of the city but the way it is used.Fast Processes: . There are even more rapid processes that are planned and completed in less than aMobilityyear’s time. They refer to the mobility of people, goods, and information within and between given buildings andcommunication facilities. These changes range from job relocations and residential moves to the daily pattern oftrips and messages.The advocates of dynamic models argue that in order to make realistic forecasts, it is necessary to explicitly take accountof the different speeds of processes. In particular, they criticize the implicit assumption of spatial-interaction locationmodels that households and firms are perfectly elastic in their location behavior and change to the equilibrium spatialconfiguration as if there were no transaction costs of moving.In contrast, dynamic urban models make the evolution of the urban system through time explicit. Early dynamic urbanmodels (Harris and Wilson ; Allen et al. ) treated time as a continuum. Today the most common form are1978 1981recursive or quasi-dynamic models in which the end state of one simulation period serves as the initial state of thesubsequent period. The length of the simulation period, usually 1year, is the implicit time lag of the model, as changesoccurring in one simulation period affect other changes only in the next simulation period. By using results from earliersimulation periods, the modeler can implement longer delays and feedbacks. For instance, if it is assumed that it typicallytakes 3 years to plan and build a house, a delay of 3 years between residential investment decisions and the newdwellings appearing on the market would be appropriate. Similar delays between investment decision and completionallow to model the typical cycles of over- and undersupply of office space.Most current dynamic urban models are composite models, i.e., operate with a combination of custom-tailored submodelsfor different urban change processes. By selecting the sequence in which these submodels are processed during asimulation period, the modeler can give certain processes priority access to scarce resources. It is no coincidence thatmost dynamic land-use models are accessibility-based location models, i.e., use accessibility indicators as link betweentransport and land use and so take advantage of the possibility to select different types of accessibility for different typesof development.Most existing equilibrium urban models, however, are unified, i.e., apply one algorithm to all its parts, such asspatial-interaction location in the case of MEPLAN, TRANUS, and PECAS, or bid-rent location in the case of MUSSA,because they aim at between supply and demand, which is easier to achieve in a unified model.general equilibriumHowever, the growing success of dynamic or quasi-dynamic models has had its effects on equilibrium models. Somespatial-interaction location models, such as MEPLAN and PECAS, have been made recursive, i.e., they are processednot only for a distant target year but for years in between and have been complemented by developer submodelsproducing residential, commercial, and industrial floorspace that serve as constraints for the allocation of households andeconomic activity in the equilibration of the subsequent simulation period.Macro or Micro
  8. 8. 8SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013The second major divide appearing in the urban modeling scene concerns the debate about the most appropriate level ofspatial and substantive disaggregation.The first urban models were zone-based like the travel models of the time, as the data required by both types of modelswere available only for relatively large statistical areas. However, in the 1990s, the growth in computing power and theavailability of GIS-based disaggregate data fuelled by non-modeling applications, such as data capture, mapping, spatial, and visualization, has had its impact on urban modeling. New modeling techniques, such as cellular automataanalysis(CA) and agent-based models developed and applied in the environmental sciences, were proposed for modelingland-use changes of high-resolution (see ). In transport planning,grid cells Cellular Automata and Agent-Based Modelsactivity-based models modeling no longer trips but activity-related multi-stop tours have become the state of the art (see). The impact of these developments on urban modeling has been a massive and still continuingActivity-Based Analysistrend toward disaggregation to the individual level or .microsimulationThere are important conceptual reasons for microsimulation, such as improved theories and growing knowledge abouthuman cognition, preferences, behavior under uncertainty and constraints, and interactions between individuals inhouseholds, groups, and social networks (see Social Network Analysis), a growing potential for ; theindividualizationchoice of diversified lifestyles and hence mobility and location patterns. Disaggregate models of individual behavior arebetter suited to capture this heterogeneity.Microsimulation was first used in the social sciences by Orcutt et al. ( ). Early applications with a spatial dimension1961covered a wide range of processes, such as spatial diffusion and urban expansion (see ). SinceSpatial Microsimulationthe 1980s, several microsimulation models of urban land use and transport have been developed, such as the pioneeringILUTE (Salvini and Miller ). Stimulated by the technical and conceptual advances discussed above, agent-based2005microsimulation urban models are proliferating all over the world, including microsimulation versions of originallyaggregate models, such as IRPUD, DELTA, and UrbanSim.However, not all disaggregate urban modeling projects have been successful (see, for instance, Wagner and Wegener; Nguyen-Luong ). Many large modeling projects had to reduce their too ambitious targets. The reasons for2007 2008these failures are partly practical, such as large data requirements and long computing times, but partly also conceptual.The most important conceptual problem is the lack of stability of microsimulation models due to stochastic variation.Stochastic variation, also called microsimulation or Monte Carlo error, is the variation in model results between simulationruns with different random number seeds (see ). In agent-based models of choice behavior, theSpatial Microsimulationmagnitude of stochastic variation is a function of the ratio between the number of choices and the number of alternativesand the selectivity of the choosing agents (the parameter in the equations of this chapter). The stochastic variation isβsmall when a large number of agents with clear preferences choose between few alternatives, e.g., travel modes. It islarge when a small number of agents with less pronounced preferences choose between a large number of alternatives,e.g., locations, such as grid cells, parcels, or zones, as in the case of residential or business location. In that case, thestochastic noise may be larger than the differences between competing planning alternatives under investigation, and theresults may convey an illusionary sense of precision (Wegener ).2011bThere are several ways to overcome this dilemma, such as averaging the results to a higher spatial level or to artificiallyincreasing the number of choices in the model. The most frequently recommended method is to run the model severaltimes and to average across the results of the different runs, something rarely done because of the already longcomputation times of models.microsimulationIn conclusion, the microsimulation community has yet to find a proper answer to the stochastic variation problem. Theoptimum level of disaggregation may not be the most disaggregate one. What is needed is a theory of multilevel urbanmodels to identify the appropriate level of conceptual, spatial, and for each modeling task.temporal resolutionFuture ChallengesThe world is changing fast, and so are the problems of . The first land-use transport models wereurban planninggrowth-oriented and mainly addressed technical problems, such as the reduction of urban sprawl and traffic congestion.The second generation of models increasingly considered equity aspects, such as social and ethnic segregation,accessibility of public facilities, and distributive issues, such as who gains and who loses if certain policies areimplemented. Today the third generation of models tries to take account of the observed of lifestyles andindividualizationpreferences by ever greater spatial, temporal, and substantial disaggregation.However, today new challenges are becoming visible that cannot be handled by many of the urban land-use transport
  9. 9. 9SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013models existing today.The first challenge is to extend the models from land-use transport interaction models to land-use transport environmentmodels. Today only few urban models are linked to environmental models to show the impacts of planning policies ongreenhouse gas emissions, air quality, traffic noise, and open space (Lautso et al. ). As environmental submodels2004predicting air quality or noise propagation require high-resolution grid cell data, this model extension may give a new twistto the macro versus micro debate toward multilevel models using different spatial levels with different resolutions andupward and downward feedbacks. Even fewer models are able to model the reverse relationship, the impact ofenvironmental quality, such as air quality or traffic noise, on location.The second challenge is the transition from population growth to population decline already observed and foreseeable inmany European cities. With small population decline and moderate , there is still demand for neweconomic growthhousing because of decreasing household size and increasing floorspace per capita. The same is true for work placesdue to growing floorspace demand per worker. However, if the losses of population and employment become larger thanthe growth in floorspace demand per capita or per worker, the task is no longer the allocation of growth but themanagement of decline by new types of policies, such as rehabilitation of neighborhoods, upgrading of rundown housing,or conversion or demolition of derelict or vacant buildings. Only few current urban models are able to handle this.The third and greatest challenge arises from the possibility of future energy crises and the requirements of climateprotection. Both causes are likely to make mobility significantly more expensive. For model design, it does not matterwhether car trips become more expensive through higher prices of fossil fuels on the world market or through governmentpolicies to meet greenhouse gas reduction targets. What matters is that these targets cannot be achieved withoutrigorous changes in the framework conditions of land use and transport in urban areas, in particular without significantincreases in the price of fossil fuels.Most current urban models are not prepared for this. Many of them are not able to model transport policies, such ascarbon taxes, , road pricing, or alternative vehicles and fuels, or land-use policies, such as strictemissions tradingdevelopment controls, improvement of the energy efficiency of buildings, or decentralized energy generation. Even fewermodels are able to identify population groups or neighborhoods most affected by such policies or possible problems withaccess to basic services, such as schools or health facilities, or participation in social and cultural life in low-densitysuburban or rural areas.Many current transport models cannot correctly predict the impacts of substantial fuel price increases. Many do notconsider travel costs in modeling car ownership, trip generation, trip distribution, and modal choice. Many do not forecastinduced or suppressed trips. Many use price elasticities estimated in times of cheap energy. Many do not considerhousehold budgets for housing and travel.Action space theory with explicit travel time and travel cost budgets permits to predict what will happen if speed and costof travel are changed by environment-oriented planning policies. Acceleration and cost reduction in transport lead tomore, faster, and longer trips; speed limits and higher costs to fewer, slower, and shorter trips. In the , this haslong runeffects on the spatial structure. Longer trips make more dispersed locations and a higher degree of spatial division ofpossible; shorter trips require a better spatial coordination of locations. However, making travel slower and morelaborexpensive does not necessarily lead to a reconcentration of land uses back to the historical city center. In many urbanregions, population has already decentralized so much that further deconcentration of employment would be moreeffective in achieving shorter trips than reconcentration of population.That plausible forecasts of the impacts of substantial energy price increases can be made with land-use transport modelsbased on action space theory was demonstrated by the results of the EU project Scenarios for the Transport System and(STEPs). They show that with appropriate combinations of transport andEnergy Supply and their Potential Effectsland-use policies, significant reductions in greenhouse gas emissions can be achieved without unacceptable loss of(Fiorello et al. ).quality of life 2006ConclusionsAfter half a century of development, there exists today a broad spectrum of mathematical models to predict the spatialevolution of cities subject to exogenous trends and land-use and transport policies. These models build on a range oftheories from transport economics, and social geography to explain the complex two-way interactionplanning, urbanbetween urban land use and transport, i.e., the location of households and firms and the resulting mobility patterns inurban regions subject to concurrent economic, demographic, and technological developments. Stimulated by advances in
  10. 10. 10SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013, theory development and computing technology, these models have reached an impressive level ofdata availabilitysophistication and operational applicability.However, the urban modeling field has recently become divided into camps with different modeling philosophies. Inparticular, two dividing lines are becoming visible: One is the divide between equilibrium approaches which assume thatcities are essentially markets moving toward equilibrium between demand and supply and dynamic approaches focusingon adjustment processes of different speeds. The other is the divide between macro approaches dealing with statisticalaggregates at the level of zones and micro approaches modeling individual households and firms at the level of grid cellsor parcels. In each of the two debates, the advantages and disadvantages of the competing approaches are obvious, butwhat is missing is an open and honest assessment of their relevance for the validity and robustness of the results of themodels. Collaborative research projects in which different models are applied to identical problems and their resultscompared by meta-analyses are still the exception.A second issue regarding the future of urban models is the new challenges for urban planning. The growing importance ofenvironmental impacts of land-use and transport policies has not yet fully been embraced by most urban models. Neitherhas the transition from population growth to population decline already observed or foreseeable in many cities, a greatchallenge for some models originally designed for allocating growth. But the greatest challenge for urban models will behow to cope with the combined effects of future energy and the imperatives of climate change. During and afterscarcitythe energy transition, energy for transport and building heating will no longer be abundant and cheap but scarce andexpensive. This will have fundamental consequences for mobility and location. Land-use transport models which arecalibrated on behavior observed in times of cheap energy and do not consider the costs of travel and location in relationto household income cannot adequately forecast these consequences. To deal with significantly rising energy costs,land-use transport models must consider the basic needs of households which can be assumed to remain relativelyconstant over time, such as shelter and security at home, accessibility of work, education, retail and necessary services,and the constraints on housing and travel expenditures by disposable household incomes.To avoid the danger that the models, as in the 1970s, are again rejected by the planning practice, they must give up somelong-standing traditions and be prepared to adopt new modeling principles: less of past trends but moreextrapolationopenness to fundamental change, less reliance on observed behavior but more theory on needs, less consideration ofpreferences and choices but more taking account of constraints, and less effort on detail but more focus on basicessentials.ReferencesAllen PM, Sanglier M, Boon F (1981) Models of urban settlement and structure as self-organizing systems. USDepartment of Transportation, Washington, DCAlonso W (1964) Location and land use. Harvard University Press, Cambridge, MAAnas A (1982) Residential location models and urban transportation: economic theory, econometrics, and policyanalysis with discrete choice models. Academic, New YorkAnas A (1983) Discrete choice theory, information theory and the multinomial logit and gravity models.Transportation Res B 17(1):13–23Anas A, Liu Y (2007) A regional economy, land use and transportation model (RELU-TRAN): formulation,algorithm design and testing. J Regional Sci 47(3):415–455Chapin FS, Weiss SF (1968) A probabilistic model for residential growth. Transportation Res 2(4):375–390de la Barra T (1989) Integrated land use and transport modelling. Cambridge University Press, CambridgeDomencich TA, McFadden D (1975) Urban travel demand: a behavioral analysis. North Holland, AmsterdamEchenique MH (1985) The use of integrated land use transportation planning models: the cases of Sao Paulo,Brazil and Bilbao, Spain. In: Florian M (ed) The practice of transportation planning. Elsevier, The Hague, pp263–286Fiorello D, Huismans G, López E, Marques C, Monzon A, Nuijten A, Steenberghen T, Wegener M, Zografos G(2006) Transport strategies under the scarcity of energy supply. STEPs Final report. Buck ConsultantsInternational, The HagueHägerstrand T (1970) What about people in regional science? Pap Reg Sci Assoc 24(1):7–21Hansen WG (1959) How accessibility shapes land use. J Am Inst Plann 25(2):73–76Harris B, Wilson AG (1978) Equilibrium values and dynamics of attractiveness terms in production-constrained
  11. 11. 11SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013spatial-interaction models. Environ Plann A 10(4):371–388Hunt JD, Abraham JE (2005) Design and implementation of PECAS: a generalised system for the allocation ofeconomic production, exchange and consumption quantities. In: Lee-Gosselin MEH, Doherty ST (eds) Integratedland-use and transportation models: behavioural foundations. Elsevier, St. Louis, pp 253–274Hunt JD, Kriger DS, Miller EJ (2005) Current operational urban land-use transport modeling frameworks: a review.Transport Rev 25(3):329–376Lautso K, Spiekermann K, Wegener M, Sheppard I, Steadman P, Martino A, Domingo R, Gayda S (2004)PROPOLIS: planning and research of policies for land use and transport for increasing urban sustainability.PROPOLIS final report. LT Consultants, HelsinkiLee DB (1973) Requiem for large-scale models. J Am Inst Plann 39(3):163–178Lowry IS (1964) A model of metropolis. RM-4035-RC. Rand Corporation, Santa MonicaMarshall S, Banister D (eds) (2007) Land use and transport. European research towards integrated policies.Elsevier, LondonMartinez FJ (1996) MUSSA: land use model for Santiago City. Transportation Res Rec 1552/1996:126–134Miyamoto K, Udomsri R (1996) An analysis system for integrated policy measures regarding land use, transportand the environment in a metropolis. In: Hayashi Y, Roy J (eds) Transport, land use and the environment. Kluwer,Dordrecht, pp 259–280Nguyen-Luong D (2008) An integrated land-use transport model for the Paris Region (SIMAURIF): ten lessonslearned after four years of development. IAURIF, Paris.http://mit.edu/11.521/proj08/readings/D_Mes_documentsDNLpredit3ERSA_2008article_SIMAURIF_10_lessons.pdf. Accessed 24 Mar 2012Orcutt G, Greenberger M, Rivlin A, Korbel J (1961) Microanalysis of socioeconomic systems: a simulation study.Harper and Row, New YorkRavenstein EG (1885) The laws of migration. J Stat Soc Lond 48(2):167–235Salvini PA, Miller EJ (2005) ILUTE: an operational prototype of a comprehensive microsimulation model of urbansystems. Network Spatial Econ 5(2):217–234Simmonds DC (1999) The design of the DELTA land-use modelling package. Environ Plann B: Plann Des26(5):665–684Snickars F, Weibull JW (1977) A minimum information principle. Reg Sci Urban Econ 7(1–2):137–168von Thünen JH (1826) Der isolierte Staat in Beziehung auf Landwirtschaft und Nationalökonomie. Perthes,HamburgWaddell P (2002) UrbanSim: modeling urban development for land use, transportation and environmentalplanning. J Am Plann Assoc 68(3):297–314Wagner P, Wegener M (2007) Urban land use, transport and environment models: experiences with an integratedmicroscopic approach. 170:3/2007 45–56disPWegener M (1982) Modeling urban decline: a multilevel economic-demographic model of the Dortmund region. IntReg Sci Rev 7(2):217–241Wegener M (2004) Overview of land-use transport models. In: Hensher DA, Button KJ (eds) Transport geographyand spatial systems. Handbook 5 of handbook in transport. Pergamon/Elsevier Science, Kidlington, pp 127–146Wegener M (2011a) The IRPUD model. Arbeitspapier 11/01. Spiekermann & Wegener Stadt- undRegionalforschung, DortmundWegener M (2011b) From macro to micro – how much micro is too much? Transport Rev 31(2):161–177Wegener M, Fürst F (1999) Land-use transport interaction: state of the art. Berichte aus dem Institut fürRaumplanung 46. Institute of Spatial Planning, University of Dortmund, Dortmund..http://www.raumplanung.uni-dortmund.de/irpud/fileadmin/irpud/content/documents/publications/ber46.pdfAccessed 24 Mar 2012Wegener M, Gnad F, Vannahme M (1986) The time scale of urban change. In: Hutchinson B, Batty M (eds)Advances in urban systems modelling. North Holland, Amsterdam, pp 145–197Wilson AG (1967) A statistical theory of spatial distribution models. Transportation Res 1(3):253–269Wilson AG (1970) Entropy in urban and regional modelling. Pion, LondonZahavi Y (1974) Traveltime budgets and mobility in urban areas. Report FHW PL-8183. US Department ofTransportation, Washington, DC
  12. 12. 12SpringerReferenceDr. Michael WegenerLand-Use Transport Interaction Models5 Feb 2013 17:27http://www.springerreference.com/index/chapterdbid/364111© Springer-Verlag Berlin Heidelberg 2013Zipf GK (1949) Human behaviour and the principle of least effort. Addison Wesley, Cambridge, MALand-Use Transport Interaction ModelsDr. MichaelWegenerSpiekermann &Wegener, Urban and Regional Research, Dortmund,GermanyDOI: 10.1007/SpringerReference_364111URL: http://www.springerreference.com/index/chapterdbid/364111Part of: Handbook of Regional ScienceEditors: -PDF created on: February, 05, 2013 17:27© Springer-Verlag Berlin Heidelberg 2013