Keywords: transport modelling, spatial modelling, ilute

Keywords: transport modelling, spatial modelling, ilute

Keywords: transport modelling, spatial modelling

Keywords: transport modelling, spatial modelling

Keywords: computer science, spatial modelling

Keywords: computer science, spatial modelling, agent-based modelling

Keywords: spatial modelling

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Keywords: geographic information systems, spatial modelling

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Keywords: geographic information systems, spatial modelling

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Keywords: geographic information systems, spatial modelling

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Keywords: geographic information systems, spatial modelling, transport modelling

Keywords: spatial modelling

Keywords: spatial modelling

About activity-based vs. trip-based travel modelling

Keywords: spatial modelling, transport modelling

Keywords: spatial modelling, transport modelling

Keywords: spatial modelling

Keywords: spatial modelling

Keywords: spatial modelling, ilute, canada

Keywords: spatial modelling, ilute, canada, urban planning

Keywords: spatial modelling, ilute, urban planning, land use transport link

Keywords: spatial modelling

Dismisses Lee73. Mostly critical of the tone of the article, and the divisions it produced in the planning community, divorcing planning from modelling for a long period. Claims are mostly about Lee's rhetoric, the authorities he appealed to, and his limited understanding of the models. Comparisons with GIS are unfortunate, since GIS has a much larger market and hence has seen much faster development than transport modelling.

Keywords: spatial modelling, transport modelling

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Keywords: geographic information systems, spatial modelling

This paper describes the development and testing of a microsimulation of the evolution of individual ”business establishments” (BEs) in an economy. The work is part of a larger program of research and development of a model of all the transportation and land development processes in an entire spatial economic system. The simulation uses comparatively simple, yet behavioural, rules and probabilistic models, using a Monte Carlo process to simulate behaviour from the probabilistic models. A BE is described primarily by its business transactions - its purchases and sales of standard commodity categories, called its “consumption function” and “production function” respectively. Make and Use tables from traditional input-output models are used to determine these relationships for a particular industry, and individual BEs randomly vary around the industry average. Labour, floorspace and final demand are included as commodities, to bind the BEs to a given built form in a spatial system and to the patterns of population. Thus a BE is described in terms of how big it is, and its “technical coefficients” describing what it purchases and sells.

The market for each commodity type is spatially disaggregated, and BEs in a given location can sell or purchase their commodities in a variety of different “exchange zones” that they are willing to ship goods or services from or to. Prices at exchange zones are adjusted over time so that, if the system is allowed to reach equilibrium, the market for each commodity in each exchange will be cleared. The BE's market choice model is used to develop measures of the attractiveness of selling or purchasing commodities when located in a zone. These measures of commodity attractiveness are used with the production function and consumption function to determine how attractive a location is for a given BE and how well it is performing. A BE's growth (positive and negative) and its probability of bankruptcy (death) are based on the measure of location attractiveness. Relocation pressures are based on the measure of location attractiveness, as well as a composite measure of the attractiveness of all other zones in the system and the (fixed) attractiveness of leaving the model region entirely. Relocating BEs vacate floorspace in a particular physical location (a “grid cell”) and then, if necessary, acquire new floorspace in a grid cell in a different zone. As a successful BE grows it is increasingly likely to split into two separate BEs, either as a duplication of function into another location, or a separation of business functions into separate locations. In addition, entrepreneurial business ideas are set up as “Proto BEs”, which are business ideas that are being evaluated in any one year. A “Proto BE” that is in an attractive location in one year is likely to become an actual BE in the next year. Within each zone, the land is represented as “grid cells”, which are finite quantities of land with a particular type and quantity of floorspace and a particular building age. The prices for each floorspace type in each zone, along with the age, type and quantity of floorspace in each grid cell, are used to calculate the probability that the land owner will choose to undertake development, redevelopment, renovation or demolition in the grid cell. The test system is represented using a 10x10 system of zones and a network of transport connecting the zones with reasonable travel times and costs. This system is used to test the role of the various parameters, to determine reasonable values for the parameters, how the model behaves when parameter values are unreasonable, and how each parameter influences the model system. A set of “policy input” scenarios are also developed, to show how the modelling system can be used to test the policy response. These include decreased development costs, increased travel costs and changed land-use zoning regulations.

Keywords: ilute, spatial modelling

Keywords: spatial modelling

Keywords: urban planning, spatial modelling

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Keywords: geographic information systems, spatial modelling, transport modelling

Keywords: spatial modelling

Some interesting (modern) comments on the problems in urban modelling. The need for more scientific method is discussed, and the need to contribute to theory as well as drawing from theory. Critiques from Lee73: black box method (even modellers don't understand internal workings of models); general purpose nature; command-and-control assumption. For better science, models need: transparency; replicability; and pragmatic evaluation. Travel prices and parking prices should be explicit parts of models. Comprehensive models have only limited value. Urban models compare quite negatively with GIS development over the same period, which suffered from similar shortcomings in the 70s (data and computation constraints), but has flourished since.

Keywords: spatial modelling

Keywords: computer science, spatial modelling

early 2D Model of city

Keywords: urban economics, spatial modelling

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Keywords: geographic information systems, spatial modelling

This paper presents a status report concerning on-going research and development work by a team of Canadian researchers to develop a microsimulation, agent-based, integrated model of urban land use and transportation. It describes in some detail the overall design and current status of the ILUTE (Integrated Land Use, Transportation, Environment) modelling system under development. The overall purpose of ILUTE is to simulate the evolution of an entire urban region over an extended period of time. Such a model is intended to replace conventional, aggregate, static models for the analysis of a broad range of transportation, housing and other urban policies. Agents being simulated in the model include individuals, households and establishments. The model operates on a “100% sample” (i.e., the entire population) of agents which, in the base case, are synthesized from more aggregate data such as census tables and which are then evolved over time by the model. A range of modelling methods are employed within the modelling system to represent individual agents' behaviours, including simple state transition models, random utility choice models, rule-based “computational process” models, and hybrids of these approaches. A major emphasis within ILUTE is the development of microsimulation models of market demand-supply interactions, particularly within the residential and commercial real estate markets. In addition, travel demand is modelled explicitly as the outcome of a combination of household and individual decisions concerning the participation in out-of-home activities over the course of a day. Spatial entities in the model include buildings, residential dwelling units and commercial floorspace, as well as aggregate “spatial containers” such as traffic zones, census tracts or grid cells.

Good references: ConLaw02, VelKapTim00, VosPetDon02.

Their discussion of spatial representation is interesting, and echos (somewhat) my own thoughts on the subject. They have two sections: one on residential representation and one on representations for firms. I'm curious to see how far they've come in the last few months.

They discuss real estate markets, with zonal average prices. Offers can have individual prices, though, overriding zonal averages. It seems that this idea would mesh better with building-based spatial representation-grid based representation makes it hard to store data like “sale price” or compute zonal averages.

I'm a bit baffled by their commercial development model. The grid-based approach they used seems to be based on cellular automata, using logit models for state transitions. But they don't consider adjacency information, which seems like it would be essential for firms-who wants floorspace divided into a random patchwork? Can you really just rearrange floorspace as needed?

Their closing paragraphs are encouraging: they really don't want zones, anywhere.

Keywords: transport modelling, ilute, spatial modelling

Keywords: transport modelling, spatial modelling

The project ILUMASS (Integrated Land-Use Modelling and Transportation System Simulation) aims at embedding a microscopic dynamic simulation model of urban traffic flows into a comprehensive model system incorporating both changes of land use and the resulting changes in transport demand.

The land-use component of ILUMASS will be based on the land-use parts of an existing urban simulation model, but is to be microscopic like the transport parts of ILUMASS. Microsimulation modules will include models of demographic development, household formation, firm lifecycles, residential and non-residential construction, labour mobility on the regional labour market and household mobility on the regional housing market. These modules will be closely linked with the models of daily activity patterns and travel and goods movements modelled in the transport parts of ILUMASS developed by other partners of the project team. The design of the land use model takes into account that the collection of individual micro data (i.e. data which because of their micro location can be associated with individual buildings or small groups of buildings) or the retrieval of individual micro data from administrative registers for planning purposes is neither possible nor, for privacy reasons, desirable. The land use model therefore works with synthetic micro data which can be retrieved from generally accessible public data.

ILUMASS is a group project of institutes of the universities of Aachen, Bamberg, Dortmund, Cologne and Wuppertal under the co-ordination of the Transport Research Institute of the German Aerospace Centre (DLR). Study region for tests and first applications of the model is the urban region of Dortmund. The common database will be compiled in co-operation with the City of Dortmund. After its completion the integrated model is to be used for assessing the impacts of potential transport and land use policies for the new land use plan of the city.

The paper will focus on the land-use parts of the ILUMASS model. It will present the underlying behavioural theories and how they are made operational in the model design, explain how the synthetic population is generated, show first model results and demonstrate the potential usefulness of the model for the planning process.

Interesting. They've adapted the IRPUD land use project for a new integrated model. They do some major rasters (200 000 cells) for some of their lookups, although they're also interested in environmental indicators as well as transport results. They don't operate on a parcel-level due to local privacy legislation; instead they work on a zonal level, combined with a density plot of unknown detail.

Keywords: transport modelling, spatial modelling, land use transport link

Keywords: transport modelling, spatial modelling, urban economics

Keywords: spatial modelling

Keywords: transport modelling, spatial modelling

Spatial analysis and evolution simulation of such complex and dynamic systems as modern urban areas could greatly benefit from the synergy of methods and techniques that constitute the core of the fields of Information Technology and Artificial Intelligence. Additionally, if during the decision making process, a consistent methodology is applied and assisted by a user-friendly interface, premium and pragmatic solution strategies can be tested and evaluated.

In such a framework, this paper presents both a prototype Decision Support System and a consorting spatio-temporal methodology, for modelling urban growth. Its main focus is on the analysis of current trends, the detection of the factors that mostly affect the evolution process and the examination of user-defined hypotheses regarding future states of the problem environment.

According to the approach, a neural network model is formulated for a specific time intervals and each different group of spatial units, mainly based to the degree of their contiguity and spatial interaction. At this stage, fuzzy logic provides a precise image of spatial entities, further exploited in a twofold way. First, for the analysis and interpretation of up-to-date urban evolution and second, for the formulation of a robust spatial simulation model. It should be stressed, however, that the neural network model is not solely used to define future urban images, but also to evaluate the degree of influence that each variable as a significant of problem parameter, contributes to the final result. Thus, the formulation and the analysis of alternative planning scenarios are assisted.

Both the proposed methodological framework and the prototype Decision Support System are utilized during the study of Attica, Greece's principal prefecture and the definition of a twenty-year forecast. The variables considered and projected refer to population data derived from the 1961-1991 censuses and building uses aggregated in ten different categories. The final results are visualised through thematic maps in a GIS environment. Finally, the performance of the methodology is evaluated as well as directions for further improvements and enhancements are outlined.

Keywords: spatial modelling

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Keywords: geographic information systems, spatial modelling, transport modelling

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Keywords: geographic information systems, spatial modelling, transport modelling, equity

Keywords: spatial modelling

Keywords: spatial modelling, computer science

Keywords: spatial modelling, computer science

High-level reflection on CA. Authors discuss relation between theory of CA and practice (bastardization) in urban simulation. See calibration as the biggest current issue, but also feel that many modelers get caught up in modeling and don't contribute back to urban theory.

Keywords: spatial modelling, computer science

Keywords: urban economics, spatial modelling, transport modelling

Keywords: spatial modelling, transport modelling

Keywords: spatial modelling, urban economics