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Microsimulation of Businesses in ILUMASS

Part 1: Introduction and Generating Synthetic Businesses

Rolf Moeckel and Michael Wegener

Workshop of the Land Use Transportation Modelling Group University College London, 2 July 2005

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Microsimulation of Businesses in ILUMASS

Introduction

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ILUMASS Project

ILUMASS is an integrated urban model simulating

(a) changes in land use (b) the changes in transport demand (c) the resulting environmental impacts (d) the feedback from changes of accessi-

bility and environment on land use

The aim of ILUMASS is to establish a fully disaggregate urban simulation model.

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Acknowledgement

The partners are

German Aerospace Centre, Berlin (DLR)Technical University of Aachen (ISB)University of Bamberg (IfTP)University of Cologne (ZAIK)University of Wuppertal (LUIS)University of Dortmund (IRPUD)

ILUMASS is funded by the German Federal Ministry for Education and Research.

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ILUMASS Model Overview

Synthetic population- housing- persons- cars

Synthetic firms- floorspace- firms- jobs- vehicles

Transport networks- roads- public transport

Firms- floorspace- firms- jobs

Land use

Person traveldemand - activity programs- week plans

Goods trans-port demand- activities- trips

Transport

Environmental impacts

Emissions- air pollution,- traffic noise at sources

Population- housing- households- persons- cars

Dynamic trafficassignment- network flows

Impacts- air quality,- traffic noise at work

Impacts - air quality- traffic noise at housing

Accessibility- of jobs- of shops- of population- of facilities

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Interaction of Land-Use/Transport and Business Model

Simulation of Businesses

Land Use

Transport

Environmental Impacts

Accessibility

Environm. quality

Floorspace/workforce

Work locations

Transport demand

Emissions

Establishments

Moves

Growths

Shrinkings

Declines

Land Use/Transport Model

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Dortmund

0 10km

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ResidentialMixed UseIndustryAgricultureForest

Land Use 2000

0 10km

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Street Network2000

HighwayMain RoadSide Road

0 10km

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Public Transport2000

TrainSubway/TramBus

0 10km

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Microsimulation of Businesses in ILUMASS

Generating Synthetic Businesses

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Micro Data

Data availability on businesses is extremely poor in Germany!

Business census data exists for the years 1950, 1961, 1970, and 1987.

Monte Carlo Sampling and Iterative Propor-tional Fitting are used to generate micro data.

Spatial disaggregation is performed by Interpolation or Monte Carlo Sampling using ancillary land-use data.

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Iterative Proportional Fitting

Businesses by type and size

Businesses by size

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Monte Carlo Sampling

Select a business type

Select number of employees

Select qualification of employed person

Select micro location

More employees in business?

No

Yes

…

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Types of Businesses

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Agriculture

Mining and quarrying Manufacture of wood, paper and printing

Manufacture of food products, tobacco and textile

Manufacture of machinery and equipment

Manufacture of basic metals and fabricated metal products

Manufacture of furniture, jewellery, musical instruments, games and toys

Manufacture of chemicals, coke and plastic products

Manufacture of other non-metallic mineral products

Construction

Wholesale and retail trade Hotels and restaurants

Transport and communication Health and social work

Financial intermediation Other business activities

Education Sewage and refuse disposal, sanitation

Recreational, cultural and sporting activities

Public administration, public security and fire service

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Micro Data

Multi-establishment firms are treated as several individual businesses.

Features for each synthetic business in-clude:- location (x/y-coordinates), - employees per education level, - job vacancies per education level, - customer capacity,- parking, and- floorspace.

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100 x 100 m Raster Cells

1 km2

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Land Price - Industrial

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Land Price - Mixed Use

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Land Use

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Residential AreaMixed UseLight IndustryHeavy Industry

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Concen-trations

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RetailOfficeAirportPublic institution

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Agricultural Land

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Industrial Floorspace

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Retail Floorspace

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Office Floorspace

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Micro Data

Features for non-residential floorspace in-clude:- location (x/y-coordinates), - floorspace in m²,- quality, and- price.

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