1 Firmography and its application to integrated modeling Rolf Moeckel | Parsons Brinckerhoff Fifth Oregon Symposium on Integrated Land Use and Transport Models Portland State University June 2008

2 Introduction Historic background Employment simulation Example 1: SEAM Example 2: ILUMASS Conclusion Outline

3 Businesses are major players in urban system - There are more jobs as households - About 15 to 20 percent of our daily traffic is business-related, if commuting is included it is almost half of our daily traffic - Use of developable land - Firms generate relevant emissions - Relatively to their VMT, heavy trucks contribute disproportionately to emissions Introduction

4 Historic background Employment simulation today Example 1: SEAM Example 2: ILUMASS Conclusion Outline

5 Von Thünen (1826)

6 Raw material A Raw material B Consumption Production location Alfred Weber (1909)

7 Hotelling (1929) Weber (1909): Small firms that cluster have the same scale advantages as one large firm. I II III IV Market AMarket B Market AMarket B Market AMarket B Market AMarket B Agglomeration effects

8 Alonso (1964)

9 Hansen (1959) Wilson (1967) Accessibility

10 Retailer A Retailer B Probability to be chosen Huff (1963)

11 Early industrial periodPost-industrial period 1. Workforce (Quantity)1. Political conditions 2. Land, Location2. Natural Quality 3. Workforce (Quality) 4. Capital 5. Natural Quality5. Land, Location 6. Political conditions6. Workforce (Quantity) Spitzer 1991 Soft location factors

12 Introduction Historic background Employment simulation Example 1: SEAM Example 2: ILUMASS Conclusion Outline

13 History

14 Allocate jobs in Basic Sector Allocate residential location of employees in Basic Sector Calculate population densities Calculate market areas for Retail Sector Allocate jobs in Retail Sector Allocate residential location of employees in Retail Sector Equilibrium reached? no End yes Start Lowry (1964)

15 INIMP: Industrial Impact Model (Putman 1967) - Lowry model with 29 basic employment types EMPAL: Employment Allocation Model (Putman 1983) - Lowry model with 4 business types Putman (1967/1983)

16 Echenique et al. (1969) - Lowry Model as starting point - Production factors (output) and the needed input of other factors (input) are defined by input-output functions - model iterates until equilibrium is reached MEPLAN

17 IRPUD Model - Whereas the transport model reaches equilibrium, the land use is assumed to react with a time lag of several years. - Mobility rate of firms is given exogenously, relocation is simulated based on location utility by Logit models. IRPUD

18 Landis/Zhang (1998): CUF - Bid-auction approach for land development - Location choice simulated by Logit Models Van Wissen (1999): SIMFIRMS - The first large-scale microsimulation of firms - Simulates relocation and firmography Microsimulation

19 Discrete Choice - Based on discrete choice theory of McFadden - Commonly applies Logit models that are assumed to represent behavior under constraints well - Explicitly introduces limited information Bid-Auction approach - Based on economic theory of Alonso - Prices are immediate result of bid-auction process - Iterates and reaches (almost) an equilibrium - Assumes transparent market Discrete choice versus Bid-auction approach

20 PROs of simulating firms - decision-taking unit is firm - if firm moves it is ensure that all employees relocate - induced relocation due to firm growth can be modeled CONs of simulating firms - is more complex - harder to calibrate due to lack of data and lack of theory - more prone to random effects due to Monte-Carlo sampling Simulation of firms versus simulation of jobs

21 Introduction Historic background Employment simulation Example 1: SEAM Example 2: ILUMASS Conclusion Outline

22 - Iterative Proportional Fitting used to estimate employment - Initial employment estimate based on location utility If floorspace demand in a zone is higher than supply, its utility is reduced by a fixed factor. SEAM: Simple Economic Allocation Model

23 SEAM: Total employment validation

24 Introduction Historic background Employment simulation Example 1: SEAM Example 2: ILUMASS Conclusion Outline

25 This model simulates both population and firms microscopically. The firm model simulates in random order - birth of a new business - growth or decline of a business - closure of a business - business relocation ILUMASS: Events simulated for firms

26 Economic cycles Economic restructuring Employment growth and decline of existing firms Birth and closure of firms 75% 25% 100% possible adjustment Firmography: Growth/Decline and Birth/Death

27 no change8 % growth4 % decline Simulating change of firm size

28 Business Relocation Considers moving? Select a business no More sites? yes End no Start Select an alternative site as an offer Check satisfaction at alternative site Select a site and move business Another business? yes Really wants to move? yes no yes Simulating business relocation

29 Replaceable location factors: Non-replaceable location factors: Simulation Location satisfaction of a business

Business Relocation Example: Finding an premise

31 > 1.2 λ < -1.2 Base scenario

32 Scenarios > 1.2 λ < -1.2 Compact city scenario

33 Scenarios > 1.2 λ < -1.2 Decentralized concentration scenario

34 Introduction Historic background Employment simulation Example 1: SEAM Example 2: ILUMASS Conclusion Outline

35 Simulation of firms contains more uncertainty than simulation of households, because: - Firms are more diverse than households - There is less established theory about firms - Historic events have a stronger impact of employment - Many firms are dependent on world economy - Less data is available for firms There is a large variety for employment simulation, from simplistic to complex. There is no one model that served all purposes, the model choice heavily depends on the individual application. Conclusions

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37 Integrated land-use transport modeling Households Dwellings Person Transport Businesses Premises Freight Transport Accessibility

38 Base Scenario Total Employment Change Scenarios

39 Base Scenario Total Employment Change