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Simultaneous location of firms and jobs in a transport and land use model

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  • Hensher, David A.
  • Ho, Chinh Q.
  • Ellison, Richard B.

Abstract

The majority of transport planning model systems used in forecasting travel demand as input into project evaluation treat the location of firms as a given, and hence the best these model systems can do in terms of assessing the response of firms to improvements in transport systems is to redistribute the number of existing workers (as a proxy for the number of jobs) between zonal locations, holding the total number of jobs and firms constant (with the exception of an overall growth in jobs). Some exceptions assume an inducement response based on simple rules of firm response as a way of approximating induced trip demand attributable to the change in the number of firms. What is typically missing is an endogenous treatment of firm location choice through a formal model that is integrated into a transport and land use model system in a way that allows for feedback between travel and firm location. This integration is critical to capture the change in the total amount of business activity in each location and overall, that is not simply based on an-across-the-board assumption on the annual growth in jobs. This paper presents a jointly estimated aggregate zonal model of the number of firms and jobs in which the number of jobs is influenced by the number of firms at a specific location, together with elasticity estimates of each influencing effect in each industry sector. The employment model predictions for each industry sector can be input into a workplace location choice model in any integrated transport and location model system to obtain predictions of the impact of transport policies on the location decisions of firms. The Sydney Greater Metropolitan Area (SGMA) is used as a case study to estimate the systems of firm and employment location models.

Suggested Citation

  • Hensher, David A. & Ho, Chinh Q. & Ellison, Richard B., 2019. "Simultaneous location of firms and jobs in a transport and land use model," Journal of Transport Geography, Elsevier, vol. 75(C), pages 110-121.
    • Handle: RePEc:eee:jotrge:v:75:y:2019:i:c:p:110-121
    DOI: 10.1016/j.jtrangeo.2019.02.001
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    References listed on IDEAS

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    Cited by:

    1. Samani, Ali Riahi & Mishra, Sabyasachee & Golias, Mihalis & Lee, David J.-H., 2023. "What influences the location choice of establishments? An analysis considering establishment types and activities interactions," Journal of Transport Geography, Elsevier, vol. 111(C).
    2. Roy Cerqueti & Eleonora Cutrini, 2021. "A Framework for Modelling Economic Regional Location Processes Under Uncertainty," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 19(4), pages 703-725, December.
    3. Balbontin, Camila & Hensher, David A. & Ho, Chinh, 2023. "Light commercial vehicles destination choice: Understanding preferences relative to the number of stop and tour-based trip type," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    4. Ahmed, Usman & Hawkins, Jason & Roorda, Matthew J., 2022. "Establishment location choice model considering intra-firm interactions," Journal of Transport Geography, Elsevier, vol. 102(C).
    5. David A. Hensher & Chinh Quoc Ho & Wen Liu & Edward Wei & Richard Ellison & Kyle Schroeckenthaler & Derek Cutler & Glen Weisbrod, 2020. "MetroScan: A Quick Scan Appraisal Capability to Identify Value Adding Sustainable Transport Initiatives," Sustainability, MDPI, vol. 12(19), pages 1-30, September.
    6. Hensher, David A. & Wei, Edward & Liu, Wen, 2021. "Battery electric vehicles in cities: Measurement of some impacts on traffic and government revenue recovery," Journal of Transport Geography, Elsevier, vol. 94(C).

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