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Socioeconomic Forecasting Model for the Tri-County Regional Planning Commission

Filetype[PDF-29.72 KB]


English
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  • Resource Type:
    Proceedings
  • Geographical Coverage:
    Michigan
  • Abstract:
    Socioeconomic data is a critical input to transportation planning and travel demand forecasting. Accurate estimates of existing population, incomes, employment and other socioeconomic characteristics are necessary for meaningful calibration of a travel demand forecasting model. Technically sound projections of these same data are essential inputs to applications of the travel models to assess future transportation needs and deficiencies. The Lansing Area Travel Demand Model Calibration project, developed for the Tri-County Regional Planning Commission (TCRPC) in Lansing, Michigan, addressed the need for good socioeconomic data. The socioeconomic forecasting model was developed to support the development of the Regional 2015 Transportation Plan. This model created a process to represent the land use and transportation relationship using analytical allocation procedures and incorporating feedback from local jurisdictions. The socioeconomic modeling approach involved developing forecasts at three levels of geographic detail (by county, by Minor Civil Division and by Traffic Analysis Zone) and an interactive projection, review and refinement process. This project developed procedures to forecast the small area distribution of economic and demographic variables. To balance land use and transportation needs, there is emphasis on managing demand and improving efficiency rather than increasing system supply; on promoting land use patterns which are more conducive to public transportation, and on encouraging more travel by non-motorized modes. The work described here incorporates the interrelation of land use and transportation system characteristics in a simple yet effective way that avoids the problems of more complex land use allocation models. The process to develop a socioeconomic forecasting model resulted in several lessons learned about what worked and what didn?t work. The allocation of socioeconomic forecasts from Regional controls to traffic analysis zones can incorporate both local knowledge, accessibility and develop able land. Ideally, local knowledge would account for most of the near-term forecasts and the analytical procedures would be used for longer-term forecasts. The reliability for the base year data is of paramount importance to the reliability of the forecasts and should be accorded adequate resources to improve the process. Specifically, the employment data was troublesome and may be improved by conducting an employment survey. Finally, the process could be improved by developing and maintaining a GIS monitoring program for land use. 11p.
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