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Using Historical Data to Measure Transportation Infrastructure Constraints on Land Use
Michael J. Demetsky
John S. Miller
John S. Miller
Year: 1998
VTRC No.: 98-R32
Abstract: Conventional practice for developing transportation forecasts is to calibrate a model for base year conditions and then apply the model to identify future deficiencies. These models typically begin with an assumed land use and then project future traffic volumes. To determine limitations on land development as a function of the capability of the transportation system, this research effort reversed that direction, beginning with transportation system characteristics as the independent variable and calculating employment and population as dependent variables. To evaluate this process, a case study area was selected for which transportation planning data were available at three points in time over a 25-year period. This area is Charlottesville, Virginia, with imperfect snapshots of transportation and land use characteristics from 1967, 1979, and 1990. A five-component modeling process was developed and applied to the Charlottesville area for the 1967 base year. This initial approach made intuitive sense, was built from models suggested by the literature, and worked reasonably well on a small theoretical network. The performance of one component, however, was extremely weak and led the authors to develop a direct estimation model instead. This revised technique directly estimates zonal trip ends based on transportation system variables that are influenced by link volumes, roadway types, travel distances, and the geographical position of the zone. Additionally, the authors regressed retail employment, nonretail employment, and population to zonal trip ends. Lessons learned with 1967 data were used to calibrate the model for the 1979 base year and apply it for the 1990 forecast year. For individual zones, errors on the order of50% were obtained, with larger values for retail employment and smaller values for nonretail employment and population. For the aggregate study area, errors between 6% and 21% were obtained. Suggestions about how this model formulation might be interpreted to yield land use limits as a function of traffic volumes are discussed. A simple finding for achieving convergence with the iterative entropy maximization method is stated. Recommendations for using historical data to predict the present, ensuring that these planning data are available for future efforts, and conducting a longitudinal study are presented. Issues associated with linking data from different time periods are explained.