20-Year Traffic Forecasting Factors.
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20-Year Traffic Forecasting Factors.

Filetype[PDF-1.10 MB]

  • English

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      An investigation of the production of 20-year traffic forecasting factors at the South Dakota Department of Transportation (SDDOT) identified the necessary procedural improvements to develop and document a new, well-suited procedure to generate forecasting factors. The primary improvements include more finite traffic analysis zones, incorporation of supplemental data for use as analysis criteria, inherent graphical analysis tools, increased capabilities for making logical factor adjustments, and a final 20-year traffic forecasting factor array table that is greatly expanded over the output of the previous procedure. In-depth interviews of traffic forecasting personnel at state DOTs and reviews of literature relevant to recent trends in traffic forecasting resulted in the accumulation of a variety of material on progressive traffic forecasting techniques and traffic model development. Most notably, the primary traffic forecasting developments are taking place at states with larger and denser populations where sophisticated automated processes, more abundant data, and traffic modeling software are being employed. Less populated rural states are having apparent difficulties implementing advanced traffic forecasting procedures and traffic models that are normally structured with an emphasis on urban perspectives. Conditional differences at rural states and less available resources at rural state DOTs are suggested as the primary factors contributing to this traffic forecasting developmental lag. The investigations of the existing 20-year traffic forecasting procedure at the SDDOT included analysis of traffic data derivations, the sequence of events involved in the procedure, procedural components, automated processes, methodologies to produce forecasting factors, and coordination with Metropolitan Planning Organizations (MPOs) in the state. Analysis findings led to the essential conclusion that implementation of the improved procedure will specifically address the SDDOT’s traffic forecasting needs. Implementation of the improved procedure is supported by additional recommendations that point to better traffic forecasting coordination with MPOs and ways to gain more procedural efficiency through software development. The recommendations have been prepared in expectation of immediate results, as well as the anticipation of long-term benefits from ongoing enhancements to coordinating activities with MPOs and the ultimate migration to a significantly enhanced traffic forecasting model.
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