Project level traffic forecasts are the most challenging to provide due to the need to provide highly detailed spatial (such as individual turning movements) and temporal (such as 15 minutes) resolution. The Ohio Department of Transportation (ODOT) in attempting to address this need has found no single source of inaccuracy but rather a web of potential analytical and procedural pit falls. These are being addressed through ten independent efforts that have the potential to produce better fine resolution traffic forecasts in the future. Each of these is listed briefly below.

Conduct an analysis of past traffic forecasts compared to actual counts and attempt to deduce reasons for inaccuracies.

Create an automated tool that combines results from the Ohio Statewide Travel Demand Model (OSTDM) with NCHRP255 adjustments and regression analysis of ODOT’s historical counts database. This tool provides project staff the ability to produce forecasted volumes and design parameters for small low risk projects without assistance from forecasting staff.

Revise the project traffic forecast form to ensure better information from requestors.

Internally update the NCHRP255 model adjustment process to provide more consistent and logical results for a wide range of project conditions and model availability scenarios.

Participate in the NCHRP 8-83 project to formally revise NCHRP 255.

Update project forecasting procedures to require pre-review of all non-maintenance type projects by travel demand modelers and require detailed documentation for all projects requiring project-level modeling.

Create and update project modeling and certified design traffic guidelines and complimentary training courses to encourage consultants, MPOs and ODOT to follow similar procedures which are consistent with NEPA and ODOT Project Development Process requirements.

Enhance travel demand forecasting models temporal and spatial resolution and add new procedures including dynamic intersection delay, dynamic traffic assignment, Transims, and integrated supply/demand models.

Improve land use forecasts by conducting training for MPOs and developing an integrated land use/transport model for OSTDM.

Enhance traffic counting program by creating standardized databases, out-sourcing most ODOT counting and conducting increased coordination to obtain and archive traffic counts from local jurisdictions.