Data and Models
Learn more about the CUUATS modeling suite including local transportation, land use, emissions, social costs, accessibility, and mobility data.
This section includes additional documentation about the data and statistical modeling processes utilized to evaluate existing transportation conditions and project future transportation conditions.
System Performance Report
Introduction The Moving Ahead for Progress in the 21st Century (MAP-21) enacted in 2012, and the subsequent Fixing America&rsquo;s Surface Transportation Act (FAST Act), enacted in 2015, established a national performance measurement system for the highway and transit programs. The U.S. Department of Transportation (USDOT) instituted this performance management requirement by establishing performance measures for four categories through rulemakings: Highway Safety Pavement and Bridge Condition System Performance Transit Asset Conditions The state departments of transportation (state DOTs) and metropolitan planning organizations (MPOs) are required to establish targets for each highway performance measure while transit agencies and MPOs set targets for transit asset condition.
Data and Models
Diagram of the modeling process used by CUUATS staff to develop the Long-Range Transportation Plan 2045 vision. Note: To expand the image to full-size, right-click the graphic and choose &#39;open image in new tab&#39;. Image: CUUATS As described in the modeling section, the CUUATS modeling suite is designed to provide a holistic approach to planning analysis through the integration of localized transportation, land use, emission, social costs, accessibility, and mobility data.
The Champaign County Travel Demand Model (TDM) is a transportation planning tool developed to evaluate the existing transportation system and forecast future travel demand in the region. The model utilizes the roadway network, transit network, socio-economic data, land use data, and existing travel patterns to estimate traffic volumes in relation to roadway capacity. The model also predicts the future travel patterns in the region and project-level future traffic conditions based on changes to the roadway network in combination with socio-economic, land-use, and other data.