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Data and Models

Learn more about the CUUATS modeling suite including local transportation, land use, emissions, social costs, accessibility, and mobility data.

Data and Models

Diagram showing the types of software used to develop the future vision. CUUATS staff started with the Travel Demand Model and Urban Sim. These two programs analyze the vehicle miles traveled, mode choice, traffic volume by road link, congestion speed, population projections, employment projections, and areas of future growth. From there, the data collect from the Travel Demand Model and Urban Sim are used in three other programs: Social Cost of Alternative Land Development Scenarios (SCALDS), Motor Vehicle Emissions Simulator (MOVES), and Neighborhood Level Accessibility Analysis (Access Score). SCALDS measures transportation cost by mode, energy cost, infrastructure cost, and water and sewer cost. MOVES calculates GHG emissions, urban/rural emissions, and PM 25 and other emissions. Access Score measures the level of traffic stress, by transportation mode, accessibility score by transportation mode, and the accessibility scores by destination type.
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 'open image in new tab'. 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. Each model addresses a specific area of concern at the necessary level of detail to make it appropriate for Champaign County or the metropolitan planning area. The synergy of the different models allows CUUATS planners to assess how different population changes and development patterns will impact the transportation system in the future. By quantifying the various impacts of potential transportation system changes, planners are able to compare different future development scenarios as well as develop individual performance measures. While all transportation improvements require some combination of labor, materials, and expertise to carry out, some desired improvements are more straightforward to measure and model than others. For instance, completing gaps in the bicycle network or improving curb ramps to improve multimodal connectivity can be counted, measured, and tracked as improvements are completed over time. Other desired improvements, like limiting sprawl development or reducing greenhouse gas emissions to improve environmental health, rely on a number of additional factors and future unknowns that are not as clearly measurable and are therefore more difficult to model for the future.

Two models serve as the foundation of the CUUATS modeling suite: a land use model and a travel demand model (TDM). The land use model projects population, employment, and land use change into the future while the TDM estimates the number and location of auto and transit in the future. The TDM is integrated with the land use model, running in a 5-year iterative process over the 25-year planning period, to identify the relationships between land use changes and travel patterns in the region. Three additional models, SCALDS, MOVES, and Access Score, use the outputs from the first two models to project different costs of development, vehicle emissions, and transportation network accessibility for all modes. Models can’t predict the future, but they can help us imagine the future and try to understand how our actions today could impact our transportation system down the road. Countless variables will determine the health and relative success of the region over the next 25 years. While the LRTP 2045 models and projections were carefully designed and validated whenever possible, they are not perfect reflections of the social and physical processes in play, and the input data used is imperfect.

The following sections provide additional documentation or links to additional documentation about the methodologies and input data used for each of the models.

Travel Demand Model (TDM)

CUUATS built the first travel demand model (TDM) for the Champaign-Urbana urbanized area in 2003, which was later expanded to include all of Champaign County. The current Champaign County TDM is the foundation of the CUUATS modeling suite, producing fundamental information needed to plan for the future transportation system such as the current and projected transportation demand of persons and goods in Champaign County. Since 2003, CUUATS has been maintaining, documenting, and expanding the TDM capabilities to serve the planning needs of the region and to support and advance travel demand modeling initiatives across the state.

The TDM Documentation page details the different components, inputs, outputs, and processes that make up the Champaign County TDM.

A brief summary of the main TDM outputs for the LRTP 2045 is included in the Modeling section of the plan.

Land Use Model: UrbanSim

Effective coordination of land use development and transportation planning can produce policies and strategies that preserve and enhance valued natural and cultural resources and facilitate healthy, sustainable communities and neighborhoods.

UrbanSim’s UrbanCanvas modeler is a microsimulation land use model designed to support the regional transportation planning process by forecasting future scenarios based on the interactions of individual persons and households. UrbanCanvas consists of a series of interconnected models that allow planning agencies to analyze anticipated and possible future changes in land use and development patterns, regulations, and growth rates, over a designated planning horizon. The outputs forecasted by the model can vary based on the wide array of inputs.

To learn more about the inputs used in the land use model, visit the CUUATS land use site in GitLab.

To explore the parcel-level outputs of the land use model, visit CUUATS land use model results site. There you can select from a series of drop-down menus to display the land use model results for any year between 2015 and 2045.

A brief summary of the main land use model outputs for LRTP 2045 is included in the Modeling section of the plan.

Social Cost of Alternative Development Scenarios (SCALDS)

The Social Cost of Alternative Land Development Scenarios (SCALDS) model was sponsored by the Federal Highway Administration and developed under contract with Parsons Brinckerhoff, Quade and Douglas Inc. (PB), to estimate the full cost of alternative land use patterns. The EXCEL-based spreadsheet builds on three areas of research - least cost planning, full cost of travel studies, and cost of service/cost of sprawl research.

Diagram showing the full cost of alternative land use model schematic from FHWA SCALDS documentation
Full Cost of Alternative Land Use Model Schematic from FHWA's documentation on the SCALDS model. Note: To expand the image to full-size, right-click the graphic and choose 'open image in new tab'. Image: Excerpt from 'The Full Social Costs of Alternative Land Use Patterns: Theory, Data, Methods, and Recommendations.' Prepared for USDOT, FHWA by Parsons Brinckerhoff Quade & Douglas, Inc. ECONorthwest, June, 1998.

SCALDS requires detailed input data from a variety of different sources relevant to development costs and trends at the national and local level. In addition to current data, SCALDS also requires data specific to the different future scenarios being modeled in order to compare them against the base year and against one another such as population and employment projections, housing mix, and land use changes. Some inputs come from the other models in the CUUATS modeling suite as illustrated in the diagram at the top of the page.

SCALDS is made up of 19 detailed and interconnected spreadsheets that calculate different costs based on the inputs provided for the base year and the scenarios. The following is a summary of the four main components of the SCALDS inputs and outputs.

  • The first step in estimating transportation cost is to estimate the total number of daily trips produced in the region, which is obtained by multiplying the number of households by the average household trip production rate. The trips are then divided among different modes of travel using the mode choice estimates. Person miles traveled (PMT) for each mode is estimated by multiplying average trip lengths by the number of trips. PMT for different modes is them multiplied by the average cost per mile for each mode to get total transportation cost. Assumptions about the advent of connected and autonomous vehicles for the future scenarios impacted these inputs, as detailed further in the TDM documentation. SCALDS has separate cost estimates for peak and off-peak travel for each mode of transportation. Finally, all of the costs for all of the modes are combined to get one estimate of the total transportation cost for a scenario.

  • Demand for water, sewer, and storm-water infrastructure is estimated based on the type of household. As such, the projections for the number of households are broken down into different categories using observed housing mix in the region. Localized estimates for water and sewer demand for each type of housing are then used to estimate total residential water and sewer demand. A similar process is followed to estimate non-residential demand where employment projections are divided between different industrial classifications. The average demand for each type of non-residential use is then used to calculate non-residential water and sewer usage costs.

  • Energy cost estimates are based on local data on the average annual energy use of different types of households. These estimates are multiplied by the number of households associated with each category to get the total energy usage for each type of household. The total energy cost can then be estimated from the total energy by considering the average cost of energy usage. Existing plans and projections to expand solar energy production in the region were taken into account when calculating the energy inputs for future scenarios. The proliferation of electric vehicles was also incorporated into calculations for future energy consumption.

  • SCALDS estimates the cost of infrastructure for new developments based on the type of development. Based on housing projections, the number of new housing units are estimated for fixed intervals of time, such as 2020 to 2025, which are then used to estimate the infrastructure cost for new developments. Infrastructure costs are divided into two categories: streets and utilities. SCALDS uses local estimates for the infrastructure costs of street and utilities for different types of housing units.

SCALDS provides as outputs cost estimates for every five years over the planning horizon. A brief summary of the main SCALDS outputs for LRTP 2045 is included in the Modeling section of the plan.

Motor Vehicle Emission Simulator (MOVES)

EPA’s MOtor Vehicle Emission Simulator (MOVES) is a state-of-the-science emission modeling system that estimates emissions for mobile sources at the national, county, and project level for criteria air pollutants, greenhouse gases, and air toxics.

Although the Champaign-Urbana urbanized area is currently an attainment area for all emissions quality standards, CUUATS staff proactively includes MOVES in the modeling suite to estimate the environmental impact of alternative planning scenarios. This data also allows the region to continually track and better understand how ongoing development affects emissions in order to remain an attainment area.

Before the LRTP 2045 modeling process officially began, CUUATS was one of only four agencies from around the country chosen by the EPA to provide critical data for a project looking at air quality and greenhouse gas emissions caused by transportation. This work helped CUUATS staff prepare local MOVES functionality as part of the modeling suite used for the LRTP 2045.

The CUUATS case study, part of a document published on the EPA’s website titled Applying TEAM in Regional Sketch Planning, finalized in November 2018, looks at ways for Champaign County to realize a significant decrease in greenhouse gas emissions if certain strategies are implemented in the Champaign-Urbana region.

Several calculated assumptions impact the MOVES 2045 outputs including increased temperature, a significant increase in the share of electric, connected, and autonomous vehicles, and transportation network recommendations.

A brief summary of the main MOVES outputs for LRTP 2045 is included in the Modeling section of the plan.

Neighborhood Level Accessibility Analysis: Access Score

Many of the long-range transportation planning assessment processes are concerned with data and trends that occur at the regional level. While this is beneficial for understanding the overall future direction of the community, it is not localized enough to help identify specific limitations in the transportation network. To help address this spatial mismatch and make the CUUATS transportation planning and modeling processes more complete, staff developed a geography neutral, multimodal accessibility assessment, known as Access Score. This tool utilizes level of traffic stress (LTS) assessments for each mode and travel time to calculated accessibility scores to several destination types. These accessibility scores help staff to assess the current and potential future status of accessibility in the Champaign-Urbana region, to identify areas in need of improvement, and to observe potential benefits from the construction of new infrastructure.

To develop Access Score staff utilized an existing bicycle level of traffic stress (BLTS) assessment from the Mineta Transportation Institute, and an existing pedestrian level of traffic stress (PLTS) assessment from the Oregon Department of Transportation. Automobile level of traffic stress (ALTS) is assessed using an in-house analysis, created by CUUATS staff to emulate the assessments for BLTS and PLTS, by considering elements of the automobile transportation network and its interactions with other modes. Each segment in the network was assigned an overall level of stress based on the highest, or most stressful score it received for any one of the characteristics considered. Transit level of traffic stress (TLTS) is assessed using the Pandana accessibility tool, which uses general transit feed specification (GTFS) and transit headway and schedule data to assess transit trips based on the time required to reach a destination, which is then combined with the pedestrian score required to get from the point of origin to the nearest bus stop.

Once the modal LTS scores were complete, accessibility was calculated by multiplying the LTS scores by the travel time. The assessment includes accessibility to the following ten destination types: grocery stores, health facilities, jobs, parks, public facilities, retail stores, restaurants, schools, arts and entertainment, and services.

The accessibility scores for Champaign County can be seen in the Access Score application embedded below. Expand the menu in the top left corner to explore the scenarios, travel modes, and destinations.

To learn more about the code and documentation for level of traffic stress calculations, visit the CUUATS LTS site in GitLab.

For more information about the code and documentation regarding Access Score calculations, visit the CUUATS Accessibility site in GitLab.