Introduction


CMAP Modeling Area and Network


The modeling area extends beyond the 7-county CMAP region to include 21 counties. Only portions of Lee, Ogle, and LaSalle counties are included in the modeling area.



Population Synthesis

Using Census data and other socioeconomic information, a complete set of households for the CMAP modeling area is developed. This dataset contains all of the relevant attributes about each household and for each individual living in those households. While this population is statistically representative of the Census data, the actual households and individuals are “synthetic” in that they do not represent identifiable people and an individual household in the Census data may be replicated numerous times in order to generate a complete distribution of households in the modeling inputs.

The current population synthesis software used by CMAP is PopulationSim. The socioeconomic data developed from CMAP’s regional forecasting and Local Area Allocation procedures provide the subzone-level control values for all ON TO 2050 model scenarios. You can see how well the synthetic population matches the controls at the PUMA level in the Household Attributes page.

Data note: Currently, observed counts include full PUMAs that are only partially within the modeling area, so the observed total count is slightly greater than the model count.


Households by Size


Hover over household size categories to reveal details.

Household Characteristics

Households by






Income is in 2019 dollars.


Household Detail

Distribution of Adults Per Household by




Vehicle Ownership

Vehicle ownership plays an important role in individuals’ travel behavior decisions and helps define the set of travel mode options available to them. For example, households that own no vehicles may be dependent on transit to make their trips. Note that vehicle ownership refers to motor vehicles owned or leased by a household, including autos, pickup trucks, SUVs and motorcycles.


Number of Households by Vehicles Owned

Total Households: Model, Observed
Households by


Income is in 2019 dollars.


Distribution of Vehicle Ownership by

PopulationSim Model

Total Households: Model, Observed


Income is in 2019 dollars.


Distribution of Vehicle Ownership by

Vehicle Availability Sub-Model

Total Households: Model, Observed


Income is in 2019 dollars.

Synthetic Household Results

This map displays the difference between modeled and observed data for controlled attributes in the population synthesis process summarized by U.S. Census Public Use Microdata Areas (PUMAs).  PUMAs are geographic areas defined by the U.S. Census Bureau that contain a minimum of 100,000 people.



Attribute: 1-person households


Choose from household size, household income, population segment, householder age, or building type by clicking the category button and making a selection in the dropdown. Hover over each PUMA to display the values for modeled and observed data.





Trips by Mode



Trips by Mode


See more about transit in the Transit tab.
Hover over bars to compare trips by mode.

Trips by Person Type and Mode


of all trips

Individual trips only.

Trips by Household Income and Mode


of all trips



Income is in 2007 dollars.

Trips by Purpose and Mode


of all trips

Work Flows

Commute trips are an important part of a region’s overall travel pattern and are a key component of congestion experienced on the transportation system, especially during the morning and evening hours. Ensuring that commuters are traveling between the appropriate home and work locations is one important way to verify that travel demand models reflect actual travel patterns.

Note there are two sources for observed data on this page. The first three tables use data from journey-to-work flows from the Census Transportation Planning Package (CTPP). The remaining two charts use data from household travel surveys. CTPP data is generally used to estimate models that address work flows due to the large sample of data included. Data from household travel surveys can be used to supplement the journey-to-work information from the CTPP, as travel surveys offer more detailed information about the trips than the Census provides.


Modeled Journey to Work Flows



Observed Journey to Work Flows



Difference Model and Observed Journey to Work Flows




Origin and Destination Work Flows

From
To    



Hover over the chord diagram to see area boundaries on the map. Click on the map to see area name. Choose work flow geographies in the drop down menu to view numerical values for work trips.

Modeled




Observed



Work Trips by Mode and Distance


of all work trips



Shared ride 3+ toll and non-toll trips are combined due to the small sample of these trips captured by the Travel Tracker Survey.

Transit Trips

Transit is a vital part of the transportation system in northeastern Illinois and more than 600 million transit trips are made annually. It is important to properly calibrate such a key travel mode to ensure that the modeled transit trips reflect the travel sheds actually served by transit.




Modeled vs. Observed Transit Trips Origin and Destination

The scatterplot includes a point for each origin/destination pair. A map of origin/destination areas is shown below, with the CMAP region shaded for reference.

r2 =


Transit Trips by

Total transit trips: 1.63M Model, Survey


Distance is the distance of the transit trip that occurred. Income is in 2007 dollars. They survey has fewer total transit trips when viewed by income since not all respondents reported an income value.

Transit Access

The Drive to Transit access mode includes individuals parking at transit stations (Park and Ride) and those who are dropped off at the station (Kiss and Ride). The Walk to Transit access mode includes people walking or cycling to get to transit.



Hover over pie chart to compare transit access.
Modeled Transit Access

CMAP ABM - 2010 Scenario

Observed Transit Access

Travel Tracker Survey, 2007-2008

Vehicle Ownership

Vehicle ownership plays an important role in individuals’ travel behavior decisions and helps define the set of travel mode options available to them. For example, households that own no vehicles may be dependent on transit to make their trips. Note that vehicle ownership refers to motor vehicles owned or leased by a household, including autos, pickup trucks, SUVs and motorcycles.


Number of Households by Vehicles Owned

Total Households: Model, Observed

County values reflect percent of regional total.


Geography:



Distribution of Vehicle Ownership by



Total Households: Model, Observed


Income is in 2019 dollars.

Synthetic Household Validation

This map displays the difference between modeled and observed data for selected household attributes summarized by U.S. Census Public Use Microdata Areas (PUMAs).  PUMAs are geographic areas defined by the U.S. Census Bureau that contain a minimum of 100,000 people.



Attribute: 0-vehicle households


Choose from household size, household income, number of workers, and number of vehicles in households by clicking the category button and selecting the specific household type from the dropdown. Hover over each PUMA to display the values for modeled and observed data.

Example: In the model, 22.7% of households in northeast Lake County are 1-person households. According to PUMS, 27.2% of households in northeast Lake County are 1-person households. This is a difference of -4.5 percentage points.







Commute Trips Validation

The analysis of commute trips focuses on comparing the patterns of travel from home (place of residence) to work (primary place of work).


Modeled vs. Observed Journey to Work Flows

The scatterplot includes a point for each home/work county pair in the modeling area. Only a portion of Lee, Ogle, and LaSalle counties are included in the model data (refer to map in Introduction).

r2 =



Modeled Journey to Work Flows



Observed Journey to Work Flows



Difference Model and Observed Journey to Work Flows







Almost half of the commute trips in the region originate from Cook County. Whereas the model does a good job of capturing the total commute trips originating from Cook County, it is overestimating the number of these trips that end in Cook County, and underestimating the number of commute trips from Cook to all other counties. The model is underrepresenting the total number of work trips originating from Lake County by 6.6%.

Highway Assignment

During Highway Assignment, the individual motor vehicle trips developed by the ABM are routed along the model transportation network from origin to destination in order to estimate traffic flows and network conditions.  As additional vehicles attempt to use the same roadway segments during the assignment, travel becomes more congested and travel times increase.  The assignment procedures implement a series of steps moving various portions of traffic onto alternative routes in an attempt to reduce congestion and minimize travel costs.  Equilibrium is reached within the highway assignment when vehicles cannot change paths without negatively impacting travel times.  Once equilibrium is reached, the volume of vehicles on each roadway segment is retained and can be used to calculate standard measures such as vehicle miles of travel (VMT).


Daily VMT Shares by County






In general, the model underestimates the total VMT for the 7-county region (160 million VMT vs. 167 million VMT). However, the VMT shares by county are very similar between the modeled and observed data. Counties with less VMT – like Kendall and McHenry – have very similar modeled vs. observed VMT shares, while Cook County accounts for 53.3% of modeled VMT and 50% of observed VMT.

Daily VMT Shares by County and Facility Type







The model does a good job of predicting the VMT shares by facility type for a few areas, including Suburban Cook and DuPage Counties.  The share of modeled vs observed VMT on local roads is almost identical for these two areas.  In Lake County, the model is overestimating the amount of VMT on interstates and underestimating the VMT on arterial streets.  In Chicago, the model is slightly underestimating the VMT on arterial streets.  In general, the share of modeled and observed VMT on arterials is lower in the City of Chicago, Suburban Cook County, DuPage County and Will County. 

Modeled vs. Observed Average Daily Link Volumes


r2 =





The scatterplot shows a strong correlation between the observed average daily link volume and the modeled daily link volume, which is verified with an r-squared value of 0.86. A value of 0.86 means that the regression line shown is a good fit of the observed data and accounts for 86% of the variation in the data. The correlation between the observed and modeled average daily link volumes is stronger for interstates than for arterials/collectors.

Daily VMT By Interstate

This comparison examines vehicles separated into two categories: auto (which includes cars, SUVs, pickup trucks, etc. that people drive for their personal use) and commercial vehicles (which include package delivery trucks all of the way up to tractor-trailers) used to conduct business activities.

Truck volumes on I-190 not shown due to bar scale. Model and observed values equals 11,512 and 9,917 respectively.




Hover over bars to display values and highlight segment on map. Click on a highway segment on the map to show the name.


Auto



Truck





The model over predicts the amount of weekday traffic on the interstate system. Separating the VMT into auto and commercial vehicle components shows that truck tollway freeway volumes are over estimated in the compared to the observed data. Overall, modeled auto volumes show a closer relationship to the observed data for both freeways and tollways.

Daily VMT by Volume Range








This analysis compares modeled volumes to links that have observed traffic counts on them.  Overall the model is overestimating traffic on these links by less than 2%, an extremely close margin.  VMT on the lowest volume roads are being overestimated, while VMT is underestimated by the model on the highest volume road segments.  For the five categories from 5,000 up to 59,999 the model is overestimating VMT by less than 9%, and for two categories the VMT difference is less than 2%.

Root Mean Squared Error Analysis


Links were grouped into volume bins based on the observed traffic counts (AADT) and linear regression analyses were completed for each group. The root mean squared error (RMSE) is a measure commonly-used for model validation analyses and compares the average difference between the observed values and the modeled volume predicted by the linear regression. The percent RMSE standardizes the value by dividing it by the average of the AADT. 

Target values represent the standard maximum acceptable root mean squared error from the Florida Department of Transportation, which are often cited as model validation goals.







For the lowest volume categories, which contain about 87% of the links measured, the percent RMSE is below the Florida target value. There is a spike in the 15,000-19,999 category, where the percent RMSE is 8 percentage points higher than the target value. The higher volume categories also exceed the target value but by smaller amounts. Overall this analysis shows that the ABM is dong a reasonable job of replicating the observed traffic counts.

Transit Assignment

The analysis of transit assignment results generally focus on comparing the number of transit boardings estimated by the model to observed boardings. The CMAP model transit network includes the following modes:

  • Heavy Rail – operated by the Chicago Transit Authority (CTA) in Chicago and some surrounding communities.
  • Commuter Rail – operated by Metra throughout the CMAP region.
  • Bus – operated by CTA (in Chicago and some surrounding communities) and by Pace Suburban Bus (in northeastern Illinois, mostly outside of Chicago).


Transit Boardings by Mode

Transit boardings include average weekday boardings for fixed-route service. Demand responsive dial-a-ride, call and ride, or ADA paratransit services are not included.







The model does a good job predicting the average number of weekday boardings for each transit mode. The predicted number of average weekday boardings for heavy rail, buses, and the total are very close to the observed data (all within 4%). The model is slightly underpredicting the number of transit boardings for commuter rail, by about 7%.

Transit Boardings by Line



Hover over bars to display values and highlight segment on map. Click on a transit segment on the map to show the name.

CTA


Metra





Overall, the model accurately estimates transit boardings by line for both CTA and Metra. The model overpredicts some of the transit boardings (such as the Green Line on the CTA and the BNSF line on Metra), while the model also underpredicts transit boardings on other lines (such as the Brown Line on the CTA and the MD-N line on Metra). However, for the most part, the model does a good job predicting the transit boardings on each CTA and Metra line.