Transportation Performance Management

State Highway Reliability Report - Colorado

The information below summarizes the TPM Highway Reliability performance measures, which include two highway reliability measures and one truck travel time reliability measure. Per 23 CFR 490, State Departments of Transportation (DOTs) are required to establish 2- and 4-year targets for these measures. The targets should represent the anticipated condition/performance at the mid-point and end of the 4-year performance period. State DOTs establish targets at the beginning of each 4-year performance period, and report on progress every two years. When establishing targets, State DOTs have the flexibility to use the methodology they deem most appropriate. FHWA encourages States to review data sets and trends and consider factors that may affect targets. Performance targets should be data-driven, realistic, and attainable and should align with the performance management framework and legislative intent.

The targets and discussion of basis for targets, optional adjustment of targets, progress, and planned activities were provided by the State DOT in its most recent biennial performance report. FHWA has not edited this information. It is provided to help bring context to the State DOT's performance targets and progress. The data in the tables and graphs is from the biennial performance report or HPMS data submittal as noted below each measure summary. Any questions about the information should be directed to the State DOT.

Data reported by State DOTs was collected in the previous year, representing the condition/performance at the time of collection. Thus, in the tables and graphs below, FHWA labels data with the year representing the condition/performance, rather than the year the data was reported. The same thing is done for the targets--the year represents when the corresponding actual condition/performance data will be collected, not reported. More Information.

Significant Progress Determination
FHWA determines significant progress for these measures after the mid-point and end of each performance period. A State has met or made significant progress toward target achievement if “actual” condition/performance is equal to or better than the established two-year target or “actual” condition/performance is better than baseline performance 23 CFR 490.109(e). As provided in 23 CFR 490.107(b)(2)(ii)(A), baseline condition/performance is derived from the latest data collected through the beginning date of the performance period. FHWA will classify the assessment of progress toward the achievement of an individual 2-year or 4-year target as “progress not determined” if a State provides the extenuating circumstance information required in 23 CFR 490.109(e)(5), and FHWA accepts the information.

Most recent Significant Progress Determination for the Reliability measures

Most recent Significant Progress Determination for all applicable measures

• 

  Interstate Highway Reliable Person-Miles Traveled

• • Trend through 2025

    Desired trend: ↑

    Colorado % of Interstate Highway Reliable Person-Miles Traveled

    
    

• Interstate Highway Reliable Person-Miles Traveled	2021	2022	2023	2024	2025
  Condition/Performance	85.3	--	--	--	--
  Target	--	--	81.0	--	79.0

• Interstate Highway Reliable Person-Miles Traveled

  • Basis for Targets
  • 2-yr Progress
  • Basis for 4-yr Target Adjustment
  • 4-yr Planned Activities
  • 4-yr Progress

  CDOT Established 2- and 4-year targets for the 2022-2025 Performance Period for the statewide percent of the person-miles traveled on the Interstate that are reliable using a predictive model. Given the emerging usage of predictive analytics, CDOT saw this as an opportunity to develop and establish data-driven PM3 targets using a random forest model. To train the model, CDOT incorporated historical travel time data, roadway travel segments (TMC), annual average daily traffic (AADT), occupancy factors defined by FHWA, and data from CDOT’s travel demand model. CDOT’s travel demand model includes point data, population data, and long-distance travel estimates. Point data consists of 2015, 2030, and 2045 spatial data which shows the locations of establishments, schools, and homes in the state of Colorado. Population estimates were used at the county level, to ensure we were examining population growth at a more micro level rather than state growth. Long-distance travel estimates were used to factor in travel that originated from outside of the state. After training the model, CDOT tested the results on 2019 data to estimate the performance of the model. CDOT was able to predict known 2019 reliability at accurate levels. Given the accuracy of the model, CDOT forecasted 2023 and 2025 travel time reliability to identify 2- and 4-year targets.

• Data Sources:
  Colorado 2022 Biennial Performance Report
  Colorado 2022 HPMS Data Submittal

• 

  Non-Interstate National Highway System (NHS) Reliable Person-Miles Traveled

• • Trend through 2025

    Desired trend: ↑

    Colorado % of Non-Interstate NHS reliable Person-Miles Traveled

    
    

• Non-Interstate NHS reliable Person-Miles Traveled	2021	2022	2023	2024	2025
  Condition/Performance	94.7	--	--	--	--
  Target	--	--	93.0	--	94.0

• 
  

  Non-Interstate National Highway System (NHS) Reliable Person-Miles Traveled

  • Basis for Targets
  • 2-yr Progress
  • Basis for 4-yr Target Adjustment
  • 4-yr Planned Activities
  • 4-yr Progress

  CDOT Established 2- and 4-year targets for the 2022-2025 Performance Period for the statewide percent of the person-miles traveled on the Non-Interstate NHS that are reliable using a predictive model. Given the emerging usage of predictive analytics, CDOT saw this as an opportunity to develop and establish data-driven PM3 targets using a random forest model. To train the model, CDOT incorporated historical travel time data, roadway travel segments (TMC), annual average daily traffic (AADT), occupancy factors defined by FHWA, and data from CDOT’s travel demand model. CDOT’s travel demand model includes point data, population data, and long-distance travel estimates. Point data consists of 2015, 2030, and 2045 spatial data which shows the locations of establishments, schools, and homes in the state of Colorado. Population estimates were used at the county level, to ensure we were examining population growth at a more micro level rather than state growth. Long-distance travel estimates were used to factor in travel that originated from outside of the state. After training the model, CDOT tested the results on 2019 data to estimate the performance of the model. CDOT was able to predict known 2019 reliability at accurate levels. Given the accuracy of the model, CDOT forecasted 2023 and 2025 travel time reliability to identify 2- and 4-year targets.

• Data Sources:
  Colorado 2022 Biennial Performance Report
  Colorado 2022 HPMS Data Submittal

• 

  Interstate Highway Truck Travel Time Reliability (TTTR) Index

• • Trend through 2025

    Desired trend: ↓

     

    Colorado Truck Travel Time Reliability Index

    
    

• Truck Travel Time Reliability Index	2021	2022	2023	2024	2025
  Condition/Performance	1.39	--		--	--
  Target	--	--	1.46	--	1.46

• Interstate Highway Truck Travel Time Reliability

  • Basis for Targets
  • 2-yr Progress
  • Basis for 4-yr Target Adjustment
  • 4-yr Planned Activities
  • 4-yr Progress

  CDOT established 2- and 4-year targets for the 2022-2025 Performance Period for the statewide truck travel time reliability index using a predictive model. Given the emerging usage of predictive analytics, CDOT saw this as an opportunity to develop and establish data-driven PM3 targets using a random forest model. To train the model, CDOT incorporated historical truck travel time reliability, roadway travel segments (TMC), annual average daily traffic (AADT), occupancy factors defined by FHWA, and data from CDOT’s travel demand model. CDOT’s travel demand model includes point data, population data, and long-distance travel estimates. Point data consists of 2015, 2030, and 2045 spatial data which shows the locations of establishments, schools, and homes in the state of Colorado. Population estimates were used at the county level, to ensure we were examining population growth at a more micro level rather than state growth. Long-distance travel estimates were used to factor in travel that originated from outside of the state. After training the model, CDOT tested the results on 2019 data to estimate the performance of the model. CDOT was able to predict known 2019 truck reliability at accurate levels. Given the accuracy of the model, CDOT forecasted 2023 and 2025 truck travel time reliability to identify 2- and 4-year targets.

• Data Sources:
  Colorado 2022 Biennial Performance Report
  Colorado 2022 HPMS Data Submittal

Significant Progress Determination

This table shows FHWA’s most recent determination for the Infrastructure performance measures.

PLEASE NOTE: Each State’s performance target assessment is based on its own State-specific target methodology and program philosophy. Therefore, conclusions should not be drawn based only on the information in the Significant Progress Determination Results table. FHWA understands that each State’s program is unique and therefore does not prescribe a methodology for States to set targets. States have the flexibility to use the methodology they deem most appropriate when setting their performance targets. 

³ The 2-year condition/performance, in 2020 Mid Performance Period Progress Report, as the baseline condition/performance, as required in 23 CFR 490.105(e)(7)(iii).