Washington, DC--VA--MD Urbanized Area Congestion Report
In the line graphs below, FHWA uses Data Collection Year instead of Data Reporting Year to represent snapshot condition/performance at the time the data was collected. More information
The Washington, DC--VA--MD Urbanized Area covers parts of District of Columbia, Maryland, and Virginia. Targets are agreed upon by several transportation agencies and apply to the entire area.
Annual Hours of Peak-Hour Excessive Delay (PHED) Per Capita
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Annual Hours of Peak-Hour Excessive Delay (PHED) Per Capita 2021 2022 2023 2024 2025 Condition/Performance 13.1 -- -- -- -- Targets -- -- 22.5 -- 22.7
Annual Hours of Peak-Hour Excessive Delay (PHED) Per Capita
- Basis for Targets
- 2-yr Progress
- Basis for 4-yr Target Adjustment
- 4-yr Planned Activities
- 4-yr Progress
(District of Columbia) Data was collected for the Washington DC-VA-MD UZA from INRIX using a widget created for the Regional Integrated Transportation Information System (RITIS). RITIS is an automated data sharing, dissemination, and archiving system that includes many performance measure, dashboard, and visual analytics tools that help agencies gain situational awareness, measure performance, and communicate. The RITIS widget was developed by the University of Maryland Center for Advanced Transportation Technology Laboratory (CATT Lab). The RITIS widget is designed to provided historical data and baseline metrics. In determining a methodology for setting the Washington DC-VA-MD UZA four-year target for the 2022–2025 Performance Period, the relevant agencies selected a methodology averaging the forecasts from the TPB’s (MPO) travel demand model and an extrapolation of past data through 2019. The TPB travel demand model was produced internally in 2022; relevant outputs are congestion for modeled years 2021, 2023, and 2025. Forecasting was achieved by utilizing model output AM Peak Hour VMT estimates to project change in congestion, applying the percentage increases to measured performance. The travel demand model takes into account near-term predicted changes in population, employment and other factors that increase travel demand, as well as changes in the highway and transit network. Extrapolation of measured performance is an approach whereby measured data from past years through 2019 (i.e., excluding the pandemic years of 2020 and 2021) was extrapolated, via linear regression, through the year 2025. The targets were set based on the average of the results of the extrapolation of measured performance and the travel demand model forecasts.
(Maryland) In 2018 TPB staff developed a forecasting methodology that averaged the effects of the travel demand model output and the extrapolation of past performance. For forecasting for the 2022-2025 four-year performance period, TPB staff decided to use methodologies similar to that for the previous performance period. The PHED measure was forecast using the average of the trendline and an indicator output from the near-term years of the Travel Demand Model for both two-year and four-year targets. Data for all peak periods was collected for the region from the National Performance Management Research Data Set (NPMRDS), using a widget created by the Regional Integrated Transportation Information System (RITIS). RITIS is an automated data sharing, dissemination, and archiving system that includes many performance measure, dashboard, and visual analytics tools that help agencies to gain situational awareness, measure performance, and communicate. It is managed by the University of Maryland Center for Advanced Transportation Technology Laboratory (CATT Lab). The RITIS widget is designed to assist with performance measurement target creation using NPMRDS data.
(Virginia) Data was collected for the Washington DC-VA-MD UZA from INRIX using a widget created for the Regional Integrated Transportation Information System (RITIS). RITIS is an automated data sharing, dissemination, and archiving system that includes many performance measure, dashboard, and visual analytics tools that help agencies gain situational awareness, measure performance, and communicate. The RITIS widget was developed by the University of Maryland Center for Advanced Transportation Technology Laboratory (CATT Lab). The RITIS widget is designed to provided historical data and baseline metrics. In determining a methodology for setting the Washington DC-VA-MD UZA four-year target for the 2022–2025 Performance Period, the relevant agencies selected a methodology averaging the forecasts from the TPB’s (MPO) travel demand model and an extrapolation of past data through 2019. The TPB travel demand model was produced internally in 2022; relevant outputs are congestion for modeled years 2021, 2023, and 2025. Forecasting was achieved by utilizing model output AM Peak Hour VMT estimates to project change in congestion, applying the percentage increases to measured performance. The travel demand model takes into account near-term predicted changes in population, employment and other factors that increase travel demand, as well as changes in the highway and transit network. Extrapolation of measured performance is an approach whereby measured data from past years through 2019 (i.e., excluding the pandemic years of 2020 and 2021) was extrapolated, via linear regression, through the year 2025. The targets were set based on the average of the results of the extrapolation of measured performance and the travel demand model forecasts.
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Data Sources:
2022 Biennial Performance Report
2022 HPMS Data Submittal
Non-Single Occupancy Vehicle (Non-SOV) Travel
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Non-Single Occupancy Vehicle (Non-SOV) Travel 2021 2022 2023 2024 2025 Condition/Performance 39.5 -- -- -- -- Targets -- -- 37.4 -- 37.7
Non-Single Occupancy Vehicle (Non-SOV) Travel
- Basis for Targets
- 2-yr Progress
- Basis for 4-yr Target Adjustment
- 4-yr Planned Activities
- 4-yr Progress
(District of Columbia) For the Washington DC-VA-MD UZA, the selected method for calculating Non-SOV performance was Method A - American Community Survey. The dataset used for the setting of both the two and four-year targets was Table DP03 – Commuting to Work – provided by the ACS. In determining a methodology for setting the Washington DC-VA-MD UZA four-year target for the 2022 –2025 Performance Period, the relevant agencies selected a methodology which uses the average of the forecasts from the MPO’s travel demand model and an extrapolation of the past four years’ data trend. The TPB travel demand model was produced internally in 2016; relevant outputs include congestion for modeled years 2016, 2020, 2025, etc. Forecasting was achieved by applying the model output of the share of SOV Travel and by applying the forecast percent change in SOV Travel to measured performance. The travel demand model takes into account near-term predicted changes in population, employment and other factors that increase travel demand, as well as changes in the highway and transit network. Extrapolation of measured is an approach whereby measured data from the previous years of 2014 through 2017 was extrapolated, via linear regression, through the year 2021. The targets were set based on the average of the results of the extrapolation of measured performance and the travel demand model forecasts. As the center of the region, the District of Columbia has an important role in supporting the achievement of these targets. The District's own population already commutes via non-SOV shares at a much higher rate than the region as a whole and the regional population that works in the District also commutes via non-SOV modes at a higher rate than the targets. The District has stated, numeric goals supporting increased non-SOV mode share well above the regional targets and will continue to support regional efforts to increase walking, bicycling, and public transportation use.
(Maryland) For forecasting for the new four-year performance period, TPB staff decided to use methodologies similar to that for the previous performance period. The Mode Share (Non-SOV) target was forecast using only the trendline.
(Virginia) For the Washington DC-VA-MD UZA, the selected method for calculating Non-SOV performance was Method A—American Community Survey. The dataset for used for monitoring and reporting performance was Table DP03 – Commuting to Work – provided by the ACS.
In determining a methodology for forecasting future performance and developing the Washington DC-VA-MD UZA targets for the 2022–2025 Performance Period, the relevant agencies selected a methodology averaging the forecasts from the TPB’s (MPO) travel demand model and an extrapolation of past data through 2019 or the developing the 2- and 4-year targets.
Forecasting was achieved by utilizing model output AM Peak Hour VMT estimates to project change in congestion, applying the percentage increases to measured performance. The travel demand model takes into account near-term predicted changes in population, employment and other factors that increase travel demand, as well as changes in the highway and transit network.
Extrapolation of measured performance is an approach whereby measured data from past years through 2019 (i.e., excluding the pandemic years of 2020 and 2021) was extrapolated, via linear regression, through the year 2025. The targets were set based on the average of the results of the extrapolation of measured performance and the travel demand model forecasts.
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Data Sources:
2022 Biennial Performance Report
2022 HPMS Data Submittal