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Transportation Performance Management

 

Washington, DC--VA--MD Urbanized Area Congestion Report

In the line graphs below, FHWA has shifted the "Year" label in the x-axis back by one year, from Data Reporting Year to Data Collection Year. 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

    • Trend through 2021

      Desired trend: ↓

      Washington, DC--VA--MD Annual Hours of Excessive Delay Per Capita


  • Annual Hours of Peak-Hour Excessive Delay (PHED) Per Capita 2017 2018 2019 2020 2021
    Condition/Performance -- -- 24.5 -- --
    Targets -- -- -- -- 26.7
  • Behind the data: For the first performance period only, baseline condition and 2-year targets are not required for the Peak-Hour Excessive Delay measure.


  • Annual Hours of Peak-Hour Excessive Delay (PHED) Per Capita

    (District of Columbia) Data was collected for the Washington DC-VA-MD UZA from NPMRDS and INRIX data 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 provide historical data and baseline metrics. In determining a methodology for setting the Washington DC-VA-MD UZA four-year target for the 2018 – 2021 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 utilizing such outputs as 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 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. The District is committed to supporting efficient bus service on the arterial network to support non-SOV goals but how bus improvements on the arterial network will impact these measures remains to be seen given the data sources used. Current speed limits support the District's safety goals and unlikely to be increased, limiting the improvement possible for this measure in the District specifically.

    (Maryland) Data was collected for the U.S. Census 2010 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. The PHED target for the Washington DC-MD-VA UZA is reflective of the expected population and job growth forthcoming in the region, as well as the completion of significant public transportation and road infrastructure projects. The target is also logical, data driven, and reasonable. In determining a methodology for setting the Washington DC-VA-MD UZA four-year target for the 2018 – 2021 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 utilizing such outputs as 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 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.

    (Virginia) VDOT coordinated with the TPB to set this target. The selected methodology for the PHED target is an average of the region’s travel demand model output and extrapolation of past performance. Data for the AM and PM weekday peaks were collected for the region from NPMRDS data set. Data was collected for the Washington, DC-VA-MD UZA from INRIX using a widget created for 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 provide historical data and baseline metrics. In determining a methodology for setting the Washington, DC-VA-MD UZA 4-year target for the 2018-2021 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. Forecasting was achieved by utilizing such outputs as 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 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.

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  • Data Sources:
    2018, 2020 Biennial Performance Report
    2020 HPMS Data Submittal

  • Non-Single Occupancy Vehicle (Non-SOV) Travel

    • Trend through 2021

      Desired trend: ↑

      Washington, DC--VA--MD % Non-SOV Travel


  • Non-Single Occupancy Vehicle (Non-SOV) Travel 2017 2018 2019 2020 2021
    Condition/Performance 36.6 36.6 36.6 -- --
    Targets -- -- 36.9 -- 37.2
  • Behind the data: The State used data collection Method A (American Community Survey), as defined in 23 CFR 490.709(f)(1)(i), in establishing their targets.


  • Non-Single Occupancy Vehicle (Non-SOV) Travel

    (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 2018 – 2021 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 performance 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 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 2018 – 2021 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 performance 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.

    (Virginia) The non-SOV target is based on the same methodology as the PHED target. VDOT coordinated with TPB to set this target. The selected methodology for the Non-SOV target is an average of the region’s travel demand model output and extrapolation of past performance. Data sources consulted include the ACS work commute data by urban area, a local survey conducted within two years of the performance period, internal system use measurements, and U.S. Census population data.

    In determining a methodology for setting the Washington, DC-MD-VA UZA 4-year target for the 2018-2021 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. 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 performance 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.

    (District of Columbia) In 2018 when the 2-year and 4-year targets were developed, the expectation was that the percentage of non-SOV travel would very slowly increase (improve) at a rate of 0.1% per year. Instead, the non-SOV percentage has remained constant for three years at 36.6 percent. The 2-year target was therefore not met. Possible reasons for the lack of progress in the measure and not meeting the 2-year target include 1) Gas prices have fallen and stayed low, which encourages driving. 2) Car ownership is up; particularly for low-income households now having access to at least one vehicle. 3) While trends in public transportation ridership appeared to be finally recovering just before the pandemic, ridership had been below projections at WMATA and other transit systems for several years with the growth in TNC/ride-hailing services being one factor that has affected transit ridership. The long-term impacts of the pandemic on telework and travel choices is unforeseeable at the time. Without an evidence-based set of data on which to project future travel impacts, the State DOTs and the TPB will retain the previously established 4-year target for non-SOV travel.

    (Maryland) "In 2018 when the 2-year and 4-year targets were developed, the expectation was that the percentage of non-SOV travel would very slowly increase (improve) at a rate of 0.1% per year. Instead, the non-SOV percentage has remained constant for three years at 36.6 percent. The 2-year target was therefore not met.

    Possible reasons for the lack of progress in the measure and not meeting the 2-year target include 1) Gas prices have fallen and stayed low, which encourages driving. 2) Car ownership is up; particularly for low-income households now having access to at least one vehicle. 3) While trends in public transportation ridership appeared to be finally recovering just before the pandemic, ridership had been below projections at WMATA and other transit systems for several years with the growth in TNC/ride-hailing services being one factor that has affected transit ridership. 4) Telework had plateaued and in fact some major employers in the region had begun to backtrack on employee teleworking. The long-term impacts of the pandemic on telework and travel choices is unforeseeable at this time.

    Despite not meeting the 2-year target, MDOT, other DOTs and the area MPO are committed to expanding Non-SOV Travel and therefore do not wish to revise the 4-year target in line with current trends. Planned work to meet the four-year target include • MDOT continues to promote Commuter Choice Maryland, which encourages commuters to explore and use alternate means of transportation to and from work, giving them travel choices when convenient to them, such as transit, ridesharing (carpool/vanpool), biking, walking, teleworking, and alternative flexible work schedules. All of these options help to reduce commuter stress, reduce congestion and conserve energy. • Transit Apps such as CharmPass Mobile Ticketing app, introduced in September 2018, which allows riders to pay for MDOT MTA services from a smart phone for all Local Bus, Metro SubwayLink, Light RailLink, MARC Train, and Commuter Bus Services. • STIP/TIP Programming"

    (Virginia) In 2018, when the 2-year and 4-year targets were developed, the expectation was that the percentage of non-SOV travel would very slowly increase (improve) at a rate of 0.1 percent per year. Instead, the non-SOV percentage has remained constant for three years at 36.6 percent. The 2-year target was therefore not met. Possible reasons for the lack of progress in the measure and not meeting the 2-year target include - 1) Gas prices have fallen and stayed low, which encourages driving; 2) Car ownership is up, particularly for low-income households now having access to at least one vehicle; and 3) While trends in public transportation ridership appeared to be finally recovering just before the pandemic, ridership had been below projections at WMATA and other transit systems for several years with the growth in TNC/ride-hailing services being one factor that has affected transit ridership.

    The long-term impacts of the pandemic on telework and travel choices is unforeseeable at this time. Without an evidence-based set of data on which to project future travel impacts, the State DOTs and the TPB will retain the previously established 4-year target for non-SOV travel.

    State DOTs and the TPB remain committed to providing alternatives to non-SOV travel. The Commuter Connections regional network of transportation organizations works to improve commutes in the Washington, DC metropolitan area with a variety of services and programs to assist employers and commuters with making alternative choices about their commuting needs, including ridematching, transit, bicycling, teleworking, and incentive programs. Additionally, in 2021 the opening of the extension of the WMATA Metrorail Silver Line to Dulles Airport and Loudoun County should offer a significant new opportunity for alternative travel.

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  • Data Sources:
    2018, 2020 Biennial Performance Report
    2018, 2019, 2020 HPMS Data Submittal

Updated: 03/08/2022
Federal Highway Administration | 1200 New Jersey Avenue, SE | Washington, DC 20590 | 202-366-4000