Philadelphia, PA--NJ--DE--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 Philadelphia, PA--NJ--DE--MD Urbanized Area covers parts of Delaware, Marlyand, New Jersey, and Pennsylvania. 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 2017 2018 2019 2020 2021 Condition/Performance -- -- 14.6 7.3 13.1 Targets -- -- -- -- 17.2 -
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
- Basis for Targets
- 2-yr Progress
- Basis for 4-yr Target Adjustment
- 4-yr Planned Activities
- 4-yr Progress
(Delaware) The target setting process was coordinated among state, MPO, and consultant staffs, including an extensive review of NPMRDS data and trends via the RITIS Probe Data Analytics (PDA) Suite to support a comprehensive understanding of available data. Based on the historic data trends, the collaborating agencies (see PMF Input Field D4) agreed to an anticipated PHED increase of approximately 0.6% per year. This increase was applied to historic Year 2017 PHED estimates of 16.8 hours of delay per capita to build-up to future Year 2021 estimates of 17.2 hours of delay per capita. For additional details, refer to the Attachment Tab and the May 20, 2018 DelDOT Performance Management 2 and 3 submission document.
(Marlyand) Annual Average Daily Traffic (AADT) reported to the HPMS was used to estimate hourly traffic volumes when no hourly volume counts exist and AADT reported to the HPMS was used to estimate the annual percent share of traffic volumes for cars, buses, and trucks. RITIS “easy button” methodology was used with outputs extracted on May 7, 2018 for the U.S. Census 2010 Philadelphia, PA--NJ--DE—MD UZA.
(New Jersey) The following points are considered in the PHED Target Setting process. • Policy Goals - This Congestion Mitigation and Air Quality Program performance measure considers excessive traffic congestion and the role that it plays in pollutant emissions. - Goals of all partner agencies (listed in D8) address the need to appropriately manage traffic congestion. The “excessive” part of the PHED name is because some level of congestion is recognized as acceptable and is thus not counted. This corresponds to recognition that it is not possible, nor sometimes desirable, to eliminate all congestion delay; some congestion relates to economic activity and thriving places. Partners also want to provide for reliable travel times, in that some congestion is to be expected, and arriving at a destination at a planned time is most desirable. - The “per capita” implies that the total delay is shared by all residents; hence it considers it beneficial for some trips to be avoided and shifted to walking or biking, or shifted out of the peak time period. • Data - This is a measure of congestion on all roadways on the National Highway System (NHS), which includes the Interstate Highway system and most principal arterials or greater functional class in the urbanized area. - The measure indicates traffic delay experienced by travelers throughout an entire year on roadways, specifically during peak periods. The morning peak period is defined as weekdays from 6 am to 10 am and partner agencies agreed on the afternoon peak period from 3 pm to 7 pm, rather than 4 pm to 8 pm. - Excessive delay means the extra amount of time spent in congested conditions defined by speed thresholds that are lower than a normal delay threshold. The speed threshold is 20 miles per hour (mph) or 60 percent of the posted speed limit, whichever is greater. - Travel times in this measure are derived from the National Performance Management Research Data Set (NPMRDS v2), based on archived probe-based traffic data. Traffic volumes and vehicle mix (cars, buses, and trucks) are from the national Highway Performance Monitoring System (HPMS). Vehicle occupancies (Cars - 1.7, Buses - 13.3, and trucks - 1.0) and time-of-day travel distributions are from national survey data and established estimation formulas. The Probe Data Analytics (PDA) Suite Map-21 tools were used to process this data and calculate the PHED measure. - The NPMRDS data is new and imperfect, but the best source available and approved for use. It is appropriate to consider the analysis for this measure to be also considered a “benchmark”. - The baseline year is 2017 and the required 4-year target refers to travel in 2021. • Trends - The 2016 annual hours of PHED per capita was compared to the 2017 baseline. Other related measures of congestion were compared and trended including DOT vehicle miles traveled, DVPRC Travel Demand Model traffic forecasts, DVRPC population and employment forecasts, vehicle registrations (Pennsylvania only) and National Transit Database passenger miles. All showed recent increases. - With economic growth, increases in the number of people traveling, and the movement of freight on NHS roadways, delay would likely increase. This would be only partially balanced by population growth reflected in the “per capita” portion of the measure. • Considerations and Uncertainties in Meeting Established Targets - Transportation investment resources in the urbanized area are (by necessity) largely directed toward preserving the existing system, resulting in fewer programs and projects to mitigate delay overall on NHS roadways. - Transportation system management and operations should moderate the expected increase in travel delay. Minimal new NHS road capacity is being added in the urbanized area in the near term. - The ability of the existing public transit system to accommodate increased ridership is limited over the time frame of the targets. - Continued increase in Non-Single Occupant Vehicle (Non-SOV) travel would mitigate growth in traffic delay to some extent. - Shifting trip-making to outside peak hours would improve this measure (while potentially contributing to excessive delay at other times). - Changes in pricing (e.g. tolls, fuel costs and transit fares) would potentially reduce excessive delay. - The impacts of transportation network companies (TNCs) and of emerging advanced transportation technology in terms of congestion are still being understood. These may lead to increases or decreases in this measure. - Land use, housing locations, and work locations will continue to affect trip making and the traffic on NHS roads. - Variability in the trends and limited historical data affects this measure. - The limitations of the current data, in part involving conflation of HPMS traffic data to NPMRDS, add uncertainty to the values for this measure. • Approach - Based on these considerations, the Philadelphia Urbanized Area MPOs and State DOT partners are agreeing that an appropriate 4-year target (2017-21) would increase a small amount (or 0.6% per year). - This 4-year target number of 17.2 reflects the number derived at the time of "pencils down" Urbanized Area Coordination meeting with the agencies listed above in D8. - The partners expect to revisit and likely adjust this target in two years as allowed by FHWA.
(Pennsylvania) Historic vehicle miles traveled (VMT) and INRIX probe speed data suggest increasing delay trends. The MPO travel model indicates potential increases to VMT and delay. Through a combination of MPO staff input, travel model forecasts, VMT and vehicle registration trends, and economic forecast information, higher delay targets were established. For the Philadelphia Urbanized Are (UZA), Delaware Valley Regional Planning Commission (DVRPC) estimates a 0.6% annual increase in delay per capita. The PHED target will be reassessed at the mid-term period based on additional data and trends.
N/AN/AN/A(Delaware) For Delaware the PHED targets were met for 2-Year and 4-Year target intervals. Each of the PM 3 targets, including PHED for Delaware anticipated a series of maintenance and capital projects on the Delaware Interstate system would occur between the FY 2018 and FY 2025, and that those targets needed to reflect potentially how much those Interstate construction projects would impact both peak hour and daily travel during the construction periods and project durations. CTP project development patterns also reflected an agency goal of completing a range of smaller congestion-reducing projects (mainly intersection upgrades in and around the City of Wilmington) prior to initiating construction on these interstate system projects.
(Maryland) The 4-year target was 17.2 and the 4-year performance was 13.1, or 4.1 less than the target, which indicates that the congestion reduction goal was achieved. There were various programs and projects in the state TIPs within the urbanized area that were programmed with the intention of reducing excessive delay. There were also programs implemented by the TMAs within the urbanized area to encourage everything but driving alone, with the intention of reducing some excessive delay.
(New Jersey) There were various programs and projects in the state TIPs within the urbanized area that were programmed with the intention of reducing excessive delay. There were also programs implemented by the TMAs within the urbanized area to encourage everything but driving alone, with the intention of reducing some excessive delay. The 4-year target was 17.2 and the 4-year performance was 13.1, or 4.1 less than the target, which indicates that the congestion reduction goal was achieved. Moving forward, the goal is to continue to program congestion reduction projects to reduce excessive delay, and to measure performance through the target setting process
(Pennsylvania) The Philadelphia UZA actual performance has achieved the 4-year PHED target. The Philadelphia UZA PHED target was established to maintain the pre-existing investment decisions and the assumption this would result in similar levels of performance. This largely proved true during the first half of the performance period until travel behaviors changed dramatically during and after the COVID-19 pandemic.
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Data Sources:
2018, 2020, 2022 Biennial Performance Report
2020, 2021, 2022 HPMS Data Submittal
Non-Single Occupancy Vehicle (Non-SOV) Travel
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Non-Single Occupancy Vehicle (Non-SOV) Travel 2017 2018 2019 2020 2021 Condition/Performance 27.9 27.9 28.2 28.5 30.6 Targets -- -- 28.0 -- 28.1 -
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
- Basis for Targets
- 2-yr Progress
- Basis for 4-yr Target Adjustment
- 4-yr Planned Activities
- 4-yr Progress
(Delaware) Discussion noted that continued increases in ridesharing, transportation network companies (TNCs), walking, and biking would contribute to increases in this particular measure.
Land use, housing locations, and work locations would continue to affect trip-making and the use of non-SOV modes.
Changes in pricing (e.g. tolls, fuel costs and transit fares) could affect this measure.
Based on these considerations, the Philadelphia Urbanized Area MPOs and State DOT partners agreed that there would be a slight increase in targets from the baseline, with an appropriate 2-year target (2016-18) and 4-year target (2016-20) at 28.0% and 28.1%, respectively.
(Marlyand) The percent of non-SOV travel targets were calculated by Delaware Valley Regional Planning Commission in coordination with applicable agencies. A best-fit linear trend analysis was completed considering data from 2012 to 2016 ACS.
(New Jersey) The following points are considered in the Non-SOV Target Setting process. • Policy Goals - This Congestion Mitigation and Air Quality Program performance measure considers the role that single-occupant vehicles play in contributing to traffic congestion and pollutant emissions. - Goals of all partner agencies (T11) reflect strong support for increasing non-single-occupant modes, including public transit, ridesharing, walking, and biking. • Data - Non-Single Occupant Vehicle (SOV) travel includes carpool, train, bus, walk, bike, taxi, rideshare, working at home, etc.; anything other than driving alone. - Percent Non-SOV Travel for the urbanized area is calculated using U.S. Census American Community Survey (ACS) data 5-year estimates for journey-to-work trips for residents within the urbanized area. While all trips (not just journey-to-work) would be ideal to track, this regularly updated, approved dataset is recognized as the best available. - The data reflects five-year averages, with a time lag. The baseline refers to 2012-2016 values, the 2-year target from 2016-2018, and the 4-year target from 2016-2020. • Trends - ACS percent Non-SOV Travel data trends show slight increases in percent non-SOV travel from 5-year ACS (2007-11) to 5-year ACS (2012-16). A linear trend was used to establish 2- and 4-year targets. - This is a percentage measure. If trip making continues to grow, the absolute number of non-SOV trips would increase even if the percentage stays the same. • Considerations and Uncertainties in Meeting Established Targets - There is a two-year time lag in reporting of data, so any non-SOV completed project would not be reflected in the measure until two years later. Additionally, reflected changes are incremental due to five-year averages intrinsic to this measure. - The ability of the existing public transit system to accommodate increased ridership is limited, and the expansion of the transit network is limited over the target time frame. - Continued increases in ridesharing, transportation network companies (TNCs), walking, and biking would contribute to increases in this measure. - Land use, housing locations, and work locations will continue to affect trip-making and the use of non-SOV modes. - Changes in pricing (e.g. tolls, fuel costs and transit fares) could affect this measure. • Approach - Based on these considerations, the Philadelphia Urbanized Area MPOs and State DOT partners are agreeing that there would be a slight increase in targets from the baseline, with an appropriate 2-year target (2016-18) and 4-year target (2016-20) at 28.0% and 28.1%, respectively.
(Pennsylvania) Travel trends show slight increases in Percent Non-SOV travel from five-year American Community Survey (ACS) (2007–2011) to five-year ACS (2012–2016). A linear trend was used to establish two- and four-year targets. The 2017 baseline refers to 2012–2016 values.
(Delaware) The 2018 Baseline Report indicated a 2016 base year measurement of 27.5% (from the 2012 to 2016 American Community Survey – ACS). The year prior to that indicated a 2015 measurement of 27.7%. (from the 2011 to 2015 ACS) and the year prior to that a 2014 measurement of 27.5% (from the 2010 to 2014 ACS). The 2018 Baseline Report also presented a 2-Year Target of more than 28.0% and a 4-Year Target of more than 28.1%. This Mid-Performance Period Report indicates the 2017 actual measurement (from the 2013 to 2017 ACS) was 27.9%, and the 2018 actual measurement (from the 2014 to 2018 ACS) was 28.1% The 2017 and 2018 actual measurements were just slightly above the 2-Year Target of 28.0% and meet the 4-Year Target of 28.1%. The 2017 and 2018 actual measurements closely align with the trend established in the 2018 Baseline Report and no changes to the 2-Year or 4-Year targets are needed at this time. The criteria used by the multi-agency working group to establish the 2-Year and 4-Year targets was presented in the 2018 Baseline report. Through the spring and summer of 2020, that same working group met virtually several times to determine best path forward, with the discussion leading toward retaining the original 2-Year and 4-Year targets for Percent of Non-SOV Travel.
(Maryland) The Percent Non-SOV Travel measure was calculated for the interim performance period using the most recent U.S. Census ACS five-year estimates (2014-2018) with a result of 28.2 percent. The four-year linear trend value was based on the most recent non-overlapping five-year ACS estimates (2009-2013 and 2014-2018), that resulted in 28.5 percent, which was higher than the previously adopted four-year target set in 2018 of 28.1 percent (a difference of 0.4 percent), and exceeded target expectations. Since the four-year target and trend value were fairly close (0.4 percent), and because of the uncertainty in travel patterns in the foreseeable future due to the COVID-19 pandemic, there was consensus among the partners to not adjust the target.
(New Jersey) The Philadelphia urbanized area has achieved an estimated 2-year non-SOV % of 28.2% versus the 2-year target of 28.0%. Partners in the region have advanced a robust mix of projects that encourage the use of alternate forms of transportation and non-SOV travel. Bike/Ped improvement projects, such as the West Bank Pedestrian and Bikeway Improvement Project, education/outreach programs, such as the Mobility Alternatives Program, and various transit projects which include the purchase of new vehicles and the expansion of transit station parking, have contributed to the region’s success in increasing the percent of non-SOV travel. Based on the ongoing level of uncertainty due to COVID-19, there was consensus among the partners to not adjust the target.
(Pennsylvania) The Philadelphia region has had increases in the non-SOV regional travel percentage and meets the 2-year target. At this time, the region would also meet the 4-year target if trends continue over the next 2 years.
N/AN/A(Delaware) The 2017 and 2018 actual measurements were just slightly above the 2-Year Target of 28.0% and meet the 4-Year Target of 28.1%. The 2017 and 2018 actual measurements closely align with the trend established in the 2018 Baseline Report and no changes to the 2-Year or 4-Year targets are needed at this time. The criteria used by the multi-agency working group to establish the 2-Year and 4-Year targets was presented in the 2018 Baseline report. Through the spring and summer of 2020, that same working group met virtually several times to determine best path forward, with the discussion leading toward retaining the original 2-Year and 4-Year targets for Percent of Non-SOV Travel. Actual performance for 2020, 2021, and 2022 to date also meet the 4 Year target for the SMSA. Project selections in Delaware included transit enhancements as part of the I-95 reconstruction project traffic mitigation plan, as well as retaining the Ridesharing Delaware program (CMAQ funded) within DTC.
(Maryland) There were various programs and projects in the state TIPs within the urbanized area that were programmed with the intention of increasing non-SOV travel and reducing driving alone, such as transit, pedestrian, and bicyclist improvements. There were also programs implemented by the TMAs within the urbanized area to encourage everything but driving alone.
(New Jersey) There were various programs and projects in the state TIPs within the urbanized area that were programmed with the intention of increasing non-SOV travel and reducing driving alone, such as transit, pedestrian, and bicyclist improvements. There were also programs implemented by the TMAs within the urbanized area to encourage everything but driving alone. The 4-year target was 28.1 percent and the 4-year performance was 30.6 percent, or 2.5 percent more than the target, which indicates that the percent Non-SOV travel target goal was achieved. Moving forward, the goal is to continue to program multimodal projects to increase percent Non-SOV travel, and to measure performance through the target setting process.
(Pennsylvania) The Philadelphia UZA has met the 4-year target for the Non-SOV measure. It is unclear at this time if the MPO decisions or CMAQ investments have had any impact on specific performance for this measure. Other factors including the COVID-19 pandemic, increases to the number of commuters working at home, and transit ridership trends have a much larger role in affecting Non-SOV performance.
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Data Sources:
2018, 2020, 2022 Biennial Performance Report
2018, 2019, 2020, 2021, 2022 HPMS Data Submittal