Changes
On September 25, 2025 at 6:12:19 PM UTC,
-
Changed value of field
publication_date
of resource Average Highest PTI Across Peaks by Agency, Division Type, and Region to2025-09-25
(previously2025-09-10
) in Congestion
| f | 1 | { | f | 1 | { |
| 2 | "author": null, | 2 | "author": null, | ||
| 3 | "author_email": null, | 3 | "author_email": null, | ||
| 4 | "category": [ | 4 | "category": [ | ||
| 5 | "Long-Range Plan", | 5 | "Long-Range Plan", | ||
| 6 | "Planning", | 6 | "Planning", | ||
| 7 | "Roadways" | 7 | "Roadways" | ||
| 8 | ], | 8 | ], | ||
| 9 | "citation": "", | 9 | "citation": "", | ||
| 10 | "creator_user_id": "f666d0b8-ac4a-4476-96eb-36f7bdc69d58", | 10 | "creator_user_id": "f666d0b8-ac4a-4476-96eb-36f7bdc69d58", | ||
| 11 | "dataset_level": "Tabular", | 11 | "dataset_level": "Tabular", | ||
| 12 | "groups": [], | 12 | "groups": [], | ||
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| 14 | "isopen": false, | 14 | "isopen": false, | ||
| 15 | "license_id": "notspecified", | 15 | "license_id": "notspecified", | ||
| 16 | "license_title": "DVRPC Data License", | 16 | "license_title": "DVRPC Data License", | ||
| 17 | "license_url": "https://catalog.dvrpc.org/dvrpc_data_license.html", | 17 | "license_url": "https://catalog.dvrpc.org/dvrpc_data_license.html", | ||
| 18 | "maintainer": null, | 18 | "maintainer": null, | ||
| 19 | "maintainer_email": null, | 19 | "maintainer_email": null, | ||
| 20 | "managing_users": "ischwarzenberg@dvrpc.org,jdobkin@dvrpc.org", | 20 | "managing_users": "ischwarzenberg@dvrpc.org,jdobkin@dvrpc.org", | ||
| 21 | "metadata_created": "2024-08-05T19:04:06.439258", | 21 | "metadata_created": "2024-08-05T19:04:06.439258", | ||
| n | 22 | "metadata_modified": "2025-09-25T18:11:58.664801", | n | 22 | "metadata_modified": "2025-09-25T18:12:18.960604", |
| 23 | "methodology": "", | 23 | "methodology": "", | ||
| 24 | "name": "congestion", | 24 | "name": "congestion", | ||
| 25 | "notes": "One measure used to analyze roadway reliability is the | 25 | "notes": "One measure used to analyze roadway reliability is the | ||
| 26 | Planning Time Index (PTI). It is the ratio of the 95th percentile | 26 | Planning Time Index (PTI). It is the ratio of the 95th percentile | ||
| 27 | travel time relative to the free-flow (uncongested) travel time. PTI | 27 | travel time relative to the free-flow (uncongested) travel time. PTI | ||
| 28 | helps in understanding the impacts of nonrecurring congestion from | 28 | helps in understanding the impacts of nonrecurring congestion from | ||
| 29 | crashes, weather, and special events. It approximates the extent to | 29 | crashes, weather, and special events. It approximates the extent to | ||
| 30 | which a traveler should add extra time to their trip to ensure on-time | 30 | which a traveler should add extra time to their trip to ensure on-time | ||
| 31 | arrival at their destination. A value of 1.0 indicates a person can | 31 | arrival at their destination. A value of 1.0 indicates a person can | ||
| 32 | expect free-flow speeds along their route. A 2.0 index value indicates | 32 | expect free-flow speeds along their route. A 2.0 index value indicates | ||
| 33 | a traveler should expect that the trip could be twice as long as | 33 | a traveler should expect that the trip could be twice as long as | ||
| 34 | free-flow conditions. PTI values from 2.0 to 3.0 indicate moderate | 34 | free-flow conditions. PTI values from 2.0 to 3.0 indicate moderate | ||
| 35 | unreliability, and ones greater than 3.0 are highly unreliable. | 35 | unreliability, and ones greater than 3.0 are highly unreliable. | ||
| 36 | \r\n\r\nThe data comes from aggregated Global Positioning System probe | 36 | \r\n\r\nThe data comes from aggregated Global Positioning System probe | ||
| 37 | data\u2014anonymized data from mobile apps, connected vehicles, and | 37 | data\u2014anonymized data from mobile apps, connected vehicles, and | ||
| 38 | commercial fleets\u2014provided to the Probe Data Analytics (PDA) | 38 | commercial fleets\u2014provided to the Probe Data Analytics (PDA) | ||
| 39 | Suite by INRIX, a travel data technology company. The PDA Suite was | 39 | Suite by INRIX, a travel data technology company. The PDA Suite was | ||
| 40 | created by a consortium of sponsors, including the Eastern | 40 | created by a consortium of sponsors, including the Eastern | ||
| 41 | Transportation Coalition and the University of Maryland.\r\n\r\nPTI | 41 | Transportation Coalition and the University of Maryland.\r\n\r\nPTI | ||
| 42 | values by region, subregion, and county are grouped either as highway | 42 | values by region, subregion, and county are grouped either as highway | ||
| 43 | facilities or local roads. Highways are roadway segments classified as | 43 | facilities or local roads. Highways are roadway segments classified as | ||
| 44 | interstates, turnpikes, and expressways in the PDA Suite. Local roads | 44 | interstates, turnpikes, and expressways in the PDA Suite. Local roads | ||
| 45 | are segments classified as U.S. routes, state routes, parkways, | 45 | are segments classified as U.S. routes, state routes, parkways, | ||
| 46 | frontages, and others. The PDA Suite reports weekday hourly averages | 46 | frontages, and others. The PDA Suite reports weekday hourly averages | ||
| 47 | by facility type and direction. Average weekday values are reported by | 47 | by facility type and direction. Average weekday values are reported by | ||
| 48 | facility type and direction, within the following time | 48 | facility type and direction, within the following time | ||
| 49 | periods:\r\n\r\n- Morning (AM): 6:00 AM\u20139:59 AM;\r\n- Midday | 49 | periods:\r\n\r\n- Morning (AM): 6:00 AM\u20139:59 AM;\r\n- Midday | ||
| 50 | (MD): 10:00 AM\u20132:59 PM;\r\n- Evening (PM): 3:00 PM\u20136:59 | 50 | (MD): 10:00 AM\u20132:59 PM;\r\n- Evening (PM): 3:00 PM\u20136:59 | ||
| 51 | PM;\r\n- Nighttime (NT): 7:00 PM\u20135:59 AM; and\r\n- Daily: 12:00 | 51 | PM;\r\n- Nighttime (NT): 7:00 PM\u20135:59 AM; and\r\n- Daily: 12:00 | ||
| 52 | AM\u201311:59 PM.\r\n\r\nAlthough INRIX data collection precedes years | 52 | AM\u201311:59 PM.\r\n\r\nAlthough INRIX data collection precedes years | ||
| 53 | reported in Tracking Progress, early years of reporting are highly | 53 | reported in Tracking Progress, early years of reporting are highly | ||
| 54 | variable based on a lack of facility coverage. The years from 2011 | 54 | variable based on a lack of facility coverage. The years from 2011 | ||
| 55 | onward show higher stability for highway facilities for most counties | 55 | onward show higher stability for highway facilities for most counties | ||
| 56 | and for the region. For local facilities, 2014 and beyond is where | 56 | and for the region. For local facilities, 2014 and beyond is where | ||
| 57 | values seem most stable due to more widespread facility | 57 | values seem most stable due to more widespread facility | ||
| 58 | coverage.\r\n\r\nHistoric data for the federal Transportation | 58 | coverage.\r\n\r\nHistoric data for the federal Transportation | ||
| 59 | Performance Management (TPM) system performance reporting requirements | 59 | Performance Management (TPM) system performance reporting requirements | ||
| 60 | is shown. These are Level of Travel Time Reliability (LOTTR), Level of | 60 | is shown. These are Level of Travel Time Reliability (LOTTR), Level of | ||
| 61 | Truck Travel Time Reliability (TTTR), and Annual Hours of Peak-Hour | 61 | Truck Travel Time Reliability (TTTR), and Annual Hours of Peak-Hour | ||
| 62 | Excessive Delay (AHPHED). The entire states of Pennsylvania and New | 62 | Excessive Delay (AHPHED). The entire states of Pennsylvania and New | ||
| 63 | Jersey are included for LOTTR and TTTR, so the region\u2019s figures | 63 | Jersey are included for LOTTR and TTTR, so the region\u2019s figures | ||
| 64 | can be compared with statewide data.\r\n\r\nLOTTR is used to calculate | 64 | can be compared with statewide data.\r\n\r\nLOTTR is used to calculate | ||
| 65 | the percentage of roadway segments that are considered reliable. A | 65 | the percentage of roadway segments that are considered reliable. A | ||
| 66 | road segment with an LOTTR of less than 1.5 is considered reliable. | 66 | road segment with an LOTTR of less than 1.5 is considered reliable. | ||
| 67 | Reliable segment lengths in miles are multiplied by their Annual | 67 | Reliable segment lengths in miles are multiplied by their Annual | ||
| 68 | average daily traffic (AADT) values times the average number of people | 68 | average daily traffic (AADT) values times the average number of people | ||
| 69 | in a vehicle. Then, this sum is then divided by the exact same product | 69 | in a vehicle. Then, this sum is then divided by the exact same product | ||
| 70 | for all road segments, to get the resulting percentage of roadway that | 70 | for all road segments, to get the resulting percentage of roadway that | ||
| 71 | is reliable for the geography. \r\n\r\nTTTR measures how consistent | 71 | is reliable for the geography. \r\n\r\nTTTR measures how consistent | ||
| 72 | travel times are for trucks on interstates. This can be helpful with | 72 | travel times are for trucks on interstates. This can be helpful with | ||
| 73 | analyzing goods movement along the region\u2019s interstates. TTTR is | 73 | analyzing goods movement along the region\u2019s interstates. TTTR is | ||
| 74 | calculated by dividing the 95th percentile of travel times by the 50th | 74 | calculated by dividing the 95th percentile of travel times by the 50th | ||
| 75 | percentile of travel times, using the highest value over the Morning | 75 | percentile of travel times, using the highest value over the Morning | ||
| 76 | (AM), Midday (MD), Evening (PM), Nighttime (NT), and weekend. Each | 76 | (AM), Midday (MD), Evening (PM), Nighttime (NT), and weekend. Each | ||
| 77 | interstate segment multiplies its length by the travel time ratio, the | 77 | interstate segment multiplies its length by the travel time ratio, the | ||
| 78 | results are summed and then divided by total Interstate length in the | 78 | results are summed and then divided by total Interstate length in the | ||
| 79 | geography to determine the area\u2019s TTTR value. \r\n\r\nAHPHED is | 79 | geography to determine the area\u2019s TTTR value. \r\n\r\nAHPHED is | ||
| 80 | the average number of hours per year spent by motorists driving in | 80 | the average number of hours per year spent by motorists driving in | ||
| 81 | congestion during peak periods. This can be useful for analyzing the | 81 | congestion during peak periods. This can be useful for analyzing the | ||
| 82 | impact of congestion from an individual\u2019s perspective, since it | 82 | impact of congestion from an individual\u2019s perspective, since it | ||
| 83 | analyzes how many hours the average person spends stuck in congestion. | 83 | analyzes how many hours the average person spends stuck in congestion. | ||
| 84 | The figures used are based on the 2010 urbanized area boundaries in | 84 | The figures used are based on the 2010 urbanized area boundaries in | ||
| 85 | the Census. In 2020, they were renamed to urban areas. There are only | 85 | the Census. In 2020, they were renamed to urban areas. There are only | ||
| 86 | Mercer County PHED values from 2021 onward, because they only apply to | 86 | Mercer County PHED values from 2021 onward, because they only apply to | ||
| 87 | the second four-year TPM performance period, when the Trenton, NJ | 87 | the second four-year TPM performance period, when the Trenton, NJ | ||
| 88 | Urban Area was required to track metrics and set performance targets. | 88 | Urban Area was required to track metrics and set performance targets. | ||
| 89 | AHPHED per capita is that figure divided by the urban area\u2019s | 89 | AHPHED per capita is that figure divided by the urban area\u2019s | ||
| 90 | population during that year.\r\n\r\nIt is also important to measure | 90 | population during that year.\r\n\r\nIt is also important to measure | ||
| 91 | PTIs along the roads buses travel, to measure how reliable the roads | 91 | PTIs along the roads buses travel, to measure how reliable the roads | ||
| 92 | are that commuters travel on. To calculate the agency and division | 92 | are that commuters travel on. To calculate the agency and division | ||
| 93 | type combination PTIs, for each route, all their segments\u2019 | 93 | type combination PTIs, for each route, all their segments\u2019 | ||
| 94 | planning times from 7-8 AM, 8-9 AM, 4-5 PM, and 5-6 PM are first | 94 | planning times from 7-8 AM, 8-9 AM, 4-5 PM, and 5-6 PM are first | ||
| 95 | summed. Then, those are divided by the sums of those segments' | 95 | summed. Then, those are divided by the sums of those segments' | ||
| 96 | free-flow travel times for those same time periods, to get one PTI per | 96 | free-flow travel times for those same time periods, to get one PTI per | ||
| 97 | time period for each route. Then, the highest of those four PTIs is | 97 | time period for each route. Then, the highest of those four PTIs is | ||
| 98 | taken to get one maximum peak hour PTI per route. Then, for each | 98 | taken to get one maximum peak hour PTI per route. Then, for each | ||
| 99 | agency and division type combination, all of their routes\u2019 | 99 | agency and division type combination, all of their routes\u2019 | ||
| 100 | maximum peak hour PTIs are averaged for each year to get the PTIs. | 100 | maximum peak hour PTIs are averaged for each year to get the PTIs. | ||
| 101 | Since all NJ Transit routes in the DVRPC region are part of their | 101 | Since all NJ Transit routes in the DVRPC region are part of their | ||
| 102 | Southern Division, NJ Transit only has one agency and division mode | 102 | Southern Division, NJ Transit only has one agency and division mode | ||
| 103 | combination. SEPTA has two: \u201cCity\u201d and \u201cSuburban\u201d. | 103 | combination. SEPTA has two: \u201cCity\u201d and \u201cSuburban\u201d. | ||
| 104 | SEPTA splits their bus routes into their urban routes, all within | 104 | SEPTA splits their bus routes into their urban routes, all within | ||
| 105 | their City Transit Division, and their suburban routes, which are in | 105 | their City Transit Division, and their suburban routes, which are in | ||
| 106 | their Victory and Frontier divisions. The Victory and Frontier | 106 | their Victory and Frontier divisions. The Victory and Frontier | ||
| 107 | divisions have been grouped into their own \u201cSuburban\u201d | 107 | divisions have been grouped into their own \u201cSuburban\u201d | ||
| 108 | division type.\r\n\r\nCongestion is susceptible to external forces | 108 | division type.\r\n\r\nCongestion is susceptible to external forces | ||
| 109 | like the economy. A downturn can reduce congestion, but this reflects | 109 | like the economy. A downturn can reduce congestion, but this reflects | ||
| 110 | fewer and shorter trips for households and businesses during lean | 110 | fewer and shorter trips for households and businesses during lean | ||
| 111 | times and may not represent an improvement. Therefore, it may be | 111 | times and may not represent an improvement. Therefore, it may be | ||
| 112 | useful to correlate these results with the Miles Driven indicator.", | 112 | useful to correlate these results with the Miles Driven indicator.", | ||
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| 117 | "approval_status": "approved", | 117 | "approval_status": "approved", | ||
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