Traffic volumes; measured and calculated amounts of vehicle traffic that travel the sections of road.

Traffic sensors at over 1,200 locations in Allegheny County collect vehicle counts for the Pennsylvania Department of Transportation. Data included in the Health Department's DASH project includes hourly averages and average daily counts. The data was collected from years 2012-2014 and compiled by Carnegie Mellon University’s Traffic21 Institute.

Support for Health Equity datasets and tools provided by Amazon Web Services (AWS) through their Health Equity Initiative.

Traffic volumes; measured and calculated amounts of vehicle traffic that travel the sections of road.

State-owned and maintained public roads within Pennsylvania as extracted from the PENNDOT Roadway Management System (RMS). Includes fields describing pavement type, traffic volumes and other information. The Administrative version is used for reporting purposeslike the federal aid system and federal functional classification.

Maps displays NCRPO's total volume of vehicle traffic of a highway for a year divided by 365 days. In it's simplest term, it serves as a measurement of how busy or congested a roadway is.

Traffic volumes; measured and calculated amounts of vehicle traffic that travel the section of road. For more information on this layer, click here.

Traffic volumes; measured and calculated amounts of vehicle traffic that travel the section of road.=For more information on this layer, you can use the Data Dictionary available in both web and spreadsheet format.

Analyzing congestion at the roadway corridor facility level, rather than by roadway segment, can give a better understanding of why some roadway corridors are performing better than others, and enables congestion to be tracked over time. There are 336 Focus Roadway Corridor Facilities in the DVRPC region – 205 in Pennsylvania and 131 in New Jersey. Focus Roadway Corridor Facilities are used to prioritize congested locations and develop a set of focused strategies to manage congestion. Facility limits are delineated based on where there are breaks between Congested Corridor and Subcorridor Areas, and between major interchanges and arterial roadways. Ramps are not included due to lack of traffic volume data, but freeway to freeway interchanges (e.g. I-76 to I-676) with traffic volume data are included. These facilities are symbolized by rank in delay from high to low in quartiles separately for the Pennsylvania and New Jersey subregions, with brown locations being the most delayed and yellow the least. Rank is based on travel time and planning time (95th percentile) vehicle and volume delays. Analyzing congestion at the roadway corridor facility level, rather than by roadway segment, can give a better understanding of why some roadway corridors are performing better than others, and enables congestion to be tracked over time. There are 336 Focus Roadway Corridor Facilities in the DVRPC region – 205 in Pennsylvania and 131 in New Jersey. Focus Roadway Corridor Facilities are used to prioritize congested locations and develop a set of focused strategies to manage congestion. Facility limits are delineated based on where there are breaks between Congested Corridor and Subcorridor Areas, and between major interchanges and arterial roadways. Ramps are not included due to lack of traffic volume data, but freeway to freeway interchanges (e.g. I-76 to I-676) with traffic volume data are included. These facilities are symbolized by rank in delay from high to low in quartiles separately for the Pennsylvania and New Jersey subregions, with brown locations being the most delayed and yellow the least. Rank is based on travel time and planning time (95th percentile) vehicle and volume delays. CMP Focus Roadway Corridor Facility Database Fields MAPID – Unique identifier for the focus roadway corridor facility; NJ identifier starts at 300 ROADWAY – Roadway name of the facility FROMLIMIT – From limit roadway of the facility TOLIMIT – To limit roadway of the facility MILES – Length of the facility in miles LIMITACCES – Facility access type Valid values: Yes – Limited access No – Not limited access MUNICIPAL – Municipalities the facility is within; if too many then indicate “various” COUNTY – County the facility is within COUNTY_ONE – One County with the majority of the focus roadway corridor AADT – Annual Average Daily Traffic for both directions of travel TTAMVEHDEL – Highest AM Peak Vehicle Travel Time Delay TTPMVEHDEL – Highest PM Peak Travel Time Vehicle Delay TTHIVEHDEL – Highest of AM and PM Peak Travel Time Vehicle Delay TTTDVEHDEL – Time of Day of Highest Peak Travel Time Vehicle Delay Valid values: AM – AM Peak PM – PM Peak TTRKVEHDEL – Highest Peak Travel Time Vehicle Delay Rank with lower numbers indicating the most delay TTRKVOLDEL – Highest Peak Travel Time Volume Delay Rank with lower numbers indicating the most delay TTAMVOLDEL – Highest Peak Travel Time AM Volume Delay TTPMVOLDEL – Highest Peak Travel Time PM Volume Delay TTHIVOLDEL – Highest of AM and PM Peak Travel Time Volume Delay TTTDVOLDEL – Time of Day of Highest Peak Travel Time Volume Delay Valid values: AM – AM Peak PM – PM Peak TTAMHHMMSS – Highest of AM Peak Travel Time Volume Delay in HH:MM:SS TTPMHHMMSS – Highest of PM Peak Travel Time Volume Delay in HH:MM:SS PTAMVEHDEL – Highest AM Peak Planning Time Vehicle Delay PTPMVEHDEL – Highest PM Peak Planning Time Vehicle Delay PTHIVEHDEL – Highest of AM and PM Peak Planning Time Vehicle Delay PTTDVEHDEL – Time of Day of Highest Peak Planning Time Vehicle Delay Valid values: AM – AM Peak PM – PM Peak PTRKVEHDEL – Highest Peak Planning Time Vehicle Delay Rank with lower numbers indicating the most delay PTRKVOLDEL – Highest Peak Planning Time Volume Delay Rank with lower numbers indicating the most delay PTAMVOLDEL – Highest Peak Planning Time AM Volume Delay PTPMVOLDEL – Highest Peak Planning Time PM Volume Delay PTHIVOLDEL – Highest of AM and PM Peak Planning Time Volume Delay PTTDVOLDEL – Time of Day of Highest Peak Planning Time Volume Delay Valid values: AM – AM Peak PM – PM Peak PTAMHHMMSS – Highest of AM Peak Planning Time Volume Delay in HH:MM:SS PTPMHHMMSS – Highest of PM Peak Planning Time Volume Delay in HH:MM:SS TKAADT – Truck Annual Average Daily Traffic for both directions of travel TKRKVOLDEL – Highest Truck Peak Planning Time Volume Delay Rank with lower numbers indicating the most delay TKAMVOLDEL – Highest Truck Peak Planning Time AM Volume Delay TKPMVOLDEL – Highest Truck Peak Planning Time PM Volume Delay TKHIVOLDEL – Highest of AM and PM Truck Peak Planning Time Volume Delay TKTDVOLDEL – Time of Day of Highest Peak Truck Planning Time Volume Delay Valid values: AM – AM Peak PM – PM Peak TKAMHHMMSS – Highest of AM Peak Truck Planning Time Volume Delay expressed in HH:MM:SS TKPMHHMMSS – Highest of PM Peak Truck Planning Time Volume Delay expressed in HH:MM:SS STATE – State the facility is within LOTTRMAXMI – Miles of travel time unreliability for the measure (1.50 or more) LOTTRMAXPM – Miles of travel time unreliability (LOTTRMAXMI) per road mile TTTRMAXMI – Miles of truck travel time unreliability for the measure (2.00 or more)
TTTRMAXPM – Miles of truck travel time unreliability (TTTRMAXMI) per road mile PHEDVAPMI – Total Peak Hours of Excessive Delay weighted by road miles
MAXVCMI – Miles of Travel Demand Model forecasted congestion V/C greater than or equal to 0.85 in 2050
MAXVCPMI – Miles of Travel Demand Model forecasted congestion (MAXVCMI) per road mile CRINDEXMI – Miles of high crash rate for the measure CRINDEXPMI – Miles of high crash rate (CRINDEXMI) per road mile CRINDEXMI – Miles of high crash severity for the measure CRINDEXPMI – Miles of high crash severity (CRINDEXMI) per road mile IMRMAXPMI – CMP Objective Measure score to increase mobility and reliability and meet PM3 targets weighted by road miles, where the maximum score is 4.0 IMRMAXPR – CMP Objective Measure rank of IMRMAXPMI where lower values represent higher scores IMIAMAXPMI – CMP Objective Measure score to integrate modes and provide transit where it is most needed weighted by road miles, where the maximum score is 2.0 IMIAMAXPR – CMP Objective Measure rank of IMIAMAXPMI where lower values represent higher scores MRMAXPMI – CMP Objective Measure score to modernize and maintain the existing transportation network, where the maximum score is 1.5 MRMAXPR – CMP Objective Measure rank of MRMAXPMI where lower values represent higher scores SVRMAXPMI – CMP Objective Measure score to achieve Vision Zero, where the maximum score is 2.0 SVRMAXPR – CMP Objective Measure rank of SVRMAXPMI where lower values represent higher scores GCMAXPMI – CMP Objective Measure score to maintain the movement of goods by truck and meet PM3 targets, where the maximum score is 1.5 GCMAXPR – CMP Objective Measure rank of GCMAXPMI where lower values represent higher scores SPMAXPMI – CMP Objective Measure score to maintain and enhance transportation security and prepare for major events, where the maximum score is 1.0 SPMAXPR – CMP Objective Measure rank of SPMAXPMI where lower values represent higher scores LRPMAXPMI – CMP Objective Measure score to support LRP centers, infill, redevelopment and emerging growth areas, environmental sensitive areas, and Environmental Justice and Equity populations, where the maximum score is 3.0 LRPMAXPR – CMP Objective Measure rank of LRPMAXPMI where lower values represent higher scores CMPMAXPMI – Total of of the CMP Objective Measure scores, where the maximum score is 15.0 CMPMAXPR – Total CMP Objective Measure rank of CMPMAXPMI where lower values represent higher scores VOLDEL2017 – Volume Delay 5-6 PM in 2017 VOLDEL2021 – Volume Delay 5-6 PM in 2021 VOLDEL2022 – Volume Delay 5-6 PM in 2022 Below are the new fields as of February 14, 2025 TTD070817 – 7-8 AM Travel Time Vehicle Delay in 2017 TTD171817 – 5-6 PM Travel Time Vehicle Delay in 2017 TTTD070817 – 7-8 AM Travel Time Volume Delay in 2017 TTTD171817 – 5-6 PM Travel Time Volume Delay in 2017 PTD070817 – 7-8 AM Planning Time Vehicle Delay in 2017 PTD171817 – 5-6 PM Planning Time Vehicle Delay in 2017 PTTD070817 – 7-8 AM Planning Time Volume Delay in 2017 PTTD171817 – 5-6 PM Planning Time Volume Delay in 2017 TTD070821 – 7-8 AM Travel Time Vehicle Delay in 2021 TTD080921 – 8-9 AM Travel Time Vehicle Delay in 2021 TTD161721 – 4-5 PM Travel Time Vehicle Delay in 2021 TTD171821 – 5-6 PM Travel Time Vehicle Delay in 2021 TTTD070821 – 7-8 AM Travel Time Volume Delay in 2021 TTTD080921 – 8-9 AM Travel Time Volume Delay in 2021 TTTD161721 – 4-5 PM Travel Time Volume Delay in 2021 TTTD171821 – 5-6 PM Travel Time Volume Delay in 2021 PTD070821 – 7-8 AM Planning Time Vehicle Delay in 2021 PTD080921 – 8-9 AM Planning Time Vehicle Delay in 2021 PTD161721 – 4-5 PM Planning Time Vehicle Delay in 2021 PTD171821 – 5-6 PM Planning Time Vehicle Delay in 2021 PTTD070821 – 7-8 AM Planning Time Volume Delay in 2021 PTTD080921 – 8-9 AM Planning Time Volume Delay in 2021 PTTD161721 – 4-5 PM Planning Time Volume Delay in 2021 PTTD171821 – 5-6 PM Planning Time Volume Delay in 2021 TTI060721 – 6-7 AM Travel Time Index in 2021 TTI070821 – 7-8 AM Travel Time Index in

Crash data reported to the Pennsylvania Department of Transportation. Includes data involving drivers, passengers, and motor vehicles for researching highway safety. This data can be used to investigate traffic crashes, fatalities and injuries statewide and in specific counties or municipalities. An incident that occurs on a highway or traffic way that is open to the public by right or custom and involved at least one motor vehicle in transport. An incident is reportable if it involves: Injury to or death of any person, or Damage to any vehicle to the extent that it cannot be driven under its own power in its customary manner without further damage or hazard to the vehicle, other traffic elements, or the roadway, and therefore requires towing. Crash data does not include non-reportable crashes or near misses Crash data may not contain complete information, some elements may be unknown

Data fields that may help with CODE4PA 2018 event Leveraging Data to help Fight the Opioid Epidemic DRUG_RELATED At least one Driver or Pedestrian with Drugs reported or suspected NUMBER This is a flag that defines whether the crash involved a driver or pedestrian was suspected of using drugs or was actually tested and had drugs in their system. If a driver or pedestrian is suspected and were tested, but the test results show no drugs, this situation would not be included.

DRUGGED_DRIVER At least one Driver with Drugs reported or suspected NUMBER This flag is similar to drug_related, but it only applies to drivers. It defines whether the crash involved a driver suspected of using drugs or was actually tested and had drugs in their system. If a driver is suspected and were tested, but the test results show no drugs, this situation would not be included.

ILLEGAL_DRUG_RELATED At Least 1 Driver or Pedestrian had reported or suspected Illegal Drug Use NUMBER This flag is similar to drug_related, but it only applies to illegal drugs. It defines whether the crash involved a driver or pedestrian suspected of using illegal drugs. If a driver is suspected and were tested, but the test results show no drugs, this situation would not be included.

IMPAIRED_DRIVER At least One Driver was Impaired by Drugs or Alcohol NUMBER This flag is similar to drug_related, but it includes both alcohol and drugs and it only applies to drivers. It defines whether the crash involved a driver suspected of using drugs or alcohol or was actually tested and had drugs or alcohol in their system. If a driver is suspected and were tested, but the test results show no drugs, this situation would not be included. * For additional information please review the Crash Data Information pdf attachment in the About This Dataset section of the Primer on this dataset.

State forest roads that are normally closed to vehicle traffic, which will be opened to hunters between the dates for the current year's hunting season. This dataset is updated annually, normally in September.

High Injury Network (HIN) Development MethodologyCity of Pittsburgh The purpose of developing a High Injury Network (HIN) is to identify and prioritize areas with high frequencies of traffic-related injuries and fatalities. The goal of developing the HIN is to increase road safety by focusing resources and improvements on high-risk areas. The development of a HIN is an important piece of Vision Zero. Vision Zero sets a clear goal of zero fatalities and severe injuries on roadways, while the HIN identifies and prioritizes areas where these incidents are most prevalent. This analysis uses the City’s Street Centerline GIS data and PennDOT five (5) year reported traffic crash data from 2018-2022. Other assumptions used in the development of the network are outlined in this document. The Network: There are 2,423 miles of roadways in the city of Pittsburgh. For this analysis: We focused on surface streets and excluded limited access facilities including interstates, ramps, facilities with no city-maintained components (Example-Rt 65) and tunnels. Most traffic incidents occur on surface streets in urban areas. These are also the streets that will have the highest pedestrian, bicycle and vehicle interactions, making them crucial areas for safety.We included all streets regardless of facility owner (i.e., city, state or county ownership). This is because even if the city does not have direct oversight of the roadway, it is important for us to know where crashes are occurring. Additionally, while the city may not own the roadway, in many cases the city own signal equipment, streetlights and/or have oversight over other aspects of the roadway that might be opportunities for safety intervention. Injury Crash Data and network development strategies We developed a methodology to translate injury crash data into insights on roadway safety. Crashes without a reported or suspected injury are not included in the analysis. We included all Fatal and Injury Crash (FIC) data including suspected serious injury, suspected minor injury, and possible injury to identify HIN corridors. Many traditional HIN’s factor only serious and fatal injuries. The reason we used ‘all-injury crashes’ was to eliminate the possibility of any discrepancies or subjectivity in police crash reports. This allowed us to have a broader sample size which help us identify injury crash hotspots proactively before they may result in greater injury or even a fatality crash without proper interventions. This strategy also ensures a holistic view of the road system and its vulnerability which is the core of the concept of “Safe System Approach”. The team conducted a GIS analysis to identify the high injury network. The injury and crash network reflect: Roadway segments with 5 or more injury crashes within 1500 ft, along with any segments lying between them, were combined to define a corridor. Individual roadway segments with 2 or more serious or fatal injury crashes within 200 ft to each other but the overall injury crash count of the segment is less than 5.We manually reviewed crashes occurring at or near intersections to ensure that crashes were appropriately assigned to the primary or secondary street. Factoring in Vulnerable Road Users A Vulnerable Road User (VRU) is defined as a non- motorist and someone who is walking, biking, rolling, or using a mobility device, such as a wheelchair (PennDOT, 2023). VRU’s are of critical concern and a priority for DOMI as they are at heightened risk of severe injury or fatality as a result of a vehicle crash. While VRU crashes are included in the development of the HIN, the team identified Top 10 segments with high pedestrian or bicycle crashes that didn’t meet the threshold for inclusion of the high injury network. Finally, this analysis included findings from the Pennsylvania VRU Safety Assessment Report. The HIN network included both the identified high-risk areas and systemic safety focused identified urban segments and intersections from the report. As expected, almost all of these data points were already identified from the previous phases of analysis. Data that were missing was incorporated into the HIN.