Geospatial data about Prince William County, Virginia Traffic Analysis Zones. Export to CAD, GIS, PDF, CSV and access via API.

PDF of the PFAM 2024 Final Report.

Major Highways: Major highways used to analyze transportation needs and alternatives for the Lake Tahoe Region
County Roads: County roads used to analyze transportation needs and alternatives for the Lake Tahoe Region
Streets: Geocoded streets (The best available streets layer for the Lake Tahoe region)
Existing Bike Trails: Bikeway class designation.
Proposed Bike Trails: 2017 Proposed active transportation network with class, name, segment length, and constrained/unconstrained plan type
Bike Trails 75ft buffer: 2017 - 75 foot buffer around existing active transportation network segments
Traffic Analysis Zones (TAZ): The purpose for these boundaries is to provide zones to analyze transportation needs and alternatives for the Lake Tahoe Region to meet the Tahoe Regional Planning Agency thresholds, as well as conform to local, state, and federal regulations.
Bike and Ped Counter: Bicycle and pedestrian counts obtained from TRPA's Trafx/EcoVision automated counters as of November 2017
Vehicle Collisions and Injuries on Highways: Vehicle collisions and injuries on highways reported by NDOT and CalTrans. Retrieved October 2017.
Traffic volumes reported by CalTrans and NDOT: Traffic volumes reported by CalTrans and NDOT. Measured by average annual daily traffic (AADT). Data retrieved October 2017.

| A. PURPOSE | This dataset is created to show the estimated yearly pedestrian volume at each intersection. | B. METHODOLOGY | http://archives.sfmta.com/cms/rpedmast/documents/FinalPedestrianCountReport6_17_11.pdf | C. UPDATE FREQUENCY | Not updated | D. OTHER CRITICAL INFO | Volume estimates made with 2011 transportation data and 2000 US Census data | E. ATTRIBUTES | CNN: San Francisco's street centerline network unique ID; ST_NAME1: Name of cross street;ST_TYPE1: Type of street;ST_NAME2: Name of cross street;ST_TYPE2: Type of street;ST_NAME3: Name of cross street;ST_TYPE3: Type of street;ST_NAME4: Name of cross street;ST_TYPE4: Type of street;TOTEMP2: Total number of jobs within 0.25 miles of the intersection in 2010. Data calculated by SFCTA from SFCTA traffic analysis zones. These data are produced by the SF Planning Department by allocating ABAG county‐level land use figures to the SFCTA's 981 transportation analysis zones within San Francisco;UNIVPROX: Intersection is located within 0.25 miles of one the five major university campuses in the city: USF Lone Mountain, UCSF Parnassus, UCSF Mission Bay, City College Ingleside, SFSU Park Merced. Other schools are not included, since they are either smaller, more spread out, or different in character (e.g., serve adult/commuter students at night). (1 = yes, 0 = no);Signalized: Intersection is controlled by a traffic signal. (1 = yes, 0 = no); PkgMeters: Intersection is in a zone with parking meters (e.g., parking meters are present on at least one approach to the intersection). (1 = yes, 0 =no); MaxPctSlpe: Maximum slope of any approach to the intersection. (Percent slope); Model6_Vol: Annual pedestrian volume;HH_PedMode: Unknown;PCol_04: Unknown;PCol_Rate: Unknown

Major Highways: Major highways used to analyze transportation needs and alternatives for the Lake Tahoe RegionCounty Roads: County roads used to analyze transportation needs and alternatives for the Lake Tahoe RegionStreets: Geocoded streets (The best available streets layer for the Lake Tahoe region)Existing Bike Trails: Bikeway class designation. Proposed Bike Trails: 2017 Proposed active transportation network with class, name, segment length, and constrained/unconstrained plan typeBike Trails 75ft buffer: 2017 - 75 foot buffer around existing active transportation network segmentsTraffic Analysis Zones (TAZ): The purpose for these boundaries is to provide zones to analyze transportation needs and alternatives for the Lake Tahoe Region to meet the Tahoe Regional Planning Agency thresholds, as well as conform to local, state, and federal regulations. Bike and Ped Counter: Bicycle and pedestrian counts obtained from TRPA's Trafx/EcoVision automated counters as of November 2017Vehicle Collisions and Injuries on Highways: Vehicle collisions and injuries on highways reported by NDOT and CalTrans. Retrieved October 2017. Traffic volumes reported by CalTrans and NDOT: Traffic volumes reported by CalTrans and NDOT. Measured by average annual daily traffic (AADT). Data retrieved October 2017. Limebike trip: Limebike trips and users from the Summer/Fall 2017 South Shore Limebike pilot project2020 Regional Transportation Plan Projects: https://tahoempo.org/ActiveTransportationPlan/docs/appendices/Appendix%20H_Project%20Lists.pdf

Resources used within TPB's models development programScreenlines layer: Screenlines and cutlines are imaginary lines that pass through a series of roads. They are generally used to sum up traffic volumes, in order to compare observed traffic volumes (traffic counts) with estimated traffic volumes from the regional model. In general, screenlines are longer, generally capturing cross-regional traffic flows, and cutlines are shorter, generally capturing traffic flows through a major corridor.[1] The term “screenline” is often used as a general term to refer to both screenlines and cutlines.Regional Core layer: Historically, the area covered by the TPB travel demand model, has been divided into a series of transportation analysis zones (TAZs) and concentric rings, numbered from 0 to 8. Ring 0 (actually more of a trapezoid), represents the core of downtown DC. Ring 1 surrounds Ring 0 and includes more of downtown DC and the section of Arlington, Co. that is closest to the District. Similarly, Ring 4 was the Capital Beltway.This ring system is used less and less in the TPB travel model, but the area defined by Rings 0 and 1 is still used as a definition of the core of the urban area and is also used in a modeling procedure known as the Metrorail constraint through the regional core (sometimes referred to as the “transit constraint through the regional core”). This modeling constraint is a technical adjustment to the trip tables coming out of the mode choice process designed to reflect a WMATA policy assumption that, during peak periods, the Metrorail system may have insufficient capacity to handle all the demand traveling to and through the regional core. The Metrorail constraint was initiated by WMATA in 2000 to address funding shortfalls restricting the expansion of the rail fleet.[2] WMATA policy sets the binding year, which is currently set at 2020. More information can be found in the current TPB travel demand model user’s guide.[3]External Stations layer: External stations are imaginary points that represent locations where traffic enters and exits the modeled area. The current transportation analysis zones (TAZ) system has 3,722 TAZs, of which the last 47 (TAZs 3676-3722) are the external stations. Although TAZs are generally polygons, each TAZ has a centroid (point) that represents the TAZ. In the case of external stations, there is no polygon, only the TAZ centroid. Thus, each external station is simply a point lying on the external cordon of the modeled area.[4]_[1] Cambridge Systematics, Inc., Travel Model Validation and Reasonability Checking Manual, Second Edition (Washington, D.C.: Travel Model Improvement Program, Federal Highway Administration, September 24, 2010), 9-12-13, http://media.tmiponline.org/clearinghouse/FHWA-HEP-10-042/FHWA-HEP-10-042.pdf[2] Ronald Milone, “TPB Version 2.3 Travel Model on the 3,722-TAZ area system: Status report” (presented at the September 23, 2011 meeting of the Travel Forecasting Subcommittee of the Technical Committee of the National Capital Region Transportation Planning Board, held at the Metropolitan Washington Council of Governments, Washington, D.C., September 23, 2011).[3] Ronald Milone, Mark Moran, and Meseret Seifu, User’s Guide for the COG/TPB Travel Demand Forecasting Model, Version 2.3.57a: Volume 1 of 2: Main Report and Appendix A (Flowcharts) (Washington, D.C.: Metropolitan Washington Council of Governments, National Capital Region Transportation Planning Board, October 29, 2015), 161–62, https://www.mwcog.org/transportation/data-and-tools/modeling/model-documentation/.[4] Ronald Milone et al., Highway and Transit Networks for the Version 2.3.57a Travel Model, Based on the 2015 CLRP and FY 2015-2020 TIP, Final Report (Washington, D.C.: Metropolitan Washington Council of Governments, National Capital Region Transportation Planning Board, March 18, 2016), 2-3-7, https://www.mwcog.org/transportation/data-and-tools/modeling/model-documentation/.For more information, please visit TPB's Transportation and Models & Forecasts page.

Major Highways: Major highways used to analyze transportation needs and alternatives for the Lake Tahoe Region
County Roads: County roads used to analyze transportation needs and alternatives for the Lake Tahoe Region
Streets: Geocoded streets (The best available streets layer for the Lake Tahoe region)
Existing Bike Trails: Bikeway class designation.
Proposed Bike Trails: 2017 Proposed active transportation network with class, name, segment length, and constrained/unconstrained plan type
Bike Trails 75ft buffer: 2017 - 75 foot buffer around existing active transportation network segments
Traffic Analysis Zones (TAZ): The purpose for these boundaries is to provide zones to analyze transportation needs and alternatives for the Lake Tahoe Region to meet the Tahoe Regional Planning Agency thresholds, as well as conform to local, state, and federal regulations.
Bike and Ped Counter: Bicycle and pedestrian counts obtained from TRPA's Trafx/EcoVision automated counters as of November 2017
Vehicle Collisions and Injuries on Highways: Vehicle collisions and injuries on highways reported by NDOT and CalTrans. Retrieved October 2017.
Traffic volumes reported by CalTrans and NDOT: Traffic volumes reported by CalTrans and NDOT. Measured by average annual daily traffic (AADT). Data retrieved October 2017.

PDF of the PFAM 2024 Final Report.

Major Highways: Major highways used to analyze transportation needs and alternatives for the Lake Tahoe Region
County Roads: County roads used to analyze transportation needs and alternatives for the Lake Tahoe Region
Streets: Geocoded streets (The best available streets layer for the Lake Tahoe region)
Existing Bike Trails: Bikeway class designation.
Proposed Bike Trails: 2017 Proposed active transportation network with class, name, segment length, and constrained/unconstrained plan type
Bike Trails 75ft buffer: 2017 - 75 foot buffer around existing active transportation network segments
Traffic Analysis Zones (TAZ): The purpose for these boundaries is to provide zones to analyze transportation needs and alternatives for the Lake Tahoe Region to meet the Tahoe Regional Planning Agency thresholds, as well as conform to local, state, and federal regulations.
Bike and Ped Counter: Bicycle and pedestrian counts obtained from TRPA's Trafx/EcoVision automated counters as of November 2017
Vehicle Collisions and Injuries on Highways: Vehicle collisions and injuries on highways reported by NDOT and CalTrans. Retrieved October 2017.
Traffic volumes reported by CalTrans and NDOT: Traffic volumes reported by CalTrans and NDOT. Measured by average annual daily traffic (AADT). Data retrieved October 2017.

Major Highways: Major highways used to analyze transportation needs and alternatives for the Lake Tahoe Region
County Roads: County roads used to analyze transportation needs and alternatives for the Lake Tahoe Region
Streets: Geocoded streets (The best available streets layer for the Lake Tahoe region)
Existing Bike Trails: Bikeway class designation.
Proposed Bike Trails: 2017 Proposed active transportation network with class, name, segment length, and constrained/unconstrained plan type
Bike Trails 75ft buffer: 2017 - 75 foot buffer around existing active transportation network segments
Traffic Analysis Zones (TAZ): The purpose for these boundaries is to provide zones to analyze transportation needs and alternatives for the Lake Tahoe Region to meet the Tahoe Regional Planning Agency thresholds, as well as conform to local, state, and federal regulations.
Bike and Ped Counter: Bicycle and pedestrian counts obtained from TRPA's Trafx/EcoVision automated counters as of November 2017
Vehicle Collisions and Injuries on Highways: Vehicle collisions and injuries on highways reported by NDOT and CalTrans. Retrieved October 2017.
Traffic volumes reported by CalTrans and NDOT: Traffic volumes reported by CalTrans and NDOT. Measured by average annual daily traffic (AADT). Data retrieved October 2017.

BackgroundTraffic accidents on the road is an accident is a terrible accident that causes death, injury, and property damage. However, limited studies were addressed to investigate the prevalence of traffic accidents on the road and the contributing factors among drivers that help in developing strategies to cop-up the incidence within the research domain in Ethiopia, particularly in the study area.ObjectiveThis study aimed to assess the prevalence of road traffic accidents and the contributing factors among drivers of public transportation in Mizan Aman town, Ethiopia.MethodsA community-based cross-sectional survey was employed among 376 drivers of public transportation. Every research subject was selected by using a simple random sampling technique. Semi-structured and open-ended questionnaires which comprised demographic characteristics, risky personal behaviors and lifestyles, driver’s factors, vehicle condition, and environmental conditions were used to gather data. And then after, data was collected through interviewer-administered using KoBo Collect tools. Completed data were edited and cleaned in the Kobo collect toolbox and then exported for additional analysis to a statistical tool for social science statistics version 26. The descriptive statistics were displayed as figures, tables, and texts. Binary logistic regression was analyzed to identify the contributing factors. Statistically significant was decided with a p-value of ≤ 0.05.ResultsThe results showed that the prevalence of road traffic accidents among drivers of public transportation in Mizan Aman town was 17%. The study identified factors influencing traffic accidents on the roads including marital status (being single), employee condition (permanent), monthly income (1001-2500 Ethiopia Birr), alcohol use, vehicle maintenance (not), road type (non-asphalt), and weather conditions (being windy).ConclusionThe overall prevalence of road traffic accidents among drivers of public transportation in Mizan Aman town was relatively low. Despite this, sociodemographic characteristics, driver factors, vehicle conditions, and environmental conditions [road type and weather conditions] were the predicting factors of traffic accidents in town. Therefore, reduction strategies should be the highest priority duty for concerned bodies like Mizan Aman town road and transport office, Bench Sheko zone transport and logistics office, and Southwest Ethiopia People Regional State (SWEPRS) transport bureau in the study area.

link to online pdf resource. Link Function: 375-- download.

VITAL SIGNS INDICATOR
Fatalities From Crashes (EN4)

FULL MEASURE NAME
Fatalities from Crashes (traffic collisions)

LAST UPDATED
October 2022

DESCRIPTION
Fatalities from crashes refers to deaths as a result of fatalities sustained in collisions. The California Highway Patrol includes deaths within 30 days of the collision that are a result of fatalities sustained as part of this metric. This total fatalities dataset includes fatality counts for the region and counties, as well as individual collision data and metropolitan area data.

DATA SOURCE
National Highway Safety Administration: Fatality Analysis Reporting System - https://www.nhtsa.gov/file-downloads?p=nhtsa/downloads/FARS/
1990-2020

Caltrans: Highway Performance Monitoring System (HPMS) - https://dot.ca.gov/programs/research-innovation-system-information/highway-performance-monitoring-system
Annual Vehicle Miles Traveled (VMT)
2001-2020

California Department of Finance: E-4 Historical Population Estimates for Cities, Counties, and the State - https://dof.ca.gov/forecasting/demographics/estimates/
1990-2020

US Census Population and Housing Unit Estimates - https://www.census.gov/programs-surveys/popest.html
1990-2020

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Fatalities from crashes data is reported to the National Highway Traffic Safety Administration through the Fatality Analysis Reporting System (FARS) program. Data for individual collisions is reported by the California Highway Patrol (CHP) to the Statewide Integrated Traffic Records System (SWITRS). The data was tabulated using provided categories specifying injury level, individuals involved, causes of collision and location/jurisdiction of collision (for more information refer to the SWITRS codebook - http://tims.berkeley.edu/help/files/switrs_codebook.doc). For case data, latitude and longitude information for each accident is geocoded by SafeTREC’s Transportation Injury Mapping System (TIMS). Fatalities were normalized over historic population data from the US Census Bureau’s population estimates and vehicle miles traveled (VMT) data from the Federal Highway Administration.

The crash data only include crashes that involved a motor vehicle. Bicyclist and pedestrian fatalities that did not involve a motor vehicle, such as a bicyclist and pedestrian collision or a bicycle crash due to a pothole, are not included in the data.

For more regarding reporting procedures and injury classification, refer to the CHP Manual - https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ca_chp555_manual_2_2003_ch1-13.pdf.

Snapshot img

Fleet Management Market Size 2025-2029

The fleet management market size is forecast to increase by USD 52.23 billion, at a CAGR of 15.6% between 2024 and 2029. Several key factors are fueling growth, including the growth in e-commerce and the need for efficient last-mile delivery, increased emphasis on asset tracking, and the rising demand for advanced logistics solutions such as smart fleet management and cold chain transportation. This growth is driven by the expansion of e-commerce, heightened focus on asset tracking, and the increasing need for efficient logistics solutions. Fleet operators have access to vast amounts of data regarding their daily fleet operations, including fuel expenses, routes taken, real-time traffic updates, telematics data, and work order performance. Regulatory frameworks and funding programs are also promoting the adoption of efficient fleet management solutions, including AI-powered fleet management software. These solutions offer numerous benefits, including improved operational efficiency, reduced costs, and enhanced regulatory compliance. Fleet operators can leverage this data to optimize their operations, reduce downtime, and improve overall fleet performance.

What will be the size of the market during the forecast period?

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Fleet Management Market Segmentation

The market research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2025-2029, as well as historical data from 2019 - 2023 for the following segments.

Type

  Subscription
  Others


Vehicle Type

  Commercial fleet
  Passenger car


Communication Technology

  GNSS
  Cellular System


Geography

  North America

    Canada
    US


  Europe

    Germany
    UK
    France
    Italy


  APAC

    China
    India
    Japan
    South Korea


  South America



  Middle East and Africa

Which is the largest segment driving market growth?

The subscription segment is estimated to witness significant growth during the forecast period. Subscription models enable businesses to adapt their management services to their present requirements, making it more convenient to modify their plans as their fleet sizes fluctuate. This adaptability allows companies to pay only for the services and features they need, providing them with budget predictability and streamlined financial planning.

Get a glance at the market share of various regions. Download the PDF Sample

The subscription segment was valued at USD 31.92 billion in 2019. Subscription-based offerings enable clients to effectively manage, optimize, and safeguard their investments in their commercial and personal vehicle fleets. This demand is driven by the benefits of subscription services, leading to market growth during the forecast period. Their clientele spans from small fleet operators and individual consumers to large enterprises managing over 10,000 assets. Consequently, the subscription segment is projected to expand, contributing significantly to the market's growth.

Which region is leading the market?

For more insights on the market share of various regions, Request Free Sample

North America is estimated to contribute 30% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period. The economic prosperity and expansion in North America fuel commercial activities, leading to a heightened demand for effective management solutions. A flourishing economy generally results in an increase in businesses requiring transportation and logistics services. The rise of e-commerce and the escalating demand for last-mile delivery contribute to the expansion of the market. Companies in North America are investing in advanced management systems to cater to the burgeoning online retail sector and enhance delivery efficiency.

Moreover, urbanization and the subsequent traffic congestion in metropolitan areas necessitate efficient management. Solutions that streamline routes, minimize idling time, and navigate through congested urban areas are increasingly indispensable. Additionally, the market is poised for significant growth. Government initiatives encouraging the adoption of sophisticated transportation technologies and funding programs to facilitate the implementation of intelligent transportation systems (ITSs) foster market growth in Europe. Furthermore, the integration and development of autonomous vehicles play a pivotal role in market expansion. Companies pursuing autonomous fleets seek sophisticated management solutions to optimize and monitor these vehicles. Consequently, these factors will propel the market forward during the forecast period.

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NOTE: This is an updated dataset and supersedes the Forecast 2050 data released in November of 2024. This dataset includes 2019 estimates and 2035 and 2050 projections of households, population, and employment for traffic analysis zones (TAZ). For more information, see NCTCOG 2050 Forecast Methodology.pdf and Data Dictionary 2050 Forecast (TAZ).pdf

Major Highways: Major highways used to analyze transportation needs and alternatives for the Lake Tahoe RegionCounty Roads: County roads used to analyze transportation needs and alternatives for the Lake Tahoe RegionStreets: Geocoded streets (The best available streets layer for the Lake Tahoe region)Existing Bike Trails: Bikeway class designation. Proposed Bike Trails: 2017 Proposed active transportation network with class, name, segment length, and constrained/unconstrained plan typeBike Trails 75ft buffer: 2017 - 75 foot buffer around existing active transportation network segmentsTraffic Analysis Zones (TAZ): The purpose for these boundaries is to provide zones to analyze transportation needs and alternatives for the Lake Tahoe Region to meet the Tahoe Regional Planning Agency thresholds, as well as conform to local, state, and federal regulations. Bike and Ped Counter: Bicycle and pedestrian counts obtained from TRPA's Trafx/EcoVision automated counters as of November 2017Vehicle Collisions and Injuries on Highways: Vehicle collisions and injuries on highways reported by NDOT and CalTrans. Retrieved October 2017. Traffic volumes reported by CalTrans and NDOT: Traffic volumes reported by CalTrans and NDOT. Measured by average annual daily traffic (AADT). Data retrieved October 2017. 2020 Regional Transportation Plan Projects: https://tahoempo.org/ActiveTransportationPlan/docs/appendices/Appendix%20H_Project%20Lists.pdf