This map contains a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from TomTom (www.tomtom.com). Historical traffic is based on the average of observed speeds over the past year. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map can be requested for the current time and any time in the future. A map for a future request might be used for planning purposes. The map also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.

Historical data of traffic measurement points in the period of the COVID19 pandemic, NOTICE: This dataset is no longer updated. Data are offered from 30-03.2020 to 9-08-2020. There is another set of data in this portal with the historical series: Traffic. History of traffic data since 2013 In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Location of traffic measurement points. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right). In the section 'Associated documentation', there is an explanatory document with the structure of the files and recommendations on the use of the data.

Historical data of traffic measurement points. Each month the data of the previous month are incorporated. In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Location of traffic measurement points. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right). In the section 'Associated documentation', there is an explanatory document with the structure of the files and recommendations on the use of the data.

This data set features a hyperlink to the New York State Department of Transportation’s (NYSDOT) Traffic Data (TD) Viewer web page, which includes a link to the Traffic Data interactive map. The Traffic Data Viewer is a geospatially based Geographic Information System (GIS) application for displaying data contained in the roadway inventory database. The interactive map has five viewable data categories or ‘layers’. The five layers include: Average Daily Traffic (ADT); Continuous Counts; Short Counts; Bridges; and Grade Crossings throughout New York State.

This data set is related to Traffic. History of traffic data since 2013, indicating the latter for each measurement point, the passing vehicles. The infrastructure of measurement points, available in the city of Madrid corresponds to: 7,360 vehicle detectors with the following characteristics: 71 include number plate reading devices 158 have optical machine vision systems with control from the Mobility Management Center 1,245 are specific to fast roads and access to the city and the rest of the 5,886, with basic traffic light control systems. More than 4,000 measuring points : 253 with systems for speed control, characterization of vehicles and double reading loop 70 of them make up the stations of taking specific seats of the city. Automatic control systems of all the information obtained from the detectors with continuous contrast with expected behavior patterns, as well as the follow-up of the instructions marked by the Technical Committee for Standardization AEN/CTN 199; and in particular SC3 specific applications relating to “Detectors and data collection stations” and SC15 relating to “Data quality”. In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right).

This dataset is historical. For recent data, we recommend using https://chicagotraffictracker.com. -- Average Daily Traffic (ADT) counts are analogous to a census count of vehicles on city streets. These counts provide a close approximation to the actual number of vehicles passing through a given location on an average weekday. Since it is not possible to count every vehicle on every city street, sample counts are taken along larger streets to get an estimate of traffic on half-mile or one-mile street segments. ADT counts are used by city planners, transportation engineers, real-estate developers, marketers and many others for myriad planning and operational purposes. Data Owner: Transportation. Time Period: 2006. Frequency: A citywide count is taken approximately every 10 years. A limited number of traffic counts will be taken and added to the list periodically. Related Applications: Traffic Information Interactive Map (http://webapps.cityofchicago.org/traffic/).

A collection of historic traffic count data and guidelines for how to collect new data for Massachusetts Department of Transportation (MassDOT) projects.

This data set is related to Traffic. History of traffic data since 2013, indicating the latter for each measurement point, the passing vehicles. The infrastructure of measurement points, available in the city of Madrid corresponds to: 7,360 vehicle detectors with the following characteristics: 71 include number plate reading devices 158 have optical machine vision systems with control from the Mobility Management Center 1,245 are specific to fast roads and access to the city and the rest of the 5,886, with basic traffic light control systems. More than 4,000 measuring points : 253 with systems for speed control, characterization of vehicles and double reading loop 70 of them make up the stations of taking specific seats of the city. Automatic control systems of all the information obtained from the detectors with continuous contrast with expected behavior patterns, as well as the follow-up of the instructions marked by the Technical Committee for Standardization AEN/CTN 199; and in particular SC3 specific applications relating to “Detectors and data collection stations” and SC15 relating to “Data quality”. In this same portal you can find other related data sets such as: Traffic. Real-time traffic data . With real-time information (updated every 5 minutes) Traffic. Map of traffic intensity plots, with the same information in KML format, and with the possibility of viewing it in Google Maps or Google Earth. And other traffic-related data sets. You can search for them by putting the word 'Traffic' in the search engine (top right).

The FDOT Annual Average Daily Traffic feature class provides spatial information on Annual Average Daily Traffic section breaks for the state of Florida. In addition, it provides affiliated traffic information like KFCTR, DFCTR and TFCTR among others. This dataset is maintained by the Transportation Data & Analytics office (TDA). The source spatial data for this hosted feature layer was created on: 06/14/2025.Download Data: Enter Guest as Username to download the source shapefile from here: https://ftp.fdot.gov/file/d/FTP/FDOT/co/planning/transtat/gis/shapefiles/aadt.zip

FCD or Floating Car Data is the label for GPS positions that are received from so called FCD providers. The GPS data is mapped onto the digital map and provided as feed for various Be-Mobile applications. For instance, the data can be aggregated per map segment to calculate the occurrence of jams. A data hub with Floating Car Data from millions of connected vehicles lay the foundation for our solutions portfolio, including traffic management, traveler information, multimodal route planning, electronic toll collection and many more. The real-time traffic data we collect are stored for historical analysis purposes in an off-the-shelf software product: FlowCheck is the perfect instrument to detect cut-through traffic or assess the impact of certain mobility measures. FlowCheck provides tools for route analysis, segment analysis, origin-destination analysis and historical maps based on historical Floating Car Data. For more information about this data, please reach out to our mobility experts via https://be-mobile.com/contact

An accident near an elementary school in your city has drawn your attention to the topic of pedestrian and bicycle safety. You want to suggest policy actions to your city's local government that will reduce the likelihood of future accidents.

In this lesson, you'll map accident data regarding pedestrians and cyclists struck by vehicles. Then, you'll determine the number of accidents that occurred within each school zone and identify the five most dangerous zones. You'll present your findings with a story map that provides narrative context and helps users understand your position. This lesson is targeted toward city or county employees or any civic-minded individual who wants to make a difference in their community.

In this lesson you will build skills in the these areas:

• Adding data from the Living Atlas- Symbolizing data with Arcade expressions- Configuring pop-ups- Summarizing data within an area- Sharing maps in a Story Map

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

The Alberta Highways 1 to 986 Traffic Volume History and the Permanent Automated Traffic Recorder (ATR) Sites AADT Map are prepared by CornerStone Solutions Inc. for Alberta Transportation. The reports and map present average daily two-way traffic volumes on Alberta's Highways. The location of traffic count sites on Alberta's Highways are identified by its highway, control section, traffic control section and municipality.

The dataset is outcome of a paper "Floating Car Data Map-matching Utilizing the Dijkstra Algorithm" accepted for 3rd International Conference on Data Management, Analytics & Innovation held in Kuala Lumpur, Malaysia in 2019.

The floating car data (FCD representing movement of cars with their position in time) is produced by the traffic simulator software (further referred to as Simulator) published in [1] and can be used as an input for data processing and benchmarking. The dataset contains FCD of various quality levels based on the routing graph of the Czech Republic derived from Open Street Map openstreetmap.org.

Should the dataset be exploited in scientific or other way, any acknowledgement or references to our paper [1] and dataset are welcomed and highly appreciated.

Archive contents

The archive contains following folders.

city_oneway and city_roadtrip - FCD from the city of Brno, Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip

intercity_oneway and intercity_roadtrip - FCD from cities of Brno, Ostrava, Olomouc and Zlin, all Czech Republic where FCD is based on Origin-Destination in case of oneway and Origin-Destination-Origin in case of a road trip

Content explanation

All four of mentioned folders contain raw FCD as they come from our Simulator, post-processed FCD enriching Simulator FCD, and obfuscated raw FCD (of both low and high obfuscation level). In the both obfuscated data sets, each measured point was moved in a random direction a number of meters given by drawing a number from a Gaussian distribution. We utilized two Gaussian distributions, one for the roads outside the city (N(0,10) for the lower and N(0,20) for the higher obfuscation level) and one for the roads inside the city (N(0,15) and N(0,30) respectively). Then some predefined number of randomly chosen points were removed (3% in our case). This approach should roughly represent real conditions encountered by FCD data as described by El Abbous and Samanta [2].

In case of post-processed road trip data, there is one extra dataset with "cache" suffix representing the very same dataset limited to a 5-minute session memoization. This folder also contains a picture of processed FCD represented on a map.

Data format Standard UTF-8 encoded CSV files, separated by a semicolon with the following columns:

RAW

Header

session_id;timestamp;lat;lon;speed;bearing;segment_id

Data

session_id: (Type: unsigned INT) - session (car) identifier timestamp: (Type: datetime) - timestamp in UTC lat: (Type: unsigned long) - latitude as used in Google maps lon: (Type: unsigned long) - longitude as used in Google maps speed: (Type: unsigned INT) - actual speed in kmh bearing: (Type: unsigned INT) - actual bearing in angles 0-360 segment_id: (Type: unsigned long) - unique edge identifier

POST-PROCESSED

Header

gid;car_id;point_time;lat;lon;segment_id;speed_kmh;speed_avg_kmh;distance_delta_m;distance_total_m;speedup_ratio;duration;segment_changed;duration_segment;moved;duration_move;good;duration_good;bearing;interpolated

Data

gid: (Type: unsigned long) - global identifier of a record car_id: (Type: unsigned INT) - session (car) identifier point_time: (Type: datetime) - timestamp with timezone lat: (Type: unsigned long) - latitude as used in Google maps lon: (Type: unsigned long) - longitude as used in Google maps segment_id: (Type: unsigned long) - unique edge identifier speed: (Type: unsigned INT) - actual speed in kmh speed_avg_kmh: (Type: unsigned long) - actual average speed of a car in kmh distance_delta_m: (Type: unsigned long) - actual distance delta in metres distance_total_m: (Type: unsigned long) - actual total distance of a car in metres speedup_ratio: (Type: unsigned long) - actual speed-up ratio of a car duration: (Type: time) - actual duration of a car segment_changed: (Type: boolean) - signals if actual segment of a car differs from the previous one duration_segment: (Type: time) - actual duration on a segment of a car moved: (Type: boolean) - signals if actual position of a car differs from the previous one duration_move:(Type: time) - actual duration of a car since moving good: signals if actual record values satisfies all data constraints (all true as derived from Simulator) duration_good: actual duration of a car since when all constraints conditions satisfied bearing: (Type: unsigned INT) - actual bearing in angles 0-360 interpolated: (Type: boolean) - signals if actual segment identifier is calculated (all false as derived from Simulator)

References

[1] V. Ptošek, J. Ševčík, J. Martinovič, K. Slaninová, L. Rapant, and R. Cmar, Real-time traffic simulator for self-adaptive navigation system validation, Proceedings of EMSS-HMS: Modeling & Simulation in Logistics, Traffic & Transportation, 2018.

[2] A. El Abbous and N. Samanta. A modeling of GPS error distri-butions, In proceedings of 2017 European Navigation Conference (ENC), 2017.

This map shows volumes of commercial truck traffic that originate and arrive (destination) by block group (2020 Census)Limitations of Replica vehicle volume data: The map shows quarterly daily average vehicle volume at road link level provided by Replica, Inc. It includes estimates of volumes of passenger vehicles, buses, on-demand vehicles, and commercial trucks. SACOG staff only validated passenger vehicle volume and bus at road links with observed data. Due to lack of observed truck volume, the estimates of truck volume used in this map were not validated. Users should be aware of the limitations of truck volume in applications.

This dataset shows the position of vehicular crash incidents in Queensland. This includes fatalities, injuries and vehicular damage. The point of truth for this dataset is the Queensland government open data website at, https://data.qld.gov.au/dataset/crash-data-from-queensland-roads

Este Story Map Journal ofrece la dimensión geográfica de la Crisis Fiscal, con un énfasis en las quiebras Capítulo 11 en Puerto Rico desde 2008 hasta el presente. De igual modo, hacemos una comparación entre el Capítulo 11 y el Capítulo 7 de Guaynabo, segundo municipio con más quiebras. Finalmente, se muestra cómo se ha manifestado la Crisis Fiscal en el paisaje urbano de la Avenida F.D. Roosevelt a través de un inventario de sus negocios.Capas de informaciónGeographic Mapping Technologies, Corp. Población e ingreso 2010.Documentos en PDF US Bankruptcy Court District of Puerto Rico. (2016) Casos ante la Ley de Quiebras Capítulo 7 y Capítulo 11 01/01/2008-09/26/2016. Documento PDF, p.1-306. ImágenesBarreiro Morales, C. (2016) Universidad, la estación de tren [Fotografía]. ---. (2016) Vista a la costa sur [Fotografía]. ---. (2016) Vista a la sierra desde la carretera [Fotografía]. Barreiro Morales, C. & Rivera Vilches, A. (2016) Negocios en desuso en la Avenida FD Roosevelt [Fotografía]. Rivera Vilches, A. (2016) Los Tres Picachos, Barrio Coabey, Jayuya [Fotografía]. National Aeronautics and Space Administration. (2016). Puerto Rico Goes Dark [Fotografía]. Retirado de http://earthobservatory.nasa.gov/IOTD/view.php?id=88796&linkId=29141575.

Mapas webUFT. Fotos aéreas 1998 del CRIM [ArcGIS Online Web Map]. Retirado de https://www.satasgis.crimpr.net/crimgis/rest/services/Mapas/Imagen_1998/MapServer.

Páginas de la web

https://data.pr.gov/en/Transportaci-n/Annual-Average-Daily-Traffic-AADT-Transito-Promedi/7kaq-zyym/data

http://www.prb.uscourts.gov/

Reportajes de periódico

Díaz Román, M. (2005, 1 de marzo). En la quiebra la mitad de los municipios. El Nuevo Día, p.6.

García Pelatti, L. (2009, 31 de marzo). Se disparan las quiebras. El Vocero, p.37.

Marrero, R. (2009, 9 de noviembre). Alarmante alza en quiebras boricuas. Primera Hora, p.4.

VideosBarreiro Morales, C. & Rivera Vilches, A. (2016) Time Lapse del tráfico en la Avenida FD Roosevelt. Portafolio Global CNN: La Crisis de Puerto Rico según Alejandro Garcia Padilla, Pt. 1. Retirado de: https://www.youtube.com/watch?v=ROZpPNoi0EY

Autoras

Cecilia Barreiro Morales

Andrea S. Rivera Vilches