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/).

This GIS dataset contains traffic survey findings within the City of Casey. Information collected includes survey point locations, traffic volume counts, 85th percentile average speeds measurements, commercial vehicle proportions, and peak period usage levels.

Yunlin County real-time traffic information network - Roadside parking lot data inquiry

The City of Seattle Transportation GIS Datasets | https://data-seattlecitygis.opendata.arcgis.com/datasets?t=transportation | Lifecycle status: Production | Purpose: to enable open access to SDOT GIS data. This website includes over 60 transportation-related GIS datasets from categories such as parking, transit, pedestrian, bicycle, and roadway assets. | PDDL: https://opendatacommons.org/licenses/pddl/

| The City of Seattle makes no representation or warranty as to its accuracy. The City of Seattle has created this service for our GIS Open Data website. We do reserve the right to alter, suspend, re-host, or retire this service at any time and without notice.

| Datasets: 2007 Traffic Flow Counts, 2008 Traffic Flow Counts, 2009 Traffic Flow Counts, 2010 Traffic Flow Counts, 2011 Traffic Flow Counts, 2012 Traffic Flow Counts, 2013 Traffic Flow Counts, 2014 Traffic Flow Counts, 2015 Traffic Flow Counts, 2016 Traffic Flow Counts, 2017 Traffic Flow Counts, 2018 Traffic Flow Counts, Areaways, Bike Racks, Blockface, Bridges, Channelization File Geodatabase, Collisions, Crash Cushions, Curb Ramps, dotMaps Active Projects, Dynamic Message Signs, Existing Bike Facilities, Freight Network, Greater Downtown Alleys, Guardrails, High Impact Areas, Intersections, Marked Crosswalks, One-Way Streets, Paid Area Curbspaces, Pavement Moratoriums, Pay Stations, Peak Hour Parking Restrictions, Planned Bike Facilities, Public Garages or Parking Lots, Radar Speed Signs, Restricted Parking Zone (RPZ) Program, Retaining Walls, SDOT Capital Projects Input, Seattle On Street Paid Parking-Daytime Rates, Seattle On Street Paid Parking-Evening Rates, Seattle On Street Paid Parking-Morning Rates, Seattle Streets, SidewalkObservations, Sidewalks, Snow Ice Routes, Stairways, Street Design Concept Plans, Street Ends (Shoreline), Street Furnishings, Street Signs, Street Use Permits Use Addresses, Streetcar Lines, Streetcar Stations, Traffic Beacons, Traffic Cameras, Traffic Circles, Traffic Detectors, Traffic Lanes, Traffic Signals, Transit Classification, Trees.

Here are a few use cases for this project:

1. Smart City Traffic Regulation: This model could be used within traffic regulation systems of smart cities. It could provide regular updates on vehicle count to traffic control authorities, thereby helping to manage traffic and reduce congestion.

2. Intelligent Parking Solutions: The model could be used in parking lots to keep track of available spaces in real time. By detecting and counting cars, it can provide users with information on available parking spots, reducing chaos and saving time.

3. Commercial Shopping Centers: Shopping centers could use this model to manage and optimize their parking areas, ensuring a better shopping experience for customers by reducing the time taken to find parking.

4. Urban Planning Research: The model can be used by urban planners and researchers to understand patterns in vehicle movement and parking habits. The insights generated can be used in optimizing city infrastructure and planning for future developments.

5. Security and Surveillance: This model can be implemented in security systems to monitor car inflow and outflow from a location, providing useful information to enhance security measures.

Road edges are defined as the edge of the improved surface including the improved shoulder but do not include the unimproved shoulder, only the travel part of the road. The road network is compiled to include all open intersections. Features do not overlap sidewalks, but have the sidewalk area cut out of the road polygons. Overlapping features are acceptable if one of the features is hidden. Road: A generally named thoroughfare, that is usually paved and can be public or private. Unimproved thoroughfares are excluded. Road polygons are formed by a combination of road edge, curb, sidewalk, street intersection closure line, and map sheet edge. Paved Median Island: Perimeter of non-traffic paved areas that separate traffic lanes in opposing directions. Unpaved Median Island: Perimeter of non-traffic grassy, unpaved areas that separate traffic lanes in opposing directions. Paved Traffic Island: Perimeter of non-traffic concrete areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Unpaved Traffic Island: Perimeter of non-traffic unpaved, grassy areas in the middle of streets designed to segregate traffic flow. This does not include linear barriers, e.g., Jersey barriers, walls or guardrails, or point barriers, such as impact attenuators. Features do not overlap sidewalks. Alley: Perimeter of alleys first plotted photogrammetrically from other indicators such as building footprints, fence lines, curb lines, walls, paved or unpaved drives, and map sheet edge. Alley polygons are closed along the lines where they intersect with road polygons. Paved Drive: A paved driveway for a building or entranceway for a parking lot. Driveways are neither streets nor alleys, but provide access to public facilities, such as a drive to a monument, museum, hotel, large estate, sports field or golf course, grounds of the U.S. Capitol, etc. If a driveway is less than 200 feet and leads to a parking lot, the entire paved area is captured as Parking Lot. Driveways are photogrammetrically compiled as polygons and not compiled from individual vectors on different levels. Parking Lot: Generally paved surfaces used for cars to park on. Paved drives usually form entrances to these features, if the drive is more than 200 feet. If the driveway is less than 200 feet leading into the parking lot, the entire paved area is captured as Parking Lot. Parking lots sharing a common boundary with linear features must have the common segment captured once, but coded as both polygon and line. Small parking areas, where individuals park their cars in the middle of a block off a public alley, are not captured as parking lots. These are either public space (e.g., alleys) or private space where owners permit parking to occur. Intersection: A location where more than one road comes together. For standard cross streets, intersection polygons are bounded by curbs and four closure lines at street intersection crosswalks (outer line) or placed arbitrarily where crosswalks could logically be placed. For "T" intersections, the polygons are bounded by curbs and three such closure lines. Complex intersections can have more closure lines. Entire traffic circles are coded as intersections. Hidden Road: A section of a road that passes underneath a bridge or overpass and is not visible in an aerial photograph, but the location can be interpreted based on the road on either side of the bridge. Hidden Median: A road median that exists underneath a bridge or overpass and is not fully visible in an aerial photograph, but the location can be interpreted based on the information visible on either side of the bridge.

Here are a few use cases for this project:

1. Traffic Management Systems: Use the "Vehicle detection" model to regulate traffic flow, identify traffic violations, and recognize different types of vehicles on the road, which could lead to more efficient transit systems and improved road safety.

2. Surveillance and Security: Deploy the model in surveillance systems to detect and track specific types of vehicles, particularly useful for identifying unauthorized or suspicious vehicles in certain areas.

3. Autonomous Vehicles: Implement the model in self-driving cars to enable them to identify and differentiate between various vehicle types for safer navigation and traffic maneuvering.

4. Parking Facility Management: Use the model in parking lots and garages to ascertain the types of parked vehicles, automate the parking process, and efficiently allocate spaces based on vehicle classes.

5. Road Maintenance and Planning: Apply the model for traffic data collection followed by analysis on traffic patterns and transportation planning. It can help identify the predominant type of vehicles on particular routes, assisting in better road maintenance and future infrastructure planning.

In 2024, motorcycles contributed the highest average daily traffic volume in Metro Manila in comparison to other types of vehicles. In that year, the traffic volume of motorcycles reached around 1.9 million. This was followed by the volume of cars on the road.

Freight Traffic: International: To India: Foreign Operators: Lot Polish data was reported at 92.634 Tonne in Dec 2024. This records a decrease from the previous number of 103.140 Tonne for Sep 2024. Freight Traffic: International: To India: Foreign Operators: Lot Polish data is updated quarterly, averaging 302.720 Tonne from Mar 2021 (Median) to Dec 2024, with 16 observations. The data reached an all-time high of 607.627 Tonne in Jun 2021 and a record low of 92.634 Tonne in Dec 2024. Freight Traffic: International: To India: Foreign Operators: Lot Polish data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA045: Aviation Statistics: Freight Traffic: International: by Airline: To India.

Hong Kong has a lot of real-time data which are made available by the Government of Hong Kong Special Administrative Region at https://DATA.GOV.HK/ (“DATA.GOV.HK”). These data were processed and converted to Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform.These series of Operations Dashboard integrate different available real-time datasets in Hong Kong to provide a dashboard interface for monitoring real-time data feed on your desktop or tablet device. The objectives are to facilitate our Hong Kong ArcGIS Online users to view these data in a spatial ready format and save their data conversion effort.These series of Operations Dashboard come in three themes, environmental, traffic and integrated.The Environmental theme contains real-time temperature, air quality health risk and air pollution concentration of different districts in Hong Kong. To view it, please click here.Traffic theme contains real-time information of estimated journey time, car park vacancy, traffic speed of major roads, traffic snapshot images and speed map panels in Hong Kong.The integrated theme combines the above two sets of data, which are environmental and traffic, and makes them into one single dashboard view. To view it, please click here.

The global vehicle presence sensor market is experiencing robust growth, driven by increasing urbanization, advancements in intelligent transportation systems (ITS), and the rising demand for improved traffic management and parking solutions. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market value of $7.8 billion by 2033. This significant growth is fueled by several key factors. The proliferation of smart cities initiatives worldwide is a major catalyst, as these projects heavily rely on advanced sensor technologies for efficient traffic flow optimization and improved public safety. Furthermore, the increasing adoption of autonomous vehicles and connected car technologies necessitates reliable and accurate vehicle presence detection, further bolstering market demand. The segment encompassing wireless sensor technology is expected to dominate the market due to its flexibility, ease of installation, and cost-effectiveness compared to wired counterparts. Applications like traffic vehicle presence detection and counting, parking lot management, and intersection adaptive traffic light control are key growth drivers within the market. However, several challenges persist. High initial investment costs associated with installing and maintaining sensor networks can be a barrier to entry, particularly for smaller municipalities and private parking operators. Concerns regarding data security and privacy related to the collection and analysis of vehicle presence data also pose a potential restraint on market growth. Despite these challenges, the long-term outlook for the vehicle presence sensor market remains exceptionally positive, driven by ongoing technological advancements, supportive government policies, and the imperative for efficient urban transportation solutions. The continuous development of more robust, reliable, and cost-effective sensor technologies will be key to unlocking the full potential of this rapidly expanding market. This report provides a detailed analysis of the global vehicle presence sensor market, projecting significant growth in the coming years. Driven by increasing urbanization, smart city initiatives, and the demand for enhanced traffic management systems, the market is poised for substantial expansion. This in-depth study explores market segmentation, key players, technological advancements, and future growth prospects. The report utilizes robust data analysis and industry expertise to offer valuable insights for stakeholders across the automotive, transportation, and technology sectors.

Freight Traffic: International: From India: Foreign Operators: Lot Polish data was reported at 794.153 Tonne in Sep 2024. This records an increase from the previous number of 759.768 Tonne for Jun 2024. Freight Traffic: International: From India: Foreign Operators: Lot Polish data is updated quarterly, averaging 866.903 Tonne from Mar 2021 (Median) to Sep 2024, with 15 observations. The data reached an all-time high of 1,285.857 Tonne in Dec 2021 and a record low of 636.475 Tonne in Mar 2021. Freight Traffic: International: From India: Foreign Operators: Lot Polish data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA046: Aviation Statistics: Freight Traffic: International: by Airline: From India.

ICBC data as of July 31, 2014. Accurate and verifiable information is not always available. Therefore, data only include crashes where sufficient location information was available to determine a latitude and longitude. When comparing map counts with previous publications, counts may differ due to rounding, late reporting or corrections to the data. Crash maps exclude crashes in parking lots and incidents involving parked vehicles.

The Electronic Toll Collection (ETC) Equipment market is experiencing robust growth, driven by increasing adoption of cashless payment systems, government initiatives promoting smart transportation, and the rising demand for efficient traffic management solutions. The market size in 2025 is estimated at $15 billion, exhibiting a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033. This growth is fueled by several factors. Firstly, the expansion of highway networks globally necessitates advanced toll collection systems capable of handling high traffic volumes. Secondly, the increasing popularity of connected vehicles and the Internet of Things (IoT) is integrating ETC systems with advanced data analytics and traffic optimization capabilities. Finally, the continuous technological advancements in ETC technology, such as the development of more reliable and secure systems using 5G and other technologies, are driving market expansion. The market segmentation reveals strong growth across both On-Board Units (OBUs) and Roadside Units (RSUs), with highway applications holding the largest market share currently. However, the market faces challenges. High initial investment costs for infrastructure deployment can be a barrier to entry for some regions. Furthermore, concerns about data security and privacy surrounding the collection and use of toll data need to be addressed to build public trust and ensure widespread adoption. Despite these constraints, the long-term outlook for the ETC equipment market remains positive, with significant growth projected across all segments and geographic regions. The Asia-Pacific region is anticipated to show particularly strong growth, driven by large-scale infrastructure projects and increasing urbanization in countries like China and India. The competitive landscape is dynamic, with established players focusing on technological innovation and strategic partnerships to maintain market share and expand their global reach.

This dataset is designed for the detection of persons and cars in surveillance camera footage. It can be utilized for various useful applications, including:

• Security Systems: Enhancing security measures by accurately detecting and tracking persons and cars in real-time surveillance videos.
• Traffic Monitoring: Analyzing traffic patterns, estimating congestion levels, and optimizing traffic flow by detecting and counting cars on roads or at intersections.
• Safety Enhancement: Identifying potential hazards and ensuring public safety by detecting unauthorized access or suspicious activities involving persons and cars.
• Crowd Management: Monitoring crowded areas and public events to ensure safety, identify crowd density, and estimate crowd movement by detecting and tracking persons.
• Parking Systems: Optimizing parking lot management by detecting available parking spots and monitoring the entry and exit of vehicles.
• Smart Cities: Contributing to the development of smart city infrastructure by integrating the detection of persons and cars into intelligent systems for efficient urban planning and management.

This dataset is based on images collected from various sources, including:

https://universe.roboflow.com/radoslaw-kawczak/virat-ve02s

https://universe.roboflow.com/seminar-object-detection/cars-o1ljf

With this dataset, you can train and develop machine learning models capable of accurately detecting persons and cars, thus empowering surveillance and security systems with advanced object recognition capabilities.

Passenger Traffic: International: From India: Foreign Operators: Lot Polish data was reported at 14,055.000 Person in Sep 2024. This records a decrease from the previous number of 14,824.000 Person for Jun 2024. Passenger Traffic: International: From India: Foreign Operators: Lot Polish data is updated quarterly, averaging 14,568.000 Person from Jun 2022 (Median) to Sep 2024, with 10 observations. The data reached an all-time high of 16,288.000 Person in Mar 2023 and a record low of 13,026.000 Person in Dec 2023. Passenger Traffic: International: From India: Foreign Operators: Lot Polish data remains active status in CEIC and is reported by Directorate General of Civil Aviation. The data is categorized under India Premium Database’s Transportation, Post and Telecom Sector – Table IN.TA037: Aviation Statistics: Passenger Traffic: International: by Airline: From India.

Here are a few use cases for this project:

1. Smart Parking Systems: Utilize the "VEHICLE CLASSIFICATION" model to categorize different types of vehicles entering a parking lot, enabling efficient space allocation and optimized parking management for various vehicle classes.

2. Traffic Control and Analysis: Implement the model in traffic monitoring systems to identify and count various vehicle classes on the road, aiding in traffic pattern analysis and congestion prediction, allowing better city planning and traffic management.

3. Automotive Market Research: Analyze social media images and online listings using the model to study consumer preferences and trends in vehicle ownership based on vehicle classes, providing insightful data for automotive manufacturers and dealers.

4. Advanced Driver Assistance Systems (ADAS): Integrate the "VEHICLE CLASSIFICATION" model into automotive safety features to recognize and differentiate between vehicle classes on the road, enhancing the performance of features like adaptive cruise control and collision avoidance systems.

5. Vehicle Insurance Assessment: Employ the model to assist insurance companies in assessing the risk and potential cost of claims based on the identified vehicle classes, allowing for more accurate underwriting and pricing for clients.

This layer contains point features of all Commercial parking lots in the City of Cambridge. These include private and municipal lots and garages and their addresses. Created for general use by City staff. Used for both mapping and specific identification of parking lot locations.

For more information and download links see: http://www.cambridgema.gov/gis/gisdatadictionary/traffic/traffic_commercialparking.aspx

City of Pittsburgh Street Centerline Data DictionaryEditor Tracking (created_date, created_user, last_edited_date, last_edited_user)Automatically recorded information indicating the creation date and user and the last modified date and user.From/To Street Name (fromstreet, tostreet)Where the street segment ends or begins. The starting point of the line segment is the From Street and the end point of the line segment is the To Street. The names of the cross streets are used where applicable. If the segment does not begin or end at a cross street, CITY LIMIT and DEAD END can both be used. The name of the street itself can also be used in situations where the beginning or end of a segment is in between cross streets.Functional Class (domi_class)A general classification system used by the Department of Mobility and Infrastructure to stratify roads by significance.• Principal Arterial - Roadways with high traffic volumes such as interstate highways, freeways, and expressways; frequently the route of choice for intercity buses and trucks.• Minor Arterial – Roadways that serve trips of moderate length and smaller geographic areas than principal arterials.• Collector – Roadways that “collect” traffic from Local Roads and connect traffic to Arterial roadways. Typically, shorter than Arterial Routes but longer than Local Roads. Collectors provide traffic circulation within residential neighborhoods as well as commercial, industrial, or civic districts.• Local – Roadways that provide direct access to adjacent land, not intended for use in long distance travel. Locals roads provide access to higher systems, and typically don’t carry through traffic movement.• Alley - Streets intended to provide access to the rear or side of lots or buildings in urban areas and not intended for the purpose of vehicular through traffic.• Park Road – Roadways that provide access into and through parks.• Private Road – Privately owned and maintained roads. Private roads are often open to the public in spaces such as shopping malls, airports, and sports arenas. Public access can be restricted to private gated properties.• UnknownLength (measurlgth)The length of the segment measured in feet, projected using the NAD 1983 State Plane Pennsylvania South FIPS 3702 (US Feet) projection.Most Recent Paving Date (replaced)The date a segment was most recently resurfaced, repaved, or otherwise restored.Number of Travel Lanes (num_lanes)Number of travel lanes in any direction of travel on an undivided road. Divided roadways using more than one segment note number of lanes on the given segment only.One Way Designation (oneway)The direction of allowed traffic flow along a route• N: Travel allowed in both directions.• FT: One way travel allowed in the direction the line segment is drawn, from the beginning of the line (from) to the end of the line (to).• TF: One way travel allowed in the opposite direction the line segment is drawn, from the end of the line (to) to the beginning of the line (from).Owner (owner)The owner of a road, usually but not necessarily always responsible for maintenance activities and project initiation.• CITY• STATE• PRIVATE• MT OLIVER• COUNTY• PORT AUTHORITY• OTHER MUNICIPALITY• UnknownRoad Status (class)The status of the road, typically indicating either the maintenance responsibility or if and why a segment cannot traversed by vehicle.Right/Left Council District (council_rt, council_lt)The Council District identifier to the right and left of a segment, when facing the direction the line segment has been drawn.Right/Left Fire Zone (fire_zn_rt, fire_zn_lt)The Bureau of Fire Zone identifier to the right and left of a segment, when facing the direction the line segment has been drawn.Right/Left Neighborhood (hood_right, hood_left)The Neighborhood identifier to the right and left of a segment, when facing the direction the line segment has been drawn.Right/Left Police Zone (zone_right, zone_left)The Bureau of Police identifier to the right and left of a segment, when facing the direction the line segment has been drawn.Right/Left Street Maintenance Division (dpw_zon_rt, dpw_zon_lt)The Department of Public Works Street Maintenance Division identifier to the right and left of a segment, when facing the direction the line segment has been drawn.Right/Left Street Sweeping (sweep_right, sweep_left)The Street Sweeping Route identifier to the right and left of a segment, when facing the direction the line segment has been drawn. The identifier consists of 3 parts: the Public Works Division, the route number, and the date of street sweeping (e.g. ‘5SW8-2W’ is done by the 5th Division, on route number 8, and completed on the 2nd Wednesday of each month).Right/Left Voting District (vote_dt_rt, vote_dt_lt)The Voting District identifier to the right and left of a segment, when facing the direction the line segment has been drawn.Right/Left Ward (ward_rg, ward_lt)The Ward identifier to the right and left of a segment, when facing the direction the line segment has been drawn.Right/Left Zip Code (zipr, zipl)The Zip Code identifier to the right and left of a segment, when facing the direction the line segment has been drawn.Road Removal Date (retired)The date indicating when a segment was removed from the street centerline dataset, usually indicating the road was removed due to new development or other changes in the immediate area.Road Surface Width (roadwidth)Surface width in feet. The measurement of the full traveled way and shoulders/auxiliary lanes.Segment ID (carteid)A unique identifier assigned to each street segment in the City of Pittsburgh.Speed Limit (speedlimit)The speed limit for traffic travelling on both sides of the road.Street Name (streetname, prefix, name, type, suffix, dir)The streetname field contains the name of a road (a combination of prefix, name, type, suffix, dir. Names in this field are written in all capital letters and their abbreviated type. (E.g. MAIN ST, E QUINN RD). The prefix field contains any prefix before the proper street name, usually an abbreviated cardinal direction (N, S, E, W). The name field contains the spelled out street name. The type field contains the type of street (e.g. road, avenue, way). The suffix field contains any suffix after the proper street name, usually an abbreviated cardinal direction (N, S, E, W). The dir field contains the direction of travel in relation to another segment, typically a tunnel, bridge, or highway.Surface Material Type (paveclass)The material used to build the street surface• Asphalt• Concrete• Brick• Blockstone• Unsurfaced• Metal Deck• Wood• Unknown

VILD is a basic file with detailed location data on the Dutch A and N roads. The location description follows a CEN standard and contains line, point and area data essential for providing traffic information. Recognition points for road users are therefore included in the basic file, such as bridge names, parking spaces, petrol stations, exit numbers, connection points and hectometre marking. All these points have a unique TMC coding for RDS/TMC reports. This is the current point file version VILD6.7.A_V2.0, which contains the following attributes: Connection, Exit, Aqueduct, Built-up area, Bridge, Border crossing, Port, Hectometre jump, Industrial area, Junction, Junction (triangle), Intersection, PenR terrain, Parking lot (rest), Parking lot (service), Lock, Railway crossing, Petrol station, Toll, Tunnel, Ferry, Ferry terminal, Connecting road, Traffic square.