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Traffic Checker

## Overview

Traffic Checker is a dataset for object detection tasks - it contains Street Objects And Person annotations for 574 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Attributes of sites in Hamilton City which collect anonymised data from a sample of vehicles. Note: A Link is the section of the road between two sites

Column_InfoSite_Id, int : Unique identiferNumber, int : Asset number. Note: If the site is at a signalised intersection, Number will match 'Site_Number' in the table 'Traffic Signal Site Location'Is_Enabled, varchar : Site is currently enabledDisabled_Date, datetime : If currently disabled, the date at which the site was disabledSite_Name, varchar : Description of the site locationLatitude, numeric : North-south geographic coordinatesLongitude, numeric : East-west geographic coordinates

Relationship









Disclaimer

Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works.

Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data.

While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data:

‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.'

Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. For a more detailed description, please go to https://tas.chicago.gov, click the About button at the bottom of the page, and then the MAP LAYERS tab.

The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (nonfreeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every ten minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimate by traffic segments gives the observed speed typically for one-half mile of a street in one direction of traffic.

Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

This dataset contains the current estimated congestion for the 29 traffic regions. For a detailed description, please go to https://tas.chicago.gov, click the About button at the bottom of the page, and then the MAP LAYERS tab.

The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (non-freeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every 10 minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimates by traffic segments gives observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials. Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area).

There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for a relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. Speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes. The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

Traffic Counter

## Overview

Traffic Counter is a dataset for object detection tasks - it contains Cars annotations for 711 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains the historical estimated congestion for over 1,000 traffic segments, starting 6/11/2024 (except for a single time slice on 3/8/2024). Older records are in https://data.cityofchicago.org/d/sxs8-h27x. The most recent estimates for each segment are in https://data.cityofchicago.org/d/n4j6-wkkf.

The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (non-freeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses. Two types of congestion estimates are produced every 10 minutes: 1) by Traffic Segments and 2) by Traffic Regions or Zones. Congestion estimates by traffic segments gives observed speed typically for one-half mile of a street in one direction of traffic. Traffic Segment level congestion is available for about 300 miles of principal arterials.

Congestion by Traffic Region gives the average traffic condition for all arterial street segments within a region. A traffic region is comprised of two or three community areas with comparable traffic patterns. 29 regions are created to cover the entire city (except O’Hare airport area). There is much volatility in traffic segment speed. However, the congestion estimates for the traffic regions remain consistent for a relatively longer period. Most volatility in arterial speed comes from the very nature of the arterials themselves. Due to a myriad of factors, including but not limited to frequent intersections, traffic signals, transit movements, availability of alternative routes, crashes, short length of the segments, etc. Speed on individual arterial segments can fluctuate from heavily congested to no congestion and back in a few minutes.

The segment speed and traffic region congestion estimates together may give a better understanding of the actual traffic conditions.

Between July and September 2022, BYJU's emerged as the top Ed Tech platform for K12 and test preparation In India. It recorded approximately *** million website visits. Following closely behind was Toppr.com, with around *** million visits during the same period.

Traffic Light Test 3

## Overview

Traffic Light Test 3 is a dataset for object detection tasks - it contains Traffic Light annotations for 2,169 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This dataset contains the current estimated speed for about 1250 segments covering 300 miles of arterial roads. The Chicago Traffic Tracker estimates traffic congestion on Chicago’s arterial streets (nonfreeway streets) in real-time by continuously monitoring and analyzing GPS traces received from Chicago Transit Authority (CTA) buses.

The global traffic counter market is estimated to reach a value of XXXX million by 2033, exhibiting a CAGR of XX% during the forecast period (2025-2033). The market growth is primarily driven by the increasing demand for efficient traffic management systems, rising urbanization, and the need for data-driven traffic analysis. The adoption of advanced technologies, such as radar monitoring and video recognition, is further fueling the market expansion. The market is segmented based on application into road, parking lot, and others. The road segment holds the largest market share due to the extensive use of traffic counters to monitor traffic flow and congestion on highways and roads. The parking lot segment is also witnessing significant growth owing to the rising need for efficient parking management systems in commercial and residential areas. In terms of region, North America is expected to dominate the market, followed by Europe and Asia Pacific. Key drivers in the North American market include the presence of advanced traffic management systems and the growing adoption of smart city initiatives. Europe is also a significant market for traffic counters, with a high demand for traffic monitoring and analysis solutions in urban areas.

Here are a few use cases for this project:

1. Traffic Flow Analysis: This model could be used in smart cities to monitor and analyze traffic patterns across different times of the day, week or year. It can provide detailed insights into the types of vehicles and amount of pedestrians using specific roads or intersections, thereby helping in urban planning strategies.

2. Traffic Management Systems: The model could be incorporated into traffic management systems to dynamically control traffic lights depending on the type and volume of traffic. For instance, if a greater influx of cars and trucks is detected, traffic light timings could be adjusted to improve flow and decrease congestion.

3. Parking Lot Management: Retail centers, airports, or other facilities with large parking areas could use this technology to count the vehicles entering and exiting their premises, enabling efficient parking management and planning.

4. Transport Research: Research institutions could use the model to carry out comprehensive studies on transportation patterns, commuting trends, and the usage of different types of vehicles in different regions.

5. Safety Monitoring: The system could be used to detect anomalous events in traffic such as an increased number of pedestrians on the road or unusual vehicle patterns that could potentially lead to accidents. This could assist in devising safety measures and regulations.

A complete list of live websites using the Xt Visitor Counter technology, compiled through global website indexing conducted by WebTechSurvey.

The Visitor Management System (VMS) Software market is experiencing robust growth, driven by the increasing need for enhanced security, streamlined visitor processes, and improved workplace efficiency across diverse sectors. The market, estimated at $2 billion in 2025, is projected to experience a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated market value of $6 billion by 2033. This expansion is fueled by several key trends: the rising adoption of cloud-based VMS solutions offering scalability and cost-effectiveness; the integration of advanced technologies like facial recognition and mobile check-in for improved security and convenience; and the growing demand for robust reporting and analytics to track visitor data and optimize security protocols. Furthermore, the increasing awareness of regulatory compliance requirements related to visitor management further drives market adoption. While the initial investment in VMS software can represent a barrier to entry for some smaller businesses, the long-term benefits in terms of efficiency, security, and compliance are compelling drivers of market expansion. The competitive landscape is characterized by a mix of established players and emerging innovators. Key players like Envoy, Veristream, and SwipedOn are competing on the basis of feature sets, integration capabilities, and customer support. The market also shows signs of increasing consolidation, with larger players potentially acquiring smaller companies to expand their market share and product offerings. Geographic expansion, particularly in developing economies with rising security concerns, presents significant opportunities for growth. Restraints to market growth include the complexity of integrating VMS software with existing security systems in some organizations, and the need for ongoing training and support to maximize the effectiveness of the software. However, the overall market outlook remains positive, driven by the clear benefits VMS software provides in a world increasingly focused on security, efficiency, and data-driven decision-making.

Traffic analytics, rankings, and competitive metrics for tracker.gg as of May 2025

Test Traffic Sign

## Overview

Test Traffic Sign is a dataset for object detection tasks - it contains Stop And Right annotations for 401 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Traffic Light Counter Detection2

## Overview

Traffic Light Counter Detection2 is a dataset for object detection tasks - it contains Numbers annotations for 349 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

A complete list of live websites using the WP Live Visitor Counter technology, compiled through global website indexing conducted by WebTechSurvey.

This is 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 HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. Historical traffic is based on the average of observed speeds over the past three years. 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 image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer 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.