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.

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

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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.'

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).

Click Web Traffic Combined with Transaction Data: A New Dimension of Shopper Insights

Consumer Edge is a leader in alternative consumer data for public and private investors and corporate clients. Click enhances the unparalleled accuracy of CE Transact by allowing investors to delve deeper and browse further into global online web traffic for CE Transact companies and more. Leverage the unique fusion of web traffic and transaction datasets to understand the addressable market and understand spending behavior on consumer and B2B websites. See the impact of changes in marketing spend, search engine algorithms, and social media awareness on visits to a merchant’s website, and discover the extent to which product mix and pricing drive or hinder visits and dwell time. Plus, Click uncovers a more global view of traffic trends in geographies not covered by Transact. Doubleclick into better forecasting, with Click.

Consumer Edge’s Click is available in machine-readable file delivery and enables: • Comprehensive Global Coverage: Insights across 620+ brands and 59 countries, including key markets in the US, Europe, Asia, and Latin America. • Integrated Data Ecosystem: Click seamlessly maps web traffic data to CE entities and stock tickers, enabling a unified view across various business intelligence tools. • Near Real-Time Insights: Daily data delivery with a 5-day lag ensures timely, actionable insights for agile decision-making. • Enhanced Forecasting Capabilities: Combining web traffic indicators with transaction data helps identify patterns and predict revenue performance.

Use Case: Analyze Year Over Year Growth Rate by Region

Problem A public investor wants to understand how a company’s year-over-year growth differs by region.

Solution The firm leveraged Consumer Edge Click data to: • Gain visibility into key metrics like views, bounce rate, visits, and addressable spend • Analyze year-over-year growth rates for a time period • Breakout data by geographic region to see growth trends

Metrics Include: • Spend • Items • Volume • Transactions • Price Per Volume

Inquire about a Click subscription to perform more complex, near real-time analyses on public tickers and private brands as well as for industries beyond CPG like: • Monitor web traffic as a leading indicator of stock performance and consumer demand • Analyze customer interest and sentiment at the brand and sub-brand levels

Consumer Edge offers a variety of datasets covering the US, Europe (UK, Austria, France, Germany, Italy, Spain), and across the globe, with subscription options serving a wide range of business needs.

Consumer Edge is the Leader in Data-Driven Insights Focused on the Global Consumer

Mobile accounts for approximately half of web traffic worldwide. In the last quarter of 2024, mobile devices (excluding tablets) generated 62.54 percent of global website traffic. Mobiles and smartphones consistently hoovered around the 50 percent mark since the beginning of 2017, before surpassing it in 2020. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Network Traffic Analysis Solutions Market Outlook

The global network traffic analysis solutions market size was estimated at USD 3.5 billion in 2023 and is projected to reach USD 9.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 12.1%. This substantial growth is largely driven by the increasing demand for robust cybersecurity measures across various sectors. With an ever-growing volume of network traffic due to the proliferation of connected devices and the adoption of digital transformation initiatives, organizations are compelled to deploy sophisticated traffic analysis tools to effectively monitor, manage, and secure their networks. The expansion of cloud services, coupled with the rise in cyber threats, further accentuates the need for advanced traffic analysis capabilities.

The surge in cyber threats, including sophisticated hacking techniques and ransomware attacks, has become a pivotal growth factor for the network traffic analysis solutions market. As organizations strive to protect sensitive data and ensure the integrity of their networks, there is a heightened demand for solutions that can provide real-time visibility and control over network traffic. This growing emphasis on cybersecurity is not limited to large enterprises but is increasingly becoming a priority for small and medium enterprises (SMEs) as well. Consequently, the increasing cyber threat landscape is stimulating the adoption of network traffic analysis solutions across different organizational sizes, driving market growth.

Moreover, the rise of Internet of Things (IoT) devices is significantly contributing to the increased need for network traffic analysis. IoT devices generate vast amounts of data that need to be managed effectively to prevent network congestion and potential security breaches. By leveraging traffic analysis solutions, organizations can optimize IoT device performance and ensure seamless data flow while maintaining robust security protocols. As the IoT ecosystem continues to expand, it is expected to further fuel the demand for network traffic analysis solutions, facilitating better management and security of network resources.

In addition to cybersecurity concerns and IoT proliferation, regulatory compliance is another critical growth driver for the network traffic analysis solutions market. Organizations across various industries, such as BFSI, healthcare, and government sectors, are under increasing pressure to comply with stringent data protection regulations. Network traffic analysis solutions help these organizations monitor compliance effectively by providing detailed insights into network activity and data flows. As regulations continue to evolve and become more complex, the role of network traffic analysis solutions in ensuring compliance and mitigating risks is expected to become increasingly important, further bolstering market growth.

Network Telemetry Solutions are becoming increasingly essential in the realm of network traffic analysis. These solutions provide real-time data collection and analysis, enabling organizations to gain deeper insights into their network operations. By leveraging network telemetry, businesses can proactively identify and address potential issues before they escalate into significant problems. This capability is particularly valuable in today's fast-paced digital environment, where network performance and security are critical to maintaining operational efficiency. As the demand for more granular visibility into network activities grows, network telemetry solutions are poised to play a pivotal role in enhancing the capabilities of traffic analysis tools, offering a more comprehensive approach to network management and security.

From a regional perspective, North America is anticipated to maintain a dominant position in the network traffic analysis solutions market. This can be attributed to the presence of major technology companies, a high adoption rate of advanced technologies, and stringent cybersecurity regulations. The region's established digital infrastructure and focus on innovation also contribute to market growth. Meanwhile, the Asia Pacific region is projected to witness the highest growth rate due to rapid digitalization, increasing internet penetration, and growing investments in IT infrastructure. As businesses in this region continue to adopt digital technologies and face rising cyber threats, the demand for network traffic analysis solutions is expected to surge significantly.

Component Analysis</h2

In 2023, most of the global website traffic was still generated by humans but bot traffic is constantly growing. Fraudulent traffic through bad bot actors accounted for 32 percent of global web traffic in the most recently measured period, representing an increase of 1.8 percent from the previous year. Sophistication of Bad Bots on the rise The complexity of malicious bot activity has dramatically increased in recent years. Advanced bad bots have doubled in prevalence over the past two years, indicating a surge in the sophistication of cyber threats. Simultaneously, simple bad bots saw a 6 percent increase compared to the previous year, suggesting a shift in the landscape of automated threats. Meanwhile, areas like entertainment, and law & government face the highest amount of advanced bad bots, with more than 78 percent of their bot traffic affected by evasive applications. Good and bad bots across industries The impact of bot traffic varies across different sectors. Bad bots accounted for over 57.2 percent of the gaming segment's web traffic. Meanwhile, almost half of the online traffic for telecom and ISPs was moved by malicious applications. However, not all bot traffic is considered bad. Some of these applications help index websites for search engines or monitor website performance, assisting users throughout their online search. Therefore, areas like entertainment, food and groceries, and financial services experienced notable levels of good bot traffic, demonstrating the diverse applications of benign automated systems across different sectors.

The website analytics market, encompassing solutions like product, traffic, and sales analytics, is a dynamic and rapidly growing sector. While precise market sizing data wasn't provided, considering the presence of major players like Google, SEMrush, and SimilarWeb, along with numerous smaller competitors catering to SMEs and large enterprises, we can reasonably estimate a 2025 market value of $15 billion, projecting a Compound Annual Growth Rate (CAGR) of 15% from 2025-2033. This growth is fueled by the increasing reliance of businesses on data-driven decision-making, the expanding adoption of digital marketing strategies, and the rising need for precise performance measurement across all digital channels. Key trends driving this expansion include the integration of AI and machine learning for enhanced predictive analytics, the rise of serverless architectures for cost-effective scalability, and the growing demand for comprehensive dashboards providing unified insights across different marketing channels. However, challenges remain, including data privacy concerns, the complexity of integrating various analytics tools, and the need for businesses to cultivate internal expertise to effectively utilize the data generated. The competitive landscape is highly fragmented, with established giants like Google Analytics competing alongside specialized providers like SEMrush (focused on SEO and PPC analytics), SimilarWeb (website traffic analysis), and BuiltWith (technology identification). Smaller companies, such as Owletter and SpyFu, carve out niches by focusing on specific areas or offering specialized features. This dynamic competition necessitates continuous innovation and adaptation. Companies must differentiate themselves through specialized features, ease of use, and strong customer support. The market's geographic distribution is likely skewed towards North America and Europe initially, mirroring the higher digital maturity in these regions; however, rapid growth is anticipated in Asia-Pacific regions driven by increasing internet penetration and adoption of digital technologies within emerging economies like India and China. Successful players will need to develop strategies to effectively capture this expanding global market, adapting offerings to suit diverse regional needs and regulatory environments.

Network Traffic Analysis Market is Segmented by Deployment (On-Premise, Cloud-Based, and Hybrid), Component (Solutions and Services), Organization Size (Large Enterprises and Small and Medium Enterprises), End-User Industry (BFSI, IT and Telecom, and More), and Geography. The Market Sizes and Forecasts are Provided in Value (in USD Million) for all the Above Segments.

Traffic Simulation Systems Market Outlook

According to our latest research, the global Traffic Simulation Systems market size reached USD 2.34 billion in 2024 and is anticipated to grow at a robust CAGR of 13.2% during the forecast period from 2025 to 2033. By 2033, the market is projected to attain a value of USD 6.82 billion. This impressive growth trajectory is primarily driven by increasing urbanization, the proliferation of smart city initiatives, and the urgent need for efficient traffic management solutions worldwide. The adoption of advanced simulation technologies by government agencies and private enterprises is further accelerating market expansion as they seek to optimize transportation networks and enhance urban mobility.

The growth of the Traffic Simulation Systems market is strongly influenced by the rapid urbanization seen across both developed and emerging economies. As urban populations swell, the demand for efficient traffic management becomes increasingly urgent, compelling city planners and transportation authorities to invest in sophisticated simulation systems. These solutions allow for the modeling of complex traffic scenarios, enabling authorities to predict congestion points, plan infrastructure upgrades, and test policy interventions in a virtual environment before implementation. The rise of smart city programs, particularly in regions such as Asia Pacific and North America, has led to a surge in the deployment of traffic simulation tools, as cities strive to improve road safety, reduce emissions, and enhance commuter experiences.

Another significant growth factor is the integration of cutting-edge technologies such as artificial intelligence, machine learning, and big data analytics into traffic simulation systems. These advancements have revolutionized the capabilities of simulation software, allowing for more accurate and dynamic modeling of real-world traffic conditions. AI-driven simulations can process vast amounts of real-time data from sensors, cameras, and connected vehicles, providing actionable insights for traffic management and infrastructure planning. Additionally, the increasing adoption of cloud-based simulation platforms has made these solutions more accessible and scalable, enabling organizations with varying budgets and technical expertise to leverage their benefits. The shift towards cloud deployment has also facilitated remote collaboration among stakeholders, further driving market growth.

The rising emphasis on sustainability and environmental concerns is also propelling the Traffic Simulation Systems market. Governments and organizations worldwide are under pressure to reduce traffic congestion, lower carbon emissions, and promote the use of public transportation. Traffic simulation systems play a crucial role in achieving these objectives by enabling the evaluation of different traffic management strategies, assessing the impact of new infrastructure projects, and supporting the development of eco-friendly transportation policies. The ability to simulate emergency scenarios, such as natural disasters or large public events, is another key driver, as it helps authorities prepare effective response plans and minimize disruptions.

From a regional perspective, Asia Pacific is emerging as the fastest-growing market for traffic simulation systems, driven by rapid urban development, significant investments in smart infrastructure, and supportive government policies. North America remains a mature market with high adoption rates, fueled by technological innovation and a strong focus on traffic safety and efficiency. Europe is also witnessing steady growth, supported by stringent environmental regulations and robust public transportation networks. Meanwhile, Latin America and the Middle East & Africa are gradually catching up, with increasing investments in urban mobility solutions and digital transformation initiatives. The global market landscape is thus characterized by diverse growth patterns, with each region presenting unique opportunities and challenges for stakeholders.

This traffic dataset contains a balance size of encrypted malicious and legitimate traffic for encrypted malicious traffic detection and analysis. The dataset is a secondary csv feature data that is composed of six public traffic datasets.

Our dataset is curated based on two criteria: The first criterion is to combine widely considered public datasets which contain enough encrypted malicious or encrypted legitimate traffic in existing works, such as Malware Capture Facility Project datasets. The second criterion is to ensure the final dataset balance of encrypted malicious and legitimate network traffic.

Based on the criteria, 6 public datasets are selected. After data pre-processing, details of each selected public dataset and the size of different encrypted traffic are shown in the “Dataset Statistic Analysis Document”. The document summarized the malicious and legitimate traffic size we selected from each selected public dataset, the traffic size of each malicious traffic type, and the total traffic size of the composed dataset. From the table, we are able to observe that encrypted malicious and legitimate traffic equally contributes to approximately 50% of the final composed dataset.

The datasets now made available were prepared to aim at encrypted malicious traffic detection. Since the dataset is used for machine learning or deep learning model training, a sample of train and test sets are also provided. The train and test datasets are separated based on 1:4. Such datasets can be used for machine learning or deep learning model training and testing based on selected features or after processing further data pre-processing.

Real Time Traffic Data Market Outlook

The global real-time traffic data market size is anticipated to reach USD 15.3 billion by 2032 from an estimated USD 6.5 billion in 2023, exhibiting a robust CAGR of 10.1% over the forecast period. This substantial growth is driven by the increasing need for efficient traffic management systems and the rising adoption of smart city initiatives worldwide. Governments and commercial entities are investing heavily in advanced technologies to optimize traffic flow and enhance urban mobility, thus fostering market expansion.

The surge in urbanization and the consequent rise in vehicle ownership have led to severe traffic congestion issues in many metropolitan areas. This has necessitated the implementation of real-time traffic data systems that can provide accurate and timely information to manage traffic effectively. With the integration of sophisticated technologies such as IoT, AI, and big data analytics, these systems are becoming more efficient, thereby driving market growth. Furthermore, the growing emphasis on reducing carbon emissions and enhancing road safety is also propelling the adoption of real-time traffic data solutions.

Technological advancements are playing a pivotal role in shaping the real-time traffic data market. Innovations in sensor technology, the proliferation of GPS devices, and the widespread use of mobile data are providing rich sources of real-time traffic information. The ability to integrate data from multiple sources and deliver actionable insights is significantly enhancing traffic management capabilities. Additionally, the development of cloud-based solutions is enabling scalable and cost-effective deployment of traffic data systems, further contributing to market growth.

Another critical growth factor is the increasing investment in smart city projects. Governments across the globe are prioritizing the development of smart transportation infrastructure to improve urban mobility and reduce traffic-related issues. Real-time traffic data systems are integral to these initiatives, providing essential data for optimizing traffic flow, enabling route optimization, and enhancing public transport efficiency. The involvement of private sector players in these projects is also fueling market growth by introducing innovative solutions and fostering public-private partnerships.

The exponential rise in Mobile Data Traffic is another significant factor influencing the real-time traffic data market. As more people rely on smartphones and mobile applications for navigation and traffic updates, the demand for real-time data has surged. Mobile data provides a wealth of information about traffic patterns and congestion levels, enabling more accurate and timely traffic management. The integration of mobile data with other data sources, such as GPS and sensor data, enhances the overall effectiveness of traffic data systems. This trend is particularly evident in urban areas where mobile devices are ubiquitous, and the need for efficient traffic management is critical. The ability to harness mobile data for traffic insights is driving innovation and growth in the market, as companies develop new solutions to leverage this valuable resource.

Regionally, North America and Europe are leading the market due to their early adoption of advanced traffic management technologies and significant investments in smart city projects. However, the Asia Pacific region is expected to witness the highest growth rate over the forecast period, driven by rapid urbanization, increasing vehicle ownership, and growing government initiatives to develop smart transportation infrastructure. Emerging economies in Latin America and the Middle East & Africa are also showing promising growth potential, fueled by ongoing infrastructure development and increasing awareness of the benefits of real-time traffic data solutions.

Component Analysis

The real-time traffic data market by component is segmented into software, hardware, and services. Each component plays a crucial role in the overall functionality and effectiveness of traffic data systems. The software segment includes traffic management software, route optimization software, and other analytical tools that help process and analyze traffic data. The hardware segment comprises sensors, GPS devices, and other data collection tools. The services segment includes installation, maintenance, and consulting services that support the deployment and operation of traffic data systems

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 set of data houses a test set of 1000 graphs with locally skewed traffic at a rate of gamma=1.0. The throughput labels are calculated with the same methodology as the other beta sets just subjected to different traffic conditions.

This dataset contains traffic incident information from the Austin-Travis County traffic reports collected from the various Public Safety agencies through a data feed from the Combined Transportation, Emergency, and Communications Center (CTECC). For further context, see: - Active Incidents: Map and Context - https://data.austintexas.gov/stories/s/Austin-Travis-County-Traffic-Report-Page/9qfg-4swh/ - Data Trends and Analysis - https://data.austintexas.gov/stories/s/48n7-m3me The dataset is updated every 5 minutes with the latest snapshot of active traffic incidents.

The Intelligent Traffic Management System (ITMS) market is experiencing robust growth, projected to reach a market size of $8492.3 million in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 6.8%. This expansion is driven by several key factors. Increasing urbanization and traffic congestion in major cities globally necessitate efficient traffic management solutions. Government initiatives focusing on smart city development and improved transportation infrastructure are significantly boosting ITMS adoption. Furthermore, advancements in technologies such as Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT) are enabling more sophisticated and data-driven traffic management capabilities, leading to enhanced safety, reduced congestion, and optimized resource allocation. The integration of ITMS with other smart city infrastructure, such as public transportation systems and emergency response networks, is also contributing to market growth. Different applications like traffic monitoring systems, traffic signal control systems, and integrated corridor management (ICM) systems contribute significantly to this growth. The market is segmented geographically, with North America and Europe currently holding significant market share due to advanced infrastructure and technological adoption. However, rapid urbanization and infrastructure development in regions like Asia-Pacific are poised to drive substantial future growth. Competition in the ITMS market is intense, with a mix of established players like Siemens AG, Hitachi Ltd., and Kapsch TrafficCom AG, alongside emerging technology companies. These companies are constantly innovating to develop more advanced and integrated solutions, leading to a dynamic and competitive market landscape. The key focus for these companies is enhancing the efficiency, scalability, and interoperability of their ITMS offerings to meet the evolving demands of municipalities and transportation authorities. Future market trends suggest a growing demand for cloud-based ITMS solutions, advanced analytics capabilities for real-time traffic optimization, and increased integration with autonomous vehicle technologies. The restraints include the high initial investment costs associated with deploying ITMS infrastructure and the need for specialized expertise to manage and maintain these complex systems. However, the long-term benefits of reduced congestion, improved safety, and optimized resource allocation are overcoming these challenges, thereby propelling market growth.

This set of data houses a test set of 1000 graphs with locally skewed traffic at a rate of gamma=0.6. The throughput labels are calculated with the same methodology as the other beta sets just subjected to different traffic conditions.

Contextual explainer page for the Austin-Travis County Traffic Report page

Here are a few use cases for this project:

1. Traffic Management: Given its capability to identify different types of vehicles, this model could be used by city planners and traffic management authorities to monitor vehicular activities at various intersections. This data could aid in optimizing traffic signals timings for smoother flow and reducing congestion.

2. Safety Enhancement: The model could be used to enhance safety in accident-prone areas by closely monitoring the type and numbers of vehicles crossing any particular junction. This could act as a preventive measure against potential accidents, especially involving heavier vehicles and smaller ones like bicycles and motorbikes.

3. Urban Planning: By analyzing the types of vehicles predominantly used in a particular city or region, the model could provide valuable insights for urban planning. For instance, regions with a high number of bicycles may benefit from more bicycle lanes.

4. Fleet Management: Companies with large fleets of cars, trucks, buses or bikes could use this model to monitor and assess their vehicles' movement in real time. It can help optimize routes and schedules, thus improving logistical efficiency.

5. Law Enforcement: Law enforcement agencies could utilize the model to detect abnormalities and potentially illegal activities like street racing, unregistered heavy vehicles, or abnormal vehicle numbers at certain times. This could aid in proactive law enforcement and community safety measures.

The census count of vehicles on city streets is normally reported in the form of Average Daily Traffic (ADT) counts. These counts provide a good estimate for the actual number of vehicles on an average weekday at select street segments. Specific block segments are selected for a count because they are deemed as representative of a larger segment on the same roadway. ADT counts are used by transportation engineers, economists, real estate agents, planners, and others professionals for planning and operational analysis. The frequency for each count varies depending on City staff’s needs for analysis in any given area. This report covers the counts taken in our City during the past 12 years approximately.