Traffic Volume (24hr count). Data are updated as needed by the Transportation department (typically in the summer), and subsequently copied to VicMap and the Open Data Portal the following day.Traffic speed and volume data are collected at various locations around the city, from different locations each year, using a variety of technologies and manual counting. Counters are placed on streets and at intersections, typically for 24-hour periods. Targeted information is also collected during morning or afternoon peak period travel times and can also be done for several days at a time to capture variability on different days of the week. The City collects data year-round and in all types of weather (except for extreme events like snowstorms). The City also uses data from our agency partners like Victoria Police, the CRD or ICBC. Speed values recorded at each location represent the 85th percentile speed, which means 85% or less traffic travels at that speed. This is standard practice among municipalities to reduce anomalies due to excessively speedy or excessively slow drivers. Values recorded are based on the entire 24-hour period.The Traffic Volume dataset is linear. The lines can be symbolized using arrows and the "Direction" attribute. Where the direction value is "one", use an arrow symbol where the arrow is at the end of the line. Where the direction value is "both", use an arrow symbol where there are arrows at both ends of the line. Use the "Label" field to add labels. The label field indicates the traffic volume at each location, and the year the data was collected. So for example, “2108(05)” means 2108 vehicles were counted in the year 2005 at that location.Data are automatically copied to the Open Data Portal. The "Last Updated" date shown on our Open Data Portal refers to the last time the data schema was modified in the portal, or any changes were made to this description. We update our data through automated scripts which does not trigger the "last updated" date to change. Note: Attributes represent each field in a dataset, and some fields will contain information such as ID numbers. As a result some visualizations on the tabs on our Open Data page will not be relevant.

The Smart Mobility and Traffic Optimization Dataset integrates data from cyber-physical networks (CPNs) and social networks (SNs) to improve traffic management and smart mobility solutions. By combining real-time traffic patterns, vehicle telemetry, ride-sharing demand, public transport efficiency, social media sentiment, and environmental factors, this dataset provides a comprehensive foundation for optimizing urban mobility.

Designed to support machine learning models, the dataset enables accurate predictions of traffic congestion, mobility optimization, and smart city planning. It incorporates key metrics such as vehicle density, road occupancy, weather conditions, social media feedback, and emissions data to generate actionable insights.

Key Features: Traffic Data: Includes vehicle count, speed, road occupancy, and traffic light status, offering a granular view of real-time traffic conditions. Weather & Accidents: Integrates weather conditions and accident reports to assess their impact on congestion levels. Social Network Sentiment: Analyzes public opinions and complaints about mobility and congestion, extracted from social media platforms. Smart Mobility Factors: Examines ride-sharing demand, parking availability, and public transport delays, aiding in urban mobility planning. Environmental Impact: Monitors CO₂ emissions and pollution levels, ensuring eco-friendly traffic optimization. Target Variable: The dataset categorizes traffic congestion levels into three main groups: Low, Medium, or High, based on real-time traffic density, speed, and road occupancy.

This dataset is an essential resource for urban planners, smart city developers, and AI researchers, empowering them to create intelligent mobility solutions that reduce congestion, enhance efficiency, and improve overall urban sustainability.

US Automatic Traffic Recorder Stations Data

Vehicle Traffic Counts and Locations at US ATR Stations

By Homeland Infrastructure Foundation [source]

About this dataset

This comprehensive dataset records important information about Automatic Traffic Recorder (ATR) Stations located across the United States. ATR stations play a crucial role in traffic management and planning by continuously monitoring and counting the number of vehicles passing through each station.

The data contained in this dataset has been meticulously gathered from station description files supplied by the Federal Highway Administration (FHWA) for both Weigh-in-Motion (WIM) devices and Automatic Traffic Recorders. In addition to this, location referencing data was sourced from the National Highway Planning Network version 4.0 as well as individual State offices of Transportation.

The database includes essential attributes such as a unique identifier for each ATR station, indicated by 'STTNKEY'. It also indicates if a site is part of the National Highway System, denoted under 'NHS'. Other key aspects recorded include specific locations generally named after streets or highways under 'LOCATION', along with relevant comments providing additional context in 'COMMENT'.

Perhaps one of the most critical factors noted in this data set would be traffic volume at each location, measured by Annual Average Daily Traffic ('AADT'). This metric represents total vehicle flow on roads or highways for a year divided over 365 days — an essential numeric analyst's often call upon when making traffic-related predictions or decisions.

Location coordinates incorporating longitude and latitude measurements of every ATR station are documented clearly — aiding geospatial analysis. Furthermore, X and Y coordinates correspond to these locations facilitating accurate map plotting.

Additional information contained also includes postal codes labeled as 'STPOSTAL' where stations are located with respective state FIPS codes indicated under ‘STFIPS’. County specific FIPS code are documented within ‘CTFIPS’. Versioning information helps users track versions ensuring they work off latest datasets with temporal geographic attribute updates captured via ‘YEAR_GEO’.

Reference Source: Click Here

How to use the dataset

Introduction

Diving into the data

The dataset comprises a collection of attributes for each station such as its location details (latitude, longitude), AADT or The Annual Average Daily Traffic amount, classification of road where it's located etc. Additionally, there is information related to when was this geographical information last updated.

Understanding Columns

Here's what primary columns represent: - Sttnkey: A unique identifier for each station. - NHS: Indicates if the station is part of national highway system. - Location: Describes specific location of a station with street or highway name. - Comment: Any additional remarks related to that station. - Longitude,Latitude: Geographic coordinates. - STPostal: The postal code where a given station resides. - menu 4 dots indicates show more items** - ADT: Annual Average Daily Traffic count indicating average volume of vehicles passing through that route annually divided by 365 days - Year_GEO: The year when geographic information was last updated - can provide insight into recency or timeliness of recorded attribute values - Fclass: Road classification i.e interstate,dis,e tc., providing context about type/stature/importance or natureof theroad on whichstationlies 11.Stfips,Ctfips- FIPS codes representing state,county respectively

Using this information

Given its structure and contents,thisdatasetisveryusefulforanumberofpurposes:

1.Urban Planning & InfrastructureDevelopment Understanding traffic flows and volumes can be instrumental in deciding where to build new infrastructure or improve existing ones. Planners can identify high traffic areas needing more robust facilities.

2.Traffic Management & Policies Analysing chronological changes and patterns of traffic volume, local transportation departments can plan out strategic time-based policies for congestion management.

3.Residential/CommercialRealEstateDevelopment Real estate developers can use this data to assess the appeal of a location based on its accessibility i.e whether it sits on high-frequency route or is located in more peaceful, low-traffic areas etc

4.Environmental AnalysisResearch: Re...

Long-term Pavement performance, construction, traffic, and environmental data for more than 2500 pavement sections in the United States and Canada. More than a dozen experimental designs address specially constructed and existing asphalt and concrete pavements, and maintenance and rehabilitation strategies. Data collection has been on-going since 1990. About one third of the pavement sections are still under study. New warm-mix asphalt concrete pavement overlay sections are currently being recruited and constructed.

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Intelligent Traffic Management Market Size 2025-2029

The intelligent traffic management market size is forecast to increase by USD 24.01 billion at a CAGR of 14.8% between 2024 and 2029.

The market is experiencing significant growth due to the increasing demand for advanced, AI-based traffic solutions. This demand is driven by the escalating number of vehicles on the road and the resulting need for more efficient and effective traffic management systems. However, the market faces challenges as well. The lack of skilled professionals in government traffic organizations poses a significant barrier to the implementation and maintenance of these complex systems. Despite these challenges, the market presents numerous opportunities for companies seeking to capitalize on the growing demand for intelligent traffic management solutions.
Green traffic lights, on-demand transportation, and shared mobility services are also gaining popularity, contributing to the evolution of the traffic management infrastructure. Strategic partnerships, collaborations, and investments in research and development are key strategies for companies looking to stay competitive in this dynamic market. By addressing the skills gap and continuing to innovate, companies can help ensure the successful implementation and adoption of intelligent traffic management systems, ultimately improving traffic flow, reducing congestion, and enhancing public safety.

What will be the Size of the Intelligent Traffic Management Market during the forecast period?

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The market in the United States is experiencing significant growth, driven by the increasing demand for next-generation traffic management solutions. Traffic safety technologies, such as real-time traffic information, dynamic traffic routing, and pedestrian detection systems, are becoming essential components of the smart mobility ecosystem. The integration of traffic data acquisition and data-driven traffic management is revolutionizing urban traffic management, leading to road safety improvement and sustainable transportation. Traffic management innovation continues to shape the industry, with a focus on transportation network analysis, traffic data visualization, and traffic congestion mitigation.
Intelligent parking management and traffic incident detection are essential components of the market, ensuring efficient and safe traffic flow. The market is also witnessing the emergence of mobility-as-a-service (MaaS) platforms, which are transforming the way people move around cities. The market's growth is further fueled by the development of traffic management standards and the increasing adoption of data-driven approaches. The trend towards sustainable traffic management is also influencing the market, with a focus on reducing carbon emissions and improving overall transportation efficiency. In summary, the market in the United States is a dynamic and rapidly evolving industry, driven by the demand for next-generation traffic management solutions and the integration of data-driven approaches. The market's growth is underpinned by the need for improved traffic operations management, sustainable transportation, and the development of a smart mobility ecosystem.

How is the Intelligent Traffic Management Industry segmented?

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

Solution

  Traffic monitoring system
  Traffic signal control system
  Traffic enforcement camera
  Integrated corridor management
  Others


Component

  Surveillance cameras
  Video walls
  Traffic controllers and signals
  Others


End-user

  Government authorities
  Transport agencies
  Commercial


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan
    South Korea


  South America



  Middle East and Africa

By Solution Insights

The traffic monitoring system segment is estimated to witness significant growth during the forecast period. The market is witnessing significant advancements, particularly in the Traffic Monitoring Systems segment. By 2029, this segment is expected to evolve substantially, integrating advanced sensor technologies, video analytics, and real-time data processing frameworks. These systems will shift from reactive to proactive approaches, utilizing predictive analytics algorithms to anticipate congestion patterns and optimize signal timings dynamically. IoT-enabled devices and edge computing architectures will facilitate faster data transmission and localized decision-making, minimizing latency in traffic management operations. Furthermore, multimodal transportation data, including pub

Traffic volume counts collected by DOT for New York Metropolitan Transportation Council (NYMTC) to validate the New York Best Practice Model (NYBPM).

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.

Description: CityTrans is a dataset representing a city's transportation system. It includes various types of data related to the city's transportation infrastructure, such as road networks, public transportation routes, traffic flow, commuter patterns, and environmental factors. The dataset is designed to provide insights into urban mobility, aid in transportation planning, and serve as a testbed for innovative transportation solutions.

Features of the CityTrans dataset:

Road Networks:

• Graph representation of the road network, including nodes (intersections) and edges (road segments).
• Attributes for each road segment, such as length, speed limits, and number of lanes.
• Historical traffic data for different time periods, allowing analysis of traffic congestion patterns.

Public Transportation:

• Bus routes and schedules, including stops, timings, and passenger load data.
• Subway/Metro lines, stations, and their connections.
• Fare data, including ticket prices and types.

Traffic Flow:

• Real-time and historical traffic flow information, including vehicle counts and speeds on different road segments.
• Traffic congestion hotspots and peak hours.
• Random delays introduced to simulate traffic congestion.

Commuter Patterns:

• Origin-destination matrices indicating the number of commuters traveling between different areas of the city.
• Realistic departure and arrival times for different modes of transport, considering peak hours and congestion.
• Random delays added to arrival times based on traffic congestion.

Environmental Factors:

• Random weather data, including temperature, precipitation, and wind speed for different locations and times.
• Random air quality data, reflecting pollution levels at various locations.

The CityTrans dataset aims to provide a comprehensive view of the city's transportation system. It enables researchers, planners, and developers to analyze and visualize the city's transportation patterns, develop predictive models, create interactive visualizations, and explore innovative solutions for urban mobility challenges.

By making this dataset available, it fosters collaboration and allows others to leverage the data for research, testing algorithms, and creating innovative solutions to address transportation-related issues in urban environments.

Displays vehicle traffic volumes for arterial streets in Seattle based on spot studies that have been adjusted for seasonal variation. Data is a one time snapshot for 2007 and is maintained by Seattle Department of Transportation. Contact: Traffic Operations Refresh Cycle: None, Snapshot for 2007 Only.

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Integrated Traffic Systems Market Size 2025-2029

The integrated traffic systems market size is forecast to increase by USD 22.92 billion, at a CAGR of 14.8% between 2024 and 2029.

The market is driven by the escalating demand for efficient traffic management in response to the increasing number of passenger vehicles on the roads worldwide. This trend is further fueled by the growing issue of road traffic congestion, which negatively impacts urban mobility and productivity. However, the market faces significant challenges. The high setup cost and operating cost associated with implementing integrated traffic systems can act as a barrier to entry for potential market entrants. Despite these challenges, the market offers opportunities for companies to innovate and provide cost-effective solutions that address the pressing need for effective traffic management.
Companies that successfully navigate these challenges and deliver solutions that enhance urban mobility and reduce congestion are poised to capture a significant share in this growing market.

What will be the Size of the Integrated Traffic Systems Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market is characterized by its continuous evolution and dynamic nature, with various entities interplaying to optimize traffic flow and enhance road safety. Traffic simulation modeling and pedestrian signals work in tandem to anticipate and manage foot traffic, while traffic monitoring systems and traffic control software ensure real-time data collection and analysis. Traffic signal foundations and signal timing adjustment maintain the infrastructure's stability and efficiency, with vehicle detection sensors and traffic signal poles facilitating seamless communication between components. Network management systems and traffic data visualization enable effective centralized traffic control, integrating traffic accident data, signal timing plans, and traffic violation detection.

Traffic signal optimization and coordination are essential for congestion management, with roadway capacity analysis and dynamic message signs providing valuable insights. Traffic data acquisition and traffic incident management are crucial for maintaining optimal traffic flow, while traffic signal installation and maintenance ensure the longevity and reliability of the systems. Moreover, emerging technologies such as automated traffic enforcement, emergency vehicle preemption, and variable speed limits are transforming the landscape of traffic management, offering innovative solutions for traffic flow analysis and traffic signal hardware. Intersection design and traffic volume counts continue to evolve, incorporating the latest advancements in video image processing and traffic signal controllers. The integration of these entities fosters a comprehensive, adaptive traffic management ecosystem, addressing the ever-changing demands of modern transportation infrastructure.

How is this Integrated Traffic Systems Industry segmented?

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

Solution

  Traffic monitoring system
  Traffic control system
  Others


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    UK


  Middle East and Africa

    UAE


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

.

By Solution Insights

The traffic monitoring system segment is estimated to witness significant growth during the forecast period.

The market is experiencing significant growth due to the increasing demand for efficient and effective traffic management solutions. Traffic monitoring is a crucial aspect of these systems, enabling traffic analysts to identify patterns and address issues such as congestion, inefficient routing, and poor road conditions. Traffic monitoring systems, like those offered by SWARCO, provide real-time observations, traffic operation monitoring, and video management. The rising urbanization rates in developing countries, where traffic personnel may be scarce, further emphasize the importance of these systems. Additionally, advanced technologies such as loop detectors, traffic violation detection, and traffic signal optimization contribute to the market's expansion.

The integration of network management systems, traffic data collection, and traffic incident management also enhances the overall functionality and effectiveness of these systems. Furthermore, the implementation of centralized traffic control, traffic signal coordination, and real-time traffic m

Traffic Signal Performance Dashboards Market Outlook

According to our latest research, the global Traffic Signal Performance Dashboards market size reached USD 1.32 billion in 2024, demonstrating robust growth driven by increasing urbanization and smart city initiatives. The market is expected to expand at a CAGR of 11.4% from 2025 to 2033, reaching a projected value of USD 3.65 billion by 2033. The rising demand for real-time traffic management solutions and the integration of advanced analytics into urban infrastructure are key factors fueling this growth trajectory.

A primary growth driver for the Traffic Signal Performance Dashboards market is the accelerating adoption of intelligent transportation systems (ITS) across metropolitan areas worldwide. As cities grapple with escalating congestion and environmental concerns, the need for dynamic traffic management has never been greater. Traffic signal performance dashboards offer municipalities and transportation authorities the ability to collect, analyze, and act on real-time data, optimizing signal timing and reducing delays. This not only improves traffic flow but also contributes to lower emissions and enhanced road safety, aligning with broader sustainability goals. The proliferation of IoT devices and sensors at intersections further enhances data granularity, enabling even more precise performance analytics and predictive traffic control.

Another significant factor fueling market expansion is the ongoing digital transformation within public sector agencies and urban planning departments. Government initiatives aimed at modernizing aging infrastructure and deploying smart city technologies are allocating substantial budgets toward advanced traffic management solutions. The integration of cloud-based platforms and AI-driven analytics within traffic signal performance dashboards provides stakeholders with actionable insights, facilitating proactive maintenance and resource allocation. As urban populations continue to swell, particularly in emerging economies, the demand for scalable and interoperable traffic management solutions is expected to surge, further propelling market growth.

The evolution of connected and autonomous vehicles (CAVs) also plays a pivotal role in shaping the future of the Traffic Signal Performance Dashboards market. As vehicle-to-infrastructure (V2I) communication becomes more prevalent, traffic signal dashboards are evolving to support seamless integration with CAVs, enabling adaptive signal control and improved traffic coordination. This convergence of transportation technologies not only enhances commuter experiences but also supports broader initiatives aimed at reducing urban congestion and improving overall mobility. The ongoing advancements in machine learning and data analytics are expected to unlock new capabilities within traffic signal performance dashboards, driving innovation and market expansion through the forecast period.

From a regional perspective, North America currently leads the market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The region’s dominance is attributed to substantial investments in smart transportation infrastructure and the presence of leading technology providers. Meanwhile, Asia Pacific is anticipated to witness the highest CAGR during the forecast period, driven by rapid urbanization, government-led smart city projects, and increasing adoption of cloud-based traffic management solutions. Europe maintains a strong position, supported by stringent regulatory frameworks and a focus on sustainable urban mobility. Latin America and the Middle East & Africa are gradually increasing their market presence, underpinned by ongoing infrastructure development and modernization initiatives.

Component Analysis

The Component segment of the Traffic Signal Performance Dashboards market is broadly classified into Software, Hardware, and Services. Software solutions form the backbone of this market, encompassing advanced analytics platforms, real-time data visualization tools, and AI-driven decision support systems. The increasing demand for intuitive and customizable dashboards that provide actionable insights is fueling the growth of this segment. Software vendors are continuously enhancing their offerings with features such as predictive analytics, automated reporting, and seamless integration with existing traffic

Traffic Signal Assets (AKA Traffic Cabinets) store and protect the electrical equipment that controls traffic signals. Traffic Signal Asset data are maintained by Electrical staff and get copied to VicMap and Open Data daily. The "Last Updated" date shown on our Open Data Portal refers to the last time the data schema was modified in the portal, or any changes were made to this description. We update our data through nightly scripts which does not trigger the "last updated" date to change. Note: Attributes represent each field in a dataset, and some fields will contain information such as ID numbers. As a result some visualizations on the tabs on our Open Data page will not be relevant.

See motorcycle traffic fatality trending data and helmets life saving data.

Overview

3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.

Our corresponding paper (published at ITSC 2022) is available here. Further, we have applied 3DHD CityScenes to map deviation detection here.

Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:

Python tools to read, generate, and visualize the dataset,

3DHDNet deep learning pipeline (training, inference, evaluation) for map deviation detection and 3D object detection.

The DevKit is available here:

https://github.com/volkswagen/3DHD_devkit.

The dataset and DevKit have been created by Christopher Plachetka as project lead during his PhD period at Volkswagen Group, Germany.

When using our dataset, you are welcome to cite:

@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}

Acknowledgements

We thank the following interns for their exceptional contributions to our work.

Benjamin Sertolli: Major contributions to our DevKit during his master thesis

Niels Maier: Measurement campaign for data collection and data preparation

The European large-scale project Hi-Drive (www.Hi-Drive.eu) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.

The Dataset

After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.

1. Dataset

This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.

During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.

To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.

import json

json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)

1. HD_Map

Map items are stored as lists of items in JSON format. In particular, we provide:

traffic signs,

traffic lights,

pole-like objects,

construction site locations,

construction site obstacles (point-like such as cones, and line-like such as fences),

line-shaped markings (solid, dashed, etc.),

polygon-shaped markings (arrows, stop lines, symbols, etc.),

lanes (ordinary and temporary),

relations between elements (only for construction sites, e.g., sign to lane association).

1. HD_Map_MetaData

Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.

Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.

1. HD_PointCloud_Tiles

The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.

x-coordinates: 4 byte integer

y-coordinates: 4 byte integer

z-coordinates: 4 byte integer

intensity of reflected beams: 2 byte unsigned integer

ground classification flag: 1 byte unsigned integer

After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.

import numpy as np import pptk

file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['

Video Wall Systems for Traffic Operations Centers Market Outlook

According to our latest research, the global Video Wall Systems for Traffic Operations Centers market size in 2024 stood at USD 2.67 billion, with a robust year-on-year growth trajectory. The market is currently experiencing a healthy expansion, recording a CAGR of 7.8% from 2025 to 2033. By the end of 2033, the market is projected to reach a valuation of approximately USD 5.29 billion, reflecting the increasing adoption of advanced visualization technologies within traffic management infrastructures worldwide. This growth is primarily driven by the rising demand for real-time traffic monitoring solutions, the proliferation of smart city initiatives, and the need for enhanced situational awareness in urban environments.

One of the primary growth factors fueling the Video Wall Systems for Traffic Operations Centers market is the rapid urbanization and the subsequent surge in vehicular traffic across major metropolitan areas. As cities expand and the complexity of transportation networks increases, the necessity for centralized and efficient traffic management becomes paramount. Video wall systems provide traffic operations centers with the capability to visualize and manage large volumes of data, including live traffic feeds, incident alerts, and infrastructure status updates. The integration of advanced display technologies, such as ultra-high-definition LED and LCD panels, allows operators to make informed decisions swiftly, thereby reducing congestion and enhancing road safety.

Another significant driver is the evolution of smart city projects and the increasing deployment of intelligent transportation systems (ITS). Governments and municipal authorities are investing heavily in digital infrastructure to address challenges related to mobility, environmental sustainability, and public safety. Video wall systems serve as the backbone for these initiatives by enabling seamless data visualization and real-time collaboration among multiple agencies. The ability to integrate feeds from various sources—including surveillance cameras, sensors, and emergency communication networks—empowers traffic operators to coordinate incident responses more effectively. This trend is further amplified by the growing emphasis on data-driven governance and the adoption of AI-powered analytics within traffic operations centers.

The continuous advancement in display and controller technologies also acts as a major catalyst for market growth. Modern video wall systems are increasingly equipped with features such as bezel-less screens, high brightness, energy efficiency, and interactive touch capabilities. These innovations not only enhance the visual experience but also contribute to the operational efficiency of traffic management centers. Furthermore, the emergence of modular and scalable video wall architectures allows organizations to tailor solutions according to their specific requirements and budget constraints. As a result, both large metropolitan cities and smaller municipalities are able to leverage these systems to optimize their traffic operations and incident management processes.

From a regional perspective, North America currently holds the largest share of the Video Wall Systems for Traffic Operations Centers market, driven by early technology adoption and substantial government investments in transportation infrastructure. However, the Asia Pacific region is expected to witness the fastest growth rate over the forecast period, with a projected CAGR of over 9.2%. This surge is attributed to rapid urbanization, increasing vehicle ownership, and ongoing smart city developments across countries such as China, India, and Japan. Europe and the Middle East & Africa are also demonstrating steady growth, propelled by regulatory mandates for public safety and the modernization of traffic management systems. Latin America, while currently holding a smaller market share, is anticipated to experience gradual growth as urban mobility challenges intensify.

The advent of Interactive Video Wall technology is revolutionizing the way traffic operations centers manage and display information. These systems allow operators to engage with the content directly, offering a more dynami

📊 Delhi Metro Ridership & Operational Statistics Dataset

Subtitle

A comprehensive dataset representing ridership, ticket revenue, and operational performance of the Delhi Metro one of the largest urban transit systems in the world.

About the Dataset

The Delhi Metro is a rapid transit system serving the National Capital Region (NCR) of India. It plays a crucial role in reducing traffic congestion and providing sustainable public transportation to millions of passengers every day.

This dataset captures multiple performance indicators of the Delhi Metro network over time, including:

Total metro trips operated Daily total passengers Ticket revenue Average passenger distance traveled per trip Top stations based on passenger demand Total stations operational

These data points help in analyzing metro usage patterns, operational efficiency, and transit demand in the region.

Why This Dataset is Useful

This dataset enables research in:

Urban transport planning Revenue & demand forecasting Passenger travel behavior analysis Transportation infrastructure optimization Dashboard development & data storytelling Academic machine learning projects

Potential Use Cases

• Time-series forecasting of passengers and revenue
• Peak-hour identification using station-based data
• Policy evaluation on fare changes and new lines
• Visualization dashboards (e.g., Plotly/Dash, Power BI, Tableau)

Source

Data has been collected, cleaned, and aggregated using publicly available metro operational insights, news reports, and transit performance summaries released by the Delhi Metro Rail Corporation (DMRC).

File Details

(Adjust fields based on your actual dataset columns — I can refine if you share final structure.)

Licensing

License: CC BY 4.0 (Users must provide attribution when using the dataset)

If you want, I can also add:

Thumbnail Image for Kaggle Dataset Tags & Categories for better discoverability Example Notebooks (Exploration + Forecast models) Dashboard Preview Screenshots

Content

United Airlines yearly passenger data from 1995 to 2020 is included in this dataset. It contains data on the overall number of passengers transported, the frequency of flights, and operating patterns throughout the last 25 years. The information offers a thorough analysis of the airline's performance, emphasizing significant swings impacted by international events, market dynamics, and economic circumstances.

Context

The dataset is a useful tool for examning long term travel patterns in the aviation sector. It can be used by researchers, data analysts, and aviation experts to anticipate future travel demands, evaluate the effects of economic cycles, and examine passenger growth trends. In studies on air transport, it also facilitates predictive modeling and exploratory data analysis (EDA).

Acknowledgement

As a valuable resource for making United Airlines passenger data publicly available for analysis and research

Video Wall Systems for Traffic Operations Centers Market Outlook

According to our latest research, the global market size for Video Wall Systems for Traffic Operations Centers was valued at USD 1.72 billion in 2024, exhibiting a robust growth trajectory with a CAGR of 7.1% from 2025 to 2033. By the end of 2033, the market is forecasted to reach USD 3.18 billion. The primary growth driver for this market is the increasing demand for real-time traffic monitoring solutions to enhance urban mobility and safety, supported by rapid urbanization and smart city initiatives worldwide.

The surge in urban populations, coupled with the proliferation of vehicles on roads, has significantly amplified the need for advanced traffic management solutions. Video wall systems have emerged as a pivotal technology in traffic operations centers, offering seamless visualization of live feeds, incident alerts, and analytical data. These systems enable operators to manage complex transportation networks efficiently, ensuring timely interventions during emergencies and optimizing traffic flow. The integration of cutting-edge technologies such as AI-driven analytics, IoT sensors, and high-definition displays further enhances the operational capabilities of these centers, fueling the adoption of video wall solutions.

Another key growth factor is the increasing investment by governments and municipal bodies in infrastructure modernization. As cities strive to become smarter and more connected, the deployment of sophisticated video wall systems in traffic operations centers is becoming a standard practice. These systems support a wide range of applications, including surveillance, incident management, and data visualization, all of which are critical for proactive decision-making. The emphasis on reducing congestion, improving public safety, and ensuring efficient transportation management is prompting significant budget allocations toward advanced visualization solutions.

Technological advancements in display technologies, such as the evolution from LCD and DLP to ultra-fine pitch LED panels, are further propelling market growth. These innovations offer superior image clarity, scalability, and energy efficiency, making them ideal for 24/7 operations in traffic control environments. Additionally, the growing trend of integrating software-based solutions for real-time analytics and centralized control is adding value to traditional hardware-centric video wall systems. This convergence of hardware and software is creating new opportunities for market players to deliver comprehensive, end-to-end solutions tailored to the unique needs of traffic operations centers.

Regionally, North America continues to dominate the market, driven by extensive investments in smart transportation infrastructure and early adoption of advanced technologies. However, the Asia Pacific region is witnessing the fastest growth, underpinned by large-scale urbanization projects, government-led smart city initiatives, and rapidly expanding transportation networks. Europe also presents lucrative opportunities, particularly in countries emphasizing sustainable urban mobility and stringent traffic management regulations. The Middle East & Africa and Latin America are gradually catching up, with increasing focus on urban planning and public safety enhancements.

Component Analysis

The Component segment of the Video Wall Systems for Traffic Operations Centers Market is categorized into Display Units, Controllers, Software, and Accessories. Display units remain the backbone of any video wall system, providing the visual interface necessary for operators to monitor and respond to real-time traffic scenarios. Innovations in display technology, such as higher resolution, brightness, and energy efficiency, are driving the replacement cycle for legacy systems. The demand for larger, bezel-less, and modular displays is increasing, as traffic operations centers require uninterrupted visualization across multiple sources. As cities grow and traffic complexity increases, the need for scalable and reliable display units becomes even more pronounced, making this sub-segment a significant contributor to overall market revenue.

Controllers play a pivotal role in managing and distributing video signals across multiple displays, enabling seamless integration of various data sources s

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 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. The real-time 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 color coded map leverages historical, real time and predictive traffic data. Historical traffic is based on the average of observed speeds over the past three years. 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.