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.
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AbstractThis national harmonised dataset uses daily updates from State and Territory roadworks endpoints (APIs) to build a nationally consistent, harmonised historic database of roadworks and road closures over time across all of Australia. Daily updates from state/territory roadworks systems are queried to compile a set of historical and planned roadworks for all of Australia. The geospatial coverage of the dataset is all state-managed roads in Australia. Most jurisdictions include planned and unplanned works and closures, however data for Tasmania currently includes planned roadworks only. There are roadworks and road closures prior to 2016 but the dates may not be correct. Roadworks and road closures currently in progress may have an end date years into the future - the date predicted when roadworks or road closure will be complete.This data set could be used to understand (and potentially predict and manage) road damage and road closures and improve resilience in the context of natural disasters or disruptions.The API User Guide describes how the jurisdictions' data is combined and how to use the service for your application.Currency2016 to presentDate modified: TodayModification frequency: DailyData ExtentSpatial ExtentWest Bounding Longitude : 111°South Bounding Latitude : -43°East Bounding Longitude : 154°North Bounding Latitude : -10°Temporal Extent2016 to presentSource InformationThis data set is provided by the Department of Infrastructure, Transport, Regional Development, Communications and the Arts (DITRDCA).Map ServerMetadataPublic listingLineage StatementThe Harmonised National Roadworks and Road Closures dataset uses daily updates from state/territory roadworks systems to compile a set of historical roadworks for all of Australia. The geospatial coverage of the dataset is all state-managed roads in Australia. Most jurisdictions include planned and unplanned works and closures, however data for Tasmania currently includes planned roadworks only.Each state/territory uses a different set of roadwork 'types' to classify the works. The harmonisation process groups like types into a set of categories which are consistent across all jurisdictions, for reporting and visualisation purposes. Where the data indicates the road is closed (via text description), a category of Road Closure is applied, irrespective of the state/territory 'type'.Data DictionaryAll LayersAttribute NameDescriptionidUnique identifier of the record in NFDH’s ArcGIS databaseunique_identifierUnique identifier provided by the source agencycategoryThe category of road event the record relates to, determined by the NFDH. The categories in the dataset are Event, Hazard, Other, Other Works, Road Closure, Road ConditionstypeClassification of the type of roadwork, provided by the source agency. Each of the types provided by the states/territories are mapped to a ‘category’ to standardise the data. This field is included for the user’s information only and is not harmonised. It is not consistently applied between jurisdictionsfrom_dateDate and time the roadwork starts, in UTC format (milliseconds)to_dateDate and time the roadwork ends, in UTC format (milliseconds)planned_start_dateDate and time the roadwork is planned to start – this field is unpopulated for all records in the dataset as the source APIs do not include this informationplanned_end_dateDate and time the roadwork is planned to end – this field is unpopulated as the source APIs do not include this information for all records in the datasetmodified_dateThe date the record was last modified/updated in the source data, in UTC format (milliseconds)descriptionTextual description of the roadworkstreet_nameName of the road on which the work or closure is occurringside_streetHuman readable description of intersecting street nearest the start of the roadworks (could also be a motorway/freeway ramp)end_side_streetHuman readable description of intersecting street nearest the end of the roadworks (could also be a motorway/freeway ramp) – this field is unpopulated for all records in the dataset as the source APIs do not include this informationdirectionDirection/s of travel affected – this field is unpopulated for all records in the dataset as the source APIs do not include this informationstateThe state/territory which has provided the record via their source API. Note that this does not always align with the state/territory managing the road, or the state/territory in which the road is located.capture_dateThe date the record was first captured in the NFDH dataset, in UTC format (milliseconds)pointGeospatial location of roadwork – this field returns X and Y coordinates corresponding to longitude and latitude respectivelyDetailed descriptions of these attributes and the abbreviations and values used can be found herePoint of ContactOrganisation Name: Department of Infrastructure, Transport, Regional Development, Communications and the ArtsEmail address: freightdatahub@infrastructure.gov.auOnline Resource: National Freight Data Hub
Road Closure Datahttps://gis.charleston-sc.gov/road-closures-regional/The city began capturing road closure data in October 2015 while responding to the 1000 year flood event. Since then, the City has been refining the process to capture and retain a history of city street closures. Emergency closures are reported based on field reports from first responders and city staff operating in the field. This data is used for situation awareness during flooding events by staff and the public This is not a detailed and complete survey of inundated roads. Not all flooding events are actively mapped and not all streets impacted are captured.This information should be used as a general information tool. Tide and Weather DataTide and weather data is collected and processed from NOAA sources and joined to the road closures using the closure start date and time. The process of calculating the joining the tide and weather data is experimental and subject to change.NOAA Weatherhttps://www.ncdc.noaa.gov/cdo-web/datasets/GHCND/stations/GHCND:USW00013782/detailNOAA tide data api (station=8665530)https://api.tidesandcurrents.noaa.gov/api/prod/ Fields STREET – Street name as recorded by the mapper REASON – Flood, Construction, Emergency, Warning SOURCE – CPD, etc. Type – Full, partial, warning ROADCLOSUREDATE - Start Datetime ROADCLOSUREEND - End Datetime INDIDENTDATE - Start Date (no time) YEAR – Start Date Year RoadClosureHours – Calculated (Hours between start and end) CommuteHours – Calculated (6AM-10AM = Morning Commute and 3PM-7PM= Evening Commute) TideDate - Nearest High / Low tide time and date MinsUntilTide – Calculated (minutes from start to nearest High / Low tide past or future) FloodRisk Calculated for nearest high / low tide: >=8 (Major), >= 7.5 (Moderate), and >=7 (Minor) NearestHighLowTide – MLLW height of nearest high or low tide MaxTide – Highest MLLW tide of the day TideType – Tide type of nearest high or low tide TideHeight - nearest 6 minute tide reading MoonPhase - moon phase in decimal MoonPhaseDesc - Calculated <=.05 or = 1 = New Moon, between .45 and .6 = Full Moon, else Other DailyPrecipitation _Downtown – Noaa DailyPrecipitationCat_Downtown – Calculated rain categories Fastest2minWindDirection_Downtown - wind direction bearing Fastest2minWindSpeed_Downtown Fastest2minWindDirectionDesc_Downtown – calculated directionals FloodReason_6MIN– Downtown NOAA weather and 6 minute tide categories. High Tide / Rain: Rain > =.5 and tide >= 6’ , Rain / Non-High Tide: Rain >= .5’ and tide type = low , High Tide / No Rain: Rain < .5 AND tide >= 6’ , No Rain/Low Tide:FloodReason_Day – Using NOAA Max high tide for the day EventName – event name for named events
At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Forward Collision Warning Driver Behavior Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.
What Makes Our Data Unique? Our Forward Collision Warning Driver Behavior Data is distinguished by its real-time collection capabilities, utilizing our built-in computer vision technology to identify and capture instances where a driver is either tailgating or experiences a near collision and recieves a warning through our app. These critical safety events are indicative of aggressive driving behavior and potential risks on the road. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis and take proactive measures to enhance road safety.
How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.
Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior related to tailgating and near collisions. This analysis can help in understanding patterns related to rule compliance, driver attentiveness, and potential risk factors.
Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better object detection and decision-making capabilities in complex road environments.
Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of forward collision warnings, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.
Integration with Our Broader Data Offering The Forward Collision Warning Driver Behavior Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.
In summary, Driver Technologies' Forward Collision Warning Driver Behavior Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Forward Collision Warning Driver Behavior Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.
Sample Data: https://cloud.drivertechnologies.com/shared?s=146&t=4:03&token=0f469c88-d578-4b4f-80b2-f53f195683b2
At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Speed Over Limit Driver Behavior Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.
What Makes Our Data Unique? Our Speed Over Limit Driver Behavior Data is distinguished by its real-time collection capabilities, utilizing our built-in computer vision technology to identify and capture instances where a driver nearly gets into an accident. This data reflects critical safety events that are indicative of potential risks and non-compliance with traffic regulations. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis.
How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios. For our Speed Over Limit Driver Behavior Data, we leverage computer vision models to read speed limit signs as the driver drives past them, then compare that to speed data captured using the phone's sensor.
Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior. This analysis can help in understanding patterns related to rule compliance and potential risk factors.
Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better decision-making capabilities in complex driving environments.
Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of significant events, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.
Integration with Our Broader Data Offering The Speed Over Limit Driver Behavior Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.
In summary, Driver Technologies' Speed Over Limit Driver Behavior Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Speed Over Limit Driver Behavior Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.
At Driver Technologies, we specialize in collecting high-quality, highly-anonymized, driving data crowdsourced using our dash cam app. Our Do Not Enter Sign Annotated Imagery Video Data is built from the millions of miles of driving data captured and is optimized to be trained for whatever computer vision models you need and enhancing various applications in transportation and safety.
What Makes Our Data Unique? What sets our Do Not Enter Sign Annotated Imagery Video Data apart is its comprehensive approach to road object detection. By leveraging advanced computer vision models, we analyze the captured video to identify and classify various road objects encountered during an end user's trip. This includes road signs, pedestrians, vehicles, traffic signs, and road conditions, resulting in rich, annotated datasets that can be used for a range of applications.
How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.
Primary Use-Cases and Verticals The Do Not Enter Sign Annotated Imagery Video Data is tailored for various sectors, particularly those involved in transportation, urban planning, and autonomous vehicle development. Key use cases include:
Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better object detection and decision-making capabilities in complex road environments.
Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic light placement. Our data can also aid in making sure municipalities have an accurate count of signs in their area.
Integration with Our Broader Data Offering The Do Not Enter Sign Annotated Imagery Video Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and computer vision models.
In summary, Driver Technologies' Do Not Enter Sign Annotated Imagery Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Do Not Enter Sign Annotated Imagery Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.
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Predictive Analysis of Road Accidents in Western Australia
Project Description
This project focuses on predicting road accidents in Western Australia using various datasets, including historical accident data, traffic patterns, weather conditions, and other relevant factors. The analysis aims to identify key predictors of accidents and develop a predictive model to enhance road safety initiatives.
Features
Data collection from multiple sources (e.g., accident… See the full description on the dataset page: https://huggingface.co/datasets/kalley/WA_Crash.
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 t
The information in the abstract is translated from the archaeological report: Due to local plan work within the property Hackefors 5:1 in Landeryd parish, Linköping municipality, an archaeological survey (stage 2) was performed. In area 1-3 no archaeological remains were found. In area 4, remains of a road and a possible farmyard were discovered. The road ought to be constructed before 1692 since it is marked on a map from this year. No further archaeological measures are suggested to be taken within areas 1-3. A preliminary survey is suggested for area 4.
Purpose:
The information in the abstract is translated from the archaeological report: The purpose of the survey was to determine if the area contained any unmarked archaeological remains.
The ZIP file consist of GIS files and an Access database with information about the excavations, findings and other metadata about the archaeological survey.
This dataset represents the centre points of each Zone or Operational Area where Photo Enforcement was historically "scheduled" to be conducted. An enforcement unit can be found anywhere along the area of the Zone. An enforcement unit may not be able to operate at the specified zone and subsequently move to a zone that is not scheduled for enforcement. The centre points are extracted for mapping purposes only and are not intended to imply or suggest that is where the Photo Enforcement is being conducted. Designated Zones are continuously reviewed, revised, added, removed and otherwise updated in accordance to the guidelines for establishing a Zone for photo enforcement. Automated enforcement is expected to be operating at the locations indicated. Please be advised that automated enforcement may be used at other locations within Edmonton as well. Locations selected for enforcement may be removed or added as determined by weather, road conditions, roadway closures or construction, equipment issues or other unforeseen circumstances.
Each enforcement site has one or more reasons for why enforcement is taking place. The list of reasons are:
a) Areas or intersections where conventional enforcement is unsafe or ineffective; b) Areas or intersections with an identifiable, documented history of collisions; c) Areas or intersections with an identifiable, documented history of speeding problems; d) Intersections with an identifiable, documented history of offences; e) Intersections near schools, post-secondary institutions, or other areas with high pedestrian volumes; f) School and playground zones or areas; g) Construction zones; or h) Areas where the public or a community has expressed concerns related to speeding.
Please refer to the FAQ the City has available in regards to Photo Enforcement: https://www.edmonton.ca/enforcement.
At Driver Technologies, we specialize in collecting high-quality, highly-anonymized, driving data crowdsourced using our dash cam app. Our Stop Sign Urban Planning Video Data is built from the millions of miles of driving data captured and is optimized to be trained for whatever computer vision models you need and enhancing various applications in transportation and safety.
What Makes Our Data Unique? What sets our Stop Sign Urban Planning Video Data apart is its comprehensive approach to road object detection. By leveraging advanced computer vision models, we analyze the captured video to identify and classify various road objects encountered during an end user's trip. This includes stop signs, pedestrians, vehicles, traffic signs, and road conditions, resulting in rich, annotated datasets that can be used for a range of applications.
How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.
Primary Use-Cases and Verticals The Stop Sign Urban Planning Video Data is tailored for various sectors, particularly those involved in transportation, urban planning, and autonomous vehicle development. Key use cases include:
Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better object detection and decision-making capabilities in complex road environments.
Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic light placement. Our data can also aid in making sure municipalities have an accurate count of signs in their area.
Integration with Our Broader Data Offering The Stop Sign Urban Planning Video Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and computer vision models.
In summary, Driver Technologies' Stop Sign Urban Planning Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Stop Sign Urban Planning Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.
The eco-approach and departure (EAD) application for signalized intersections has been proved to be environmentally efficient in a Connected and Automated Vehicles (CAVs) system. In the real-world traffic, the traffic-related information received from sensing or communication devices is highly uncertain due to the limited sensing range and varying driving behaviors of other vehicles. This uncertainty increases the difficulty to predict the actual queue length of the downstream intersection. It further brings great challenge to derive an energy efficient speed profile for vehicles to follow. This research proposes an adaptive strategy for connected eco-driving towards a signalized intersection under real world conditions including uncertain traffic condition. A graph-based model is created with nodes representing dynamic states of the host vehicle (distance to intersection and current speed) and indicator of queue status and directed edges with weight representing expected energy consumption between two connected states. Then a dynamic programing approach is applied to identify the optimal speed for each vehicle-queue-signal state iteratively from downstream to the upstream. The uncertainty can be addressed by formulating stochastic models when describing the transition of queue-signal state. For uncertain traffic conditions, numerical simulation results show an average energy saving of 9%. It also indicates that energy consumption of a vehicle equipped with adaptive EAD strategy and a 100m-range sensor is equivalent to a vehicle with conventional EAD strategy and a 190m-range sensor. To some extent, the proposed strategy could double the effective detection range in eco-driving. The trajectory data was collected from numerical simulation using three types of methods including the proposed method in this research, the ideal method and other baseline EAD methods. The proposed method corresponds to the adaptive strategy for connected eco-driving with known historical queue distribution. The ideal trajectory for absolute minimum energy consumption can be derived when the actual queue length is known (i.e. perfect information) at the beginning of the simulation. This strategy can only be achieved if all vehicles are connected to share their positions to the study vehicle. Besides the ideal method, couple of baseline EAD methods (Baselinek) are setup for comparison: Assuming the queue length to be Qk, the vehicle first follows the ideal trajectory of the assumed Qk length, then change to the corresponding strategy after detecting the real queue length. These baselines are the methods given the same information as the proposed method except the historical queue distribution is missing. Note that if k is 0, Baseline0 corresponds to the scenario when the vehicle follows the existing EAD strategy with no-queue assumption until the sensor detects preceding traffic.
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Here are a few use cases for this project:
Traffic Monitoring Systems: The "Jordan Banners" model can be used to monitor and analyze traffic patterns based on the identification of road signs, especially in areas where Arabic is predominantly spoken. The system could detect specific sign categories, traffic updates or alert drivers about upcoming road conditions or changes ahead.
Autonomous Vehicles Navigation: In the realm of self-driving cars, this computer vision model can help vehicles recognize and understand road signs, thereby facilitating effective navigation and ensuring safety on the road, particularly in regions where the banners or signs are in Arabic.
Augmented Reality Applications: For tourists or foreigners in Arabic-speaking regions, the model could power AR applications to translate Arabic banners in real-time into the user's native language, enhancing their navigation experience.
Cultural Heritage and Historical Studies: Researchers studying historical or cultural sites can use this model to identify and understand the importance of various banners in Arabic and their significance. It could also assist in digital preservation of these sites.
Public and Urban Planning: City planners and public workers can use the model to assess the clarity, visibility and positioning of the banners or signs for better urban development planning.
This dataset represents the historic Zones or the historic Operational Areas where Photo Radar Enforcement was scheduled to be conducted. An enforcement unit can be found anywhere along the area of the Zone. Designated Zones are continuously reviewed, revised, added, removed and otherwise updated in accordance to the guidelines for establishing a Zone for photo enforcement. Automated enforcement is expected to be operating at the locations indicated. Please be advised that automated enforcement may be used at other locations within Edmonton as well. Locations selected for enforcement may be removed or added as determined by weather, road conditions, roadway closures or construction, equipment issues or other unforeseen circumstances.
Each enforcement site has one or more reasons for why enforcement is taking place. The list of reasons are:
a) Areas or intersections where conventional enforcement is unsafe or ineffective; b) Areas or intersections with an identifiable, documented history of collisions; c) Areas or intersections with an identifiable, documented history of speeding problems; d) Intersections with an identifiable, documented history of offences; e) Intersections near schools, post-secondary institutions, or other areas with high pedestrian volumes; f) School and playground zones or areas; g) Construction zones; or h) Areas where the public or a community has expressed concerns related to speeding.
Please refer to the FAQ the City has available in regards to Photo Enforcement: https://www.edmonton.ca/enforcement.
At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Tailgating Insurance Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.
What Makes Our Data Unique? Our Tailgating Insurance Data is distinguished by its real-time collection capabilities, utilizing our built-in computer vision technology to identify and capture instances where a driver tailgates the vehicle in front. This data reflects critical safety events that are indicative of potential risks and non-compliance with traffic regulations. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis.
How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.
Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior. This analysis can help in understanding patterns related to rule compliance and potential risk factors.
Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better decision-making capabilities in complex driving environments.
Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of significant events, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.
Integration with Our Broader Data Offering The Tailgating Insurance Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.
In summary, Driver Technologies' Tailgating Insurance Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Tailgating Insurance Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.
At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Hard Braking Telematics Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.
What Makes Our Data Unique? Our Hard Braking Data is distinguished by its real-time collection capabilities, utilizing the built-in accelerometer and gyroscope sensors of smartphones to capture telematics during driving. This data reflects instances of hard braking events, which are key indicators of aggressive driving behavior and potential risks on the road. Through our dataset, gain access to videos, processed through our computer vision model, of drivers hard braking and/or a telematics-only trip with an instance of a hard brake. By providing data on braking events, our dataset empowers clients to perform in-depth analysis.
How Is the Data Generally Sourced? The data is sourced directly from users who use our dash cam app. As users drive, our app monitors and records telematics data, ensuring that the information is both authentic and representative of real-world driving conditions.
Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our telematics data to analyze driving habits and identify trends in aggressive driving behavior. Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of hard braking events, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.
Integration with Our Broader Data Offering The Hard Braking Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.
In summary, Driver Technologies' Hard Braking Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Hard Braking with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.
At Driver Technologies, we specialize in collecting high-quality, highly-anonymized driving data crowdsourced through our dash cam app. Our Motorcycle Machine Learning Video Data is built from millions of miles of driving data captured by our users and is optimized for training machine learning models, enhancing various applications in road safety, and advancing the future of mobility.
What Makes Our Data Unique? What sets our Motorcycle Machine Learning Video Data apart is its comprehensive approach to road object detection. By leveraging advanced machine learning models, we analyze the captured video to identify and classify various road objects encountered during a trip. This includes vehicles, pedestrians, traffic signs androad conditions, resulting in rich, annotated datasets that can be applied across a wide range of applications.
How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.
Primary Use-Cases and Verticals The Motorcycle Machine Learning Video Data is tailored for various sectors, particularly those involved in motorcycle safety, transportation planning, and autonomous vehicle development. Key use cases include:
Training Machine Learning Models: Clients can utilize our annotated data to develop and refine machine learning models for applications in road safety and autonomous vehicle systems, ensuring better object detection and decision-making capabilities.
Urban Planning and Infrastructure Development: Our data helps municipalities understand road usage patterns, particularly those involving motorcycles, enabling them to make informed decisions regarding infrastructure improvements, safety measures, and traffic management.
Insurance Analytics: Insurance companies can leverage insights from our data to assess risk in various environments, aiding in the development of tailored insurance products for motorcyclists and improving claims processing.
Integration with Our Broader Data Offering The Motorcycle Machine Learning Video Data is an essential component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and machine learning models.
In summary, Driver Technologies' Motorcycle Machine Learning Video Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation in road safety and mobility. By integrating our Motorcycle Machine Learning Video Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.
VITAL SIGNS INDICATOR Time Spent In Congestion (T7)
FULL MEASURE NAME Congested delay on regional freeways
LAST UPDATED May 2017
DESCRIPTION Time spent in traffic congestion – also known as congested delay – refers to the number of minutes weekday travelers spend in congested conditions in which freeway speeds drop below 35 mph. Total delay, a companion measure, includes both congested delay and all other delay in which speeds are below the posted speed limit.
DATA SOURCE Metropolitan Transportation Commission: Historical Congestion Analysis
California Department of Finance Forms E-5 and E-8 http://www.dof.ca.gov/research/demographic/reports/estimates/e-8/ http://www.dof.ca.gov/research/demographic/reports/estimates/e-5/2011-20/view.php
California Employment Development Department: Labor Market Information http://www.labormarketinfo.edd.ca.gov/
CONTACT INFORMATION vitalsigns.info@mtc.ca.gov
METHODOLOGY NOTES (across all datasets for this indicator) Delay statistics only include freeway facilities and rely upon INRIX traffic data. They reflect delay on a typical weekday, which is defined as Tuesday through Thursday during peak traffic months. Delay statistics emphasize recurring delay - i.e. consistent delay greater than 15 minutes on a specific freeway segment. Congested delay is defined as congestion occurring with speeds less than 35 mph and is commonly recognized as inefficient delay (meaning that the freeway corridor is operating at speeds low enough to reduce throughput - as opposed to speeds greater than 35 mph which increase throughput). Data sources listed above were used to calculate per-capita and per-worker statistics; national datasets were used for metro comparisons and California datasets were used for the Bay Area. Top congested corridors are ranked by total vehicle hours of delay, meaning that the highlighted corridors reflect a combination of slow speeds and heavy traffic volumes. Historical Bay Area data was estimated by MTC Operations staff using a combination of internal datasets to develop an approximate trend back to 1998. The metropolitan area comparison was performed for the combined primary urbanized areas (San Francisco-Oakland and San Jose) as well as nine other major metropolitan areas' core urbanized area. Because the Texas Transportation Institute no longer reports congested freeway delay or total freeway delay (focusing solely on total regional delay), 2011 data was used to estimate 2014 total freeway delay for each metro area by relying upon the freeway-to-regional ratio from 2011. Estimated urbanized area workers were used for this analysis using the 2011 ratios, which accounts for slight differentials between Bay Area data points under the regional historical data and the metro comparison analysis. To explore how 2016 congestion trends compare to real-time congestion on the region’s freeways, visit 511.org.
At Driver Technologies, we are dedicated to harnessing advanced technology to gather anonymized critical driving data through our innovative dash cam app, which operates seamlessly on end users' smartphones. Our Drowsy Driving Alert Insurance Data offering is a key resource for understanding driver behavior and improving safety on the roads, making it an essential tool for various industries.
What Makes Our Data Unique? Our Drowsy Driving Alert Insurance Data is distinguished by its real-time collection capabilities, utilizing our built-in computer vision technology to identify and capture instances where a driver is displaying drowsy behavior and receives a warning through our app. While videos of drivers' faces are unavailable to protect privacy, the value of this data lies in understanding the different contexts in which a driver becomes drowsy, the driving behavior exhibited by drowsy drivers, and the broader effects of drowsy driving on road safety. By providing data on these significant events, our dataset empowers clients to perform in-depth analysis and take proactive measures to enhance road safety.
How Is the Data Generally Sourced? Our data is sourced directly from users who utilize our dash cam app, which harnesses the smartphone’s camera and sensors to record during a trip. This direct sourcing method ensures that our data is unbiased and represents a wide variety of conditions and environments. The data is not only authentic and reflective of current road conditions but is also abundant in volume, offering millions of miles of recorded trips that cover diverse scenarios.
Primary Use-Cases and Verticals Driver Behavior Analysis: Organizations can leverage our dataset to analyze driving habits and identify trends in driver behavior related to tailgating and near collisions. This analysis can help in understanding patterns related to rule compliance, driver attentiveness, and potential risk factors.
Training Computer Vision Models: Clients can utilize our annotated data to develop and refine their own computer vision models for applications in autonomous vehicles, ensuring better object detection and decision-making capabilities in complex road environments.
Improving Risk Assessment: Insurers can utilize our dataset to refine their risk assessment models. By understanding the frequency and context of drowsy driver warnings, they can better evaluate driver risk profiles, leading to more accurate premium pricing and improved underwriting processes.
Integration with Our Broader Data Offering The Drowsy Driving Alert Insurance Data is a crucial component of our broader data offerings at Driver Technologies. It complements our extensive library of driving data collected from various vehicles and road users, creating a comprehensive data ecosystem that supports multiple verticals, including insurance, automotive technology, and smart city planning.
In summary, Driver Technologies' Drowsy Driving Alert Insurance Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Drowsy Driving Alert Insurance Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.
The map layers in this service provide color-coded maps of the traffic conditions you can expect for the present time (the default). The map shows present traffic as a blend of live and typical information. Live speeds are used wherever available and are established from real-time sensor readings. Typical speeds come from a record of average speeds, which are collected over several weeks within the last year or so. Layers also show current incident locations where available. By changing the map time, the service can also provide past and future conditions. Live readings from sensors are saved for 12 hours, so setting the map time back within 12 hours allows you to see a actual recorded traffic speeds, supplemented with typical averages by default. You can choose to turn off the average speeds and see only the recorded live traffic speeds for any time within the 12-hour window. Predictive traffic conditions are shown for any time in the future.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. 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.Data sourceEsri’s typical speed records and 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.Data coverageThe 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. Look at the coverage map to learn whether a country currently supports traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, visit the directions and routing documentation and the ArcGIS Help.SymbologyTraffic 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%To view live traffic only—that is, excluding typical traffic conditions—enable the Live Traffic layer and disable the Traffic layer. (You can find these layers under World/Traffic > [region] > [region] Traffic). To view more comprehensive traffic information that includes live and typical conditions, disable the Live Traffic layer and enable the Traffic layer.ArcGIS Online organization subscriptionImportant Note:The World Traffic map service is available for users with an ArcGIS Online organizational subscription. To access this map service, you'll need to sign in with an account that is a member of an organizational subscription. If you don't have an organizational subscription, you can create a new account and then sign up for a 30-day trial of ArcGIS Online.
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.