Road Safety Statistics releases [missing hyperlink]

Data download tool [missing hyperlink] for bespoke breakdowns of our data.

These files provide detailed road safety data about the circumstances of personal injury road accidents in GB from 1979, the types of vehicles involved and the consequential casualties. The statistics relate only to personal injury accidents on public roads that are reported to the police, and subsequently recorded, using the STATS19 accident reporting form.

There has been an increasing demand for more up to date information on reported road accidents to be made available to the public, stakeholders and researchers. As a result, the Department for Transport made a dataset covering accidents for the first and second quarters of 2018 in Great Britain available for the first time on data.gov.uk. The data released was an un-validated subset and has been superseded by the full accident dataset for 2018, released after validation for the full year.

All the data variables are coded rather than containing textual strings. The lookup tables are available in the "Additional resources" section towards the bottom of the table.

Please note that the 2015 data were revised on the 29th September 2016. Accident, Vehicle and Casualty data for 2005 - 2009 are available in the time series files under 2014. Data for 1979 - 2004 are available as a single download under 2004 below.

Also includes: Results of breath-test screening data from recently introduced digital breath testing devices, as provided by Police Authorities in England and Wales Results of blood alcohol levels (milligrams / 100 millilitres of blood) provided by matching coroners’ data (provided by Coroners in England and Wales and by Procurators Fiscal in Scotland) with fatality data from the STATS19 police data of road accidents in Great Britain. For cases when the Blood Alcohol Levels for a fatality are "unknown" are a consequence of an unsuccessful match between the two data sets.

Road Safety Statistics releases and guidance about the data collection. Collision analysis tool for bespoke breakdowns of our data. STATS19 R package developed independently of DfT, offering an alternative way to access this data for those familiar with the R language. Latest data Provisional data for the first 6 months of 2024 published 28 November 2024. These are provisional un-validated data. Data included These files provide detailed road safety data about the circumstances of personal injury road collisions in Great Britain from 1979, the types of vehicles involved and the consequential casualties. The statistics relate only to personal injury collisions on public roads that are reported to the police, and subsequently recorded, using the STATS19 collision reporting form. This data contains all the non-sensitive fields that can be made public. Sensitive data fields, for example contributory factors data, can be requested by completing the sensitive data form and contacting the road safety statistics team at roadacc.stats@dft.gov.uk All the data variables are coded rather than containing textual strings. The lookup tables are available in the supporting documents section towards the bottom of the table. Data relating to the casualty and collision severity adjustment to account for changes in police reporting of severity is provided in separate files and can be joined using the appropriate record identifiers. Timing of data release Final annual data is released annually in late September following the publication of the annual reported road casualties Great Britain statistical publication. Individual years data is available for each of the last 5 years, with earlier years available as part of a single download. In addition, un-validated provisional mid-year data (covering January to June) is released at end November, to provide more up to date information Data revisions Except for the severity adjustments, data are not routinely revised those occasionally minor amendments to previous years can be made. Details of recent revisions are available, together with a request for any feedback on the approach to revising the data. The files published here represent the latest data.

The main source of the crash data is owned and maintained by the Virginia Department of Motor Vehicle (DMV). DMV’s Traffic Records Electronic Data System (TREDS) is a state-of-the-art data system maintained by the DMV Highway Safety Office (HSO) that automates and centralizes all crash data in Virginia. Per data sharing use agreement with DMV, VDOT publishes the non-privileged crash data through Virginia Roads data portal. In providing this data, VDOT assumes no responsibility for the accuracy and completeness of the data. In the process of recording and compiling the data, some deletions and/or omissions of data may occur and VDOT is not responsible for any such occurrences. The most recent data contained in this dataset is preliminary and subject to change.

Please be advised that, under Title 23 United State Code – Section 407, this crash information cannot be used in discovery or as evidence in a Federal or State court proceeding or considered for other purposes in any action for damages against VDOT or the State of Virginia arising from any occurrence at the location identified.

All users shall comply with and be subject to all applicable laws and regulations, whether federal or state, in connection with any of the receipt and use of DMV data including, but not limited to, (1) the Federal Drivers Privacy Protection Act (18 U.S.C. § 2721 et seq.), (2) the Government Data Collection and Dissemination Practices Act (Va. Code § 2.2-3800 et seq.), (3) the Virginia Computer Crimes Act (Va. Code § 18.2-152.1 et seq.), (4) the provisions of Va. Code §§ 46.2-208 and 58.1-3, and (5) any successor rules, regulations, or guidelines adopted by DMV with regard to disclosure or dissemination of any information obtained from DMV records or files.

The Motor Vehicle Collisions crash table contains details on the crash event. Each row represents a crash event. The Motor Vehicle Collisions data tables contain information from all police reported motor vehicle collisions in NYC. The police report (MV104-AN) is required to be filled out for collisions where someone is injured or killed, or where there is at least $1000 worth of damage (https://www.nhtsa.gov/sites/nhtsa.dot.gov/files/documents/ny_overlay_mv-104an_rev05_2004.pdf). It should be noted that the data is preliminary and subject to change when the MV-104AN forms are amended based on revised crash details.For the most accurate, up to date statistics on traffic fatalities, please refer to the NYPD Motor Vehicle Collisions page (updated weekly) or Vision Zero View (updated monthly).

Due to success of the CompStat program, NYPD began to ask how to apply the CompStat principles to other problems. Other than homicides, the fatal incidents with which police have the most contact with the public are fatal traffic collisions. Therefore in April 1998, the Department implemented TrafficStat, which uses the CompStat model to work towards improving traffic safety. Police officers complete form MV-104AN for all vehicle collisions. The MV-104AN is a New York State form that has all of the details of a traffic collision. Before implementing Trafficstat, there was no uniform traffic safety data collection procedure for all of the NYPD precincts. Therefore, the Police Department implemented the Traffic Accident Management System (TAMS) in July 1999 in order to collect traffic data in a uniform method across the City. TAMS required the precincts manually enter a few selected MV-104AN fields to collect very basic intersection traffic crash statistics which included the number of accidents, injuries and fatalities. As the years progressed, there grew a need for additional traffic data so that more detailed analyses could be conducted. The Citywide traffic safety initiative, Vision Zero started in the year 2014. Vision Zero further emphasized the need for the collection of more traffic data in order to work towards the Vision Zero goal, which is to eliminate traffic fatalities. Therefore, the Department in March 2016 replaced the TAMS with the new Finest Online Records Management System (FORMS). FORMS enables the police officers to electronically, using a Department cellphone or computer, enter all of the MV-104AN data fields and stores all of the MV-104AN data fields in the Department’s crime data warehouse. Since all of the MV-104AN data fields are now stored for each traffic collision, detailed traffic safety analyses can be conducted as applicable.

Snapshot img

Road Safety Market Size 2024-2028

The road safety market size is forecast to increase by USD 1.81 billion at a CAGR of 9.45% between 2023 and 2028.

The increase in the number of road fatalities and accidents is a key driver of the road safety market. In response, the adoption of connected vehicles and connected road infrastructure is emerging as a key trend. These technologies aim to enhance communication between vehicles and infrastructure, improving safety, reducing accidents, and enabling real-time traffic monitoring, thereby contributing to safer road environments. This trend is expected to shape the future of road safety solutions and reduce fatalities globally.
However, the lack of standardized and uniform technologies poses a challenge to market growth. As the market continues to evolve, it is essential to address these challenges and ensure the implementation of consistent and effective safety measures, including traffic safety products, to reduce the number of road accidents and save lives.

What will be the Size of the Road Safety Market During the Forecast Period?

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The market In the US is a critical sector within the transport and freight system, prioritizing the well-being of essential workers and the public. With the increasing reliance on transportation for goods and services, digital services have emerged as essential tools for traffic mobility management. Road safety products and services, including highway safety systems and traffic management solutions, are in high demand to mitigate road fatalities and ensure national safety council regulations are met.
Safety authorities continue to implement stringent measures to enhance road safety, driving market growth. Transport operators are increasingly adopting advanced technologies to improve their fleet safety and adhere to evolving regulatory requirements.
The market is poised for continued expansion, underpinned by the importance of ensuring the safety and efficiency of the transport sector.

How is this Road Safety Industry segmented and which is the largest segment?

The road safety industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Component

  Solution
  Services


Type

  Enforcement solution
  ALPR or ANPR
  Incident detection and response
  Railroad crossing safety
  School bus stop-arm enforcement
  Back office systems


Geography

  North America

    US


  Europe

    Germany
    UK


  APAC

    China
    Japan


  Middle East and Africa



  South America

By Component Insights

The solution segment is estimated to witness significant growth during the forecast period. The market encompasses solutions designed to ensure the safety of pedestrians, cyclists, and vehicle drivers. In 2023, the solutions segment dominated the market, accounting for the largest share, due to the increasing number of vehicles on the road and the need to protect road users from accidents. Solutions include red light and speed enforcement, automated license plate readers/automatic number plate recognition (ALPR/ANPR), and incident detection and response. These technologies utilize granular data, such as pedestrian and vehicle movement, traffic volume, and image-processing, to enhance safety. Smartphone applications, Geographic Information Systems (GIS), Global Positioning Systems (GPS), drones, and social media are also employed to improve road safety.

Radar, sensors, and virtual-reality simulators are additional tools used to prevent accidents and respond to incidents effectively. The demand for these solutions continues to grow as authorities strive to mitigate the risks associated with increased traffic volume.

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The solution segment was valued at USD 1.74 billion in 2018 and showed a gradual increase during the forecast period.

Regional Analysis

North America is estimated to contribute 29% to the growth of the global market during the forecast period. Technavio's analysts have elaborately explained the regional trends and drivers that shape the market during the forecast period.

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The North American region dominates The market due to the high incidence of road accidents and significant investments in road safety improvements. In the US alone, over 38,000 fatalities and 4.4 million injuries occur annually as a result of road crashes, imposing an estimated economic cost of USD850 billion. These statistics underscore the urgent need for advanced road safety solutions. Information technology plays a pivotal role in this regard, with applications such as Automatic Lic

The program collects data for analysis of traffic safety crashes to identify problems, and evaluate countermeasures leading to reducing injuries and property damage resulting from motor vehicle crashes. The FARS dataset contains descriptions, in standard format, of each fatal crash reported. To qualify for inclusion, a crash must involve a motor vehicle traveling a traffic-way customarily open to the public and resulting in the death of a person (occupant of a vehicle or a non-motorist) within 30 days of the crash. Each crash has more than 100 coded data elements that characterize the crash, the vehicles, and the people involved. The specific data elements may be changed slightly each year to conform to the changing user needs, vehicle characteristics and highway safety emphasis areas. The type of information that FARS, a major application, processes is therefore motor vehicle crash data.

Malta had the lowest rate of road fatalities in the European Union in 2021. That year, 1,000 more people lost their lives on roads in the European Union, up by about five percent between 2020 and 2021.

Road Safety System Market Outlook

The global road safety system market size in 2023 is estimated at approximately USD 4.5 billion, with a projected growth to reach USD 8.6 billion by 2032, reflecting a robust CAGR of 7.2%. This significant growth is largely driven by increasing urbanization, burgeoning vehicle numbers, and an elevated focus on reducing road accidents through advanced safety mechanisms. The market is also propelled by the integration of cutting-edge technologies such as Artificial Intelligence (AI) and the Internet of Things (IoT), which are enhancing the efficiency and effectiveness of road safety systems.

One of the primary growth factors for the road safety system market is the rising number of vehicles on the road, which in turn increases traffic congestion and the likelihood of accidents. Governments worldwide are increasingly adopting stringent regulations and policies to mitigate road accidents, which is fostering the demand for advanced road safety systems. Additionally, the advent of smart city projects across various regions requires sophisticated road safety infrastructures, further driving market growth.

Technology advancements are playing a pivotal role in the market's expansion. The integration of AI and IoT in road safety systems allows for real-time monitoring and data collection, significantly enhancing the efficiency of traffic management and accident prevention measures. AI-powered analytics can predict potential accident hotspots and suggest preventive measures, while IoT enables seamless communication between devices, ensuring timely responses to potential road hazards.

Another growth factor is the increasing investments by governments and private sector companies in road infrastructure improvements. These investments are aimed at modernizing existing road safety systems and developing new ones to cope with the rising traffic demands. Moreover, public awareness campaigns and educational programs on road safety are contributing to the increased adoption of these systems, as individuals and communities become more proactive in ensuring safe road environments.

Regionally, Asia Pacific is expected to witness the highest growth rate in the road safety system market. Rapid urbanization, coupled with significant investments in infrastructure by countries such as China and India, is propelling the demand for advanced road safety solutions. Additionally, the increasing incidence of road accidents in these regions is pushing governments to implement more robust safety measures, thus fostering market growth.

Component Analysis

The road safety system market can be segmented by component into hardware, software, and services. Hardware components include cameras, sensors, radars, and other physical devices that are essential for the functioning of road safety systems. These components are fundamental for capturing real-time data and ensuring the accuracy of safety measures. Continuous innovations in hardware technology are making these devices more efficient and cost-effective, which is likely to spur market growth.

Software components play a crucial role in the processing and analysis of data collected by hardware devices. These include traffic management software, incident detection algorithms, and data analytics tools. The software segment is witnessing rapid advancements with the integration of AI and machine learning, which are enhancing the capabilities of road safety systems. These advancements are enabling more accurate predictions of traffic incidents and better response strategies, thus improving overall road safety.

Services in the road safety system market encompass installation, maintenance, and consulting services. These services are essential for the effective deployment and operation of hardware and software components. The increasing complexity of road safety systems is driving the demand for specialized services to ensure optimal performance. Additionally, ongoing support and maintenance are crucial for the longevity and reliability of these systems, further boosting the service segment.

Integration of these components into a cohesive system is crucial for achieving the desired outcomes in road safety. Advanced integration solutions facilitate seamless communication between different components, ensuring that data collected by hardware devices is accurately processed by software applications. This holistic approach is essential for real-time incident detection, traffic management, and enforcement of road safety regulations.

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Responsibility for the crash

        We determined responsibility levels in the crash using a standardized method adapted from the

Robertson and Drummer crash responsibility tool (11). The adapted method takes into account mitigating factors likely to reduce driver responsibility: road environment, vehicle related factors, traffic conditions, type of accident, traffic rule obedience, and difficulty of the driving task. Each factor scores from 1 (not mitigating, i.e. favorable to driving) to 3 or 4 (mitigating, i.e. not favorable to driving). All six scores are summed to provide a responsibility score (multiplied by 8/6 to be comparable with the eight factor score proposed by Robertson and Drummer). This method has been previously validated in the French context (5,12–15). Indeed, two factors such as “level of fatigue” and “witness observation” are unavailable in French Police records. The higher the score, the lower the responsibility. Responsibility scores are classified into three categories: 8- 12=responsible; 13-15=contributory; >15=not responsible. Drivers displaying any degree of responsibility for the crash were classified as cases (score ≤15); drivers who were judged not responsible (score >15) served as controls. The interviewer was unaware of the responsibility status while interviewing the participants since responsibility scores were computed during the analysis. Risk factors

        Participants were asked to describe their thoughts just before the crash and the question was

coupled with a numeric scale from 0 to 10 that captured the self-estimated level of perturbation. In order to reduce memory bias and halo effect, two opportunities were offered during the interview to report thoughts which were subsequently classified as being related or not to driving. The Mind Wandering State was defined as the report of any thought unrelated to driving. A Disturbing Thought (DT) corresponded to a Mind Wandering State with a perturbation rating higher than 4. Perturbation level was indeed the answer to “How disturbed / distracted was this thought?”. Mind Wandering Trait was built from a scale comprising four items selected based on their clinical significance. Two items are part of the Day Dreaming Frequency Scale (DDFS): Daydreams and fantasies make up X % of the day, and Recalling things from the past, thinking of the future, or imagining unusual kinds of event occupies X% of my day (16). Two items were developed from literature data: In general, when you drive, how often do you happen to think about something else? And In general, when you read, how often do you happen to think about something else. For each question, the related time spent each day was measured from 0 to 100 percent. If the frequency was higher than 50% for at least one item, the patient was defined as in the high category of the boolean MWT variable. The analysis also included well-known risk factors for road crash and potential confounders such as patient characteristics (age, sex, socioeconomic category), alcohol consumption during the 6 hours before the crash and self-reported psychotropic drug use the day before accident. Characteristics of the crash were also reported (location, vehicle type). The variable Distractive Activity was obtained by asking participants about their activities just before the crash (this included use of a mobile phone, listening to radio/television, talking with or listening to a passenger, manipulation of electronic devices, manipulation of objects, grooming, smoking, eating, drinking, reading). Patients were also asked to evaluate their pain at the time of the interview with a numeric scale; A painful participants was defined as with a self-rated pain value strictly superior to 3. Participants were also asked whether they had been distracted by a distracting event that occurred inside or outside the vehicle. Sleep Deprivation was evaluated with The Epworth Sleepiness Scale (ESS) (17). Statistical AnalysisUnivariate analysis was conducted to investigate the link between crash responsibility and risk factors using Student t-test for continuous variable and Chi-square test for categorical variable. Multivariate analysis was then performed with a step by step backwards selection procedure keeping all significant variables (p < 0.05) and all confounders (variation of β > 20%). We then tested interactions between independent variables kept in the final model. Finally, we performed sensitivity analyses to assess the robustness of the results: 1. by stratifying on pain; 2. by changing the cut-off for responsibility score to 14 and 16; 3. by stratifying on the existence of chronic disease.

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.

For each personal injury accident (i.e. an accident on a road open to public traffic, involving at least one vehicle and involving at least one victim requiring treatment), information describing the accident is seized by the police unit (police, gendarmerie, etc.) which intervened at the scene of the accident. These seizures are collected in a sheet entitled ‘Injury Analysis Bulletin’. All these forms constitute the national register of road traffic injuries, known as the ‘BAAC file’, administered by the National Interministerial Observatory for Road Safety (ONISR). The databases, extracted from the BAAC file, list all road traffic injuries occurring during a specific year in mainland France, in the overseas departments (Guadeloupe, French Guiana, Martinique, Réunion and Mayotte since 2012) and in the other overseas territories (Saint-Pierre-et-Miquelon, Saint-Barthélemy, Saint-Martin, Wallis and Futuna, French Polynesia and New Caledonia; available only from 2019 in open data) with a simplified description. This includes information on the location of the accident, as provided, as well as information on the characteristics of the accident and its location, the vehicles involved and their victims. Compared to the aggregated databases 2005-2010 and 2006-2011 currently available on the website www.data.gouv.fr, the databases from 2005 to 2022 are now annual and composed of 4 files (Characteristics – Locations – Vehicles – Users) in csv format. However, those databases conceal certain specific data relating to users and vehicles and their conduct in so far as disclosure of that data would undermine the protection of the privacy of easily identifiable natural persons or reveal the conduct of such persons, whereas disclosure of that conduct could be detrimental to them (CADA opinion – 2 January 2012). Warning: Data on the classification of injured persons hospitalised since 2018 cannot be compared to previous years following changes in the seizure process of the police. The indicator ‘injured hospitalised’ has no longer been labelled by the public statistics authority since 2019. The validity of the statistical operations that can be made from this database depends on the verification methods specific to the field of application of road safety and in particular on a precise knowledge of the definitions relating to each variable used. For any operation, it is important to take note in particular of the structure of the attached BAAC sheet and the guide to using the codification of the road traffic accident analysis bulletin. It should be noted that a number of indicators from this database are labelled by the public statistics authority (Order of 27 November 2019). The list is available at: https://www.onisr.securite-routiere.gouv.fr/statistical tools/labelled indicators

The Vehicle Accident Reconstruction market is experiencing robust growth, driven by increasing road traffic, stricter regulations concerning accident investigation, and advancements in accident reconstruction technologies. The market's value is estimated at $1.5 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 7% between 2025 and 2033. This growth is fueled by several key factors. Firstly, the rising number of road accidents globally necessitates more thorough and accurate accident investigations, creating a strong demand for professional reconstruction services. Secondly, technological advancements such as advanced simulation software, high-resolution imaging, and sophisticated data analysis techniques are enhancing the precision and efficiency of accident reconstruction, leading to more accurate reports and improved safety measures. Furthermore, government regulations mandating detailed accident investigations and the increasing focus on road safety are further stimulating market expansion. Growth is particularly strong in North America and Europe, due to higher vehicle ownership and more established safety regulations, but significant opportunities exist in developing economies in Asia-Pacific and other regions as vehicle ownership grows. However, the market also faces some challenges. High costs associated with specialized equipment, software, and expert witness fees can limit accessibility for smaller firms and individuals. Additionally, the complexity of accident reconstruction often requires specialized skills and expertise, creating a shortage of qualified professionals in the field. The market is segmented by application (e.g., legal proceedings, insurance claims, engineering analysis) and type of service (e.g., on-site investigation, data analysis, expert testimony). Key players in this market include established engineering firms, specialized accident reconstruction companies, and software providers offering simulation and analysis tools. Future growth will be significantly influenced by continued technological innovation, government initiatives promoting road safety, and the growing demand for detailed accident analysis in legal and insurance contexts. The market’s expansion will continue to be driven by the imperative to enhance road safety and improve the accuracy of accident investigations worldwide.

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 Near Accident Traffic 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 Near Accident Traffic 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.

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 Near Accident Traffic 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' Near Accident Traffic Data provides a unique opportunity for data buyers to access high-quality, actionable insights that drive innovation across mobility. By integrating our Near Accident Traffic Data with other datasets, clients can gain a holistic view of transportation dynamics, enhancing their analytical capabilities and decision-making processes.

***The Los Angeles Police Department (LAPD) has transitioned traffic collision reporting to our new Records Management System (RMS) as part of our ongoing efforts to modernize data collection and comply with the FBI’s National Incident-Based Reporting System (NIBRS). This transition will improve the accuracy and detail of reported traffic-related incidents.

During this process, there will be a delay in the availability of new traffic collision datasets while they are being developed for the new system. In the meantime, users will continue to see only historical data from the retired system. We appreciate your patience as we complete this transition. ***

This dataset reflects traffic collision incidents in the City of Los Angeles dating back to 2010. This data is transcribed from original traffic reports that are typed on paper and therefore there may be some inaccuracies within the data. Some location fields with missing data are noted as (0°, 0°). Address fields are only provided to the nearest hundred block in order to maintain privacy. This data is as accurate as the data in the database. Please note questions or concerns in the comments.

Human body models (HBMs) have the potential to provide significant insights into the pediatric response to impact. This study describes a scalable/posable approach to perform child accident reconstructions using the Position and Personalize Advanced Human Body Models for Injury Prediction (PIPER) scalable child HBM of different ages and in different positions obtained by the PIPER tool. Overall, the PIPER scalable child HBM managed reasonably well to predict the injury severity and location of the children involved in real-life crash scenarios documented in the medical records. The developed methodology and workflow is essential for future work to determine child injury tolerances based on the full Child Advanced Safety Project for European Roads (CASPER) accident reconstruction database. With the workflow presented in this study, the open-source PIPER scalable HBM combined with the PIPER tool is also foreseen to have implications for improved safety designs for a better protection of children in traffic accidents.

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 Road Safety Signs market plays a crucial role in enhancing traffic safety and promoting smooth vehicle flow on roads. These signs serve as vital communication tools, providing essential information to drivers and pedestrians about regulations, warnings, and guidance. Their usage spans a wide array of application

Road Safety Product Market Outlook

The global road safety product market size was valued at approximately $4.5 billion in 2023 and is projected to reach around $8.2 billion by 2032, growing at a CAGR of 6.8% during the forecast period. This growth is primarily driven by increasing concerns over road safety, urbanization, and government regulations mandating the use of road safety products.

One major growth factor for the road safety product market is the rising incidence of road accidents worldwide. Governments are increasingly focusing on implementing stringent regulations and policies to enhance road safety, which has accelerated the demand for road safety products. For instance, many countries have introduced new standards and guidelines for the installation and usage of road safety products like traffic cones, barricades, and speed bumps, to mitigate accidents and ensure smooth traffic flow.

Another significant factor contributing to market growth is the rapid urbanization and infrastructural development occurring across various regions. With the expansion of urban areas and the construction of new highways, roads, and residential areas, there is an urgent need for effective road safety measures. This has led to a rise in demand for various road safety products such as signage, safety apparel, and speed bumps, which are essential for maintaining order and safety on newly constructed roads.

Technological advancements in road safety products are also playing a crucial role in market growth. The integration of smart technologies such as IoT (Internet of Things) in road safety products is gaining traction. These advanced products provide real-time data to monitoring systems, enabling quick response to road safety issues. For example, smart traffic cones equipped with sensors can alert authorities about their displacement, ensuring timely action and minimizing the risk of accidents.

In terms of regional outlook, Asia Pacific is expected to dominate the road safety product market during the forecast period. The region's rapid economic development, coupled with increasing investments in infrastructure projects, is driving the demand for road safety products. Countries like China and India are investing heavily in road construction and safety measures to accommodate their growing urban populations. Moreover, government initiatives aimed at reducing road fatalities and promoting road safety further contribute to market growth in this region.

Traffic Cone Holders are an essential component in the effective deployment and management of traffic cones, particularly in areas with high traffic volumes or construction zones. These holders ensure that traffic cones are securely stored and easily accessible, facilitating quick deployment when needed. By organizing traffic cones in a systematic manner, traffic cone holders help in maintaining order and efficiency on roads, especially during peak hours or emergency situations. The use of traffic cone holders also minimizes the risk of cones being misplaced or damaged, which can lead to potential safety hazards. As urban areas continue to expand and infrastructure projects increase, the demand for reliable traffic management solutions, including traffic cone holders, is expected to rise. This trend underscores the importance of investing in durable and efficient traffic cone holders to enhance road safety and streamline traffic management processes.

Product Type Analysis

The market for road safety products can be segmented by product type, which includes traffic cones, barricades, speed bumps, signage, safety apparel, and others. Traffic cones account for a significant share of the market owing to their widespread usage in directing traffic and marking hazardous areas. They are essential tools in both urban and rural settings, playing a crucial role in ensuring road safety during construction or maintenance activities. The demand for traffic cones is further driven by their cost-effectiveness and ease of deployment.

Barricades are another vital segment within the road safety product market. These are used extensively to segregate work zones, prevent vehicular access to certain areas, and guide traffic. With increasing urbanization and road construction projects, the demand for durable and easy-to-install barricades has surged. Many modern barricades come with reflective materials and lights to enhance visibility during night-time or low-light conditions, further ensuring road safety.