The number of road traffic fatalities per one million inhabitants in the United States was forecast to continuously increase between 2024 and 2029 by in total 18.5 deaths (+13.81 percent). After the tenth consecutive increasing year, the number is estimated to reach 152.46 deaths and therefore a new peak in 2029. Depicted here are the estimated number of deaths which occured in relation to road traffic. They are set in relation to the population size and depicted as deaths per 100,000 inhabitants.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of road traffic fatalities per one million inhabitants in countries like Mexico and Canada.

An overall decline in the number of road deaths in Russia was observed from 2007, when the highest volume of road fatalities was recorded countrywide in the given timeframe. The temporary growth of five percent between 2010 and 2012 was followed by a significant drop in the number of incidences over the following years. Namely, the count of road deaths in the country reduced nearly twofold by 2020 relative to the 2012 figures, measuring at 16,152 cases in the last observed period.

Mortality rate and causes of death in Russia   

Mortality rate has been gradually declining since 2000 in Russia, yet it remained greater than the rates measured prior to the 1990s. Even though road accidents were not the major cause of fatalities in the country, roughly 183 thousand residents were reported being injured in road traffic incidents in 2020. The coronavirus (COVID-19) outbreak at the beginning of 2020 ranked Russia as one of the most affected worldwide. The coronavirus-related mortality rate, nonetheless, was measured significantly lower than in most countries with a somewhat similar number of disease cases.

General outlook   

Despite the fact that road fatalities have been in decline in Russia, the country was listed second by road mortality worldwide after Georgia in 2017. The most significant decline in road death rates among the European countries was recorded in Norway in the same year.

About 228,200 Americans had a license to operate a motor vehicle in the United States in 2020. That year, an estimated 36,680 people died on U.S. roads. Traffic-related fatalities per 100,000 licensed drivers stood at 17.01 in 2020.

Road safety rankings
The United States has among the highest rates of road fatalities per population worldwide. Possible contributing factors to deaths on the road can include speeding, not wearing a seatbelt, driving while under the influence of drugs or alcohol, and driving while fatigued. Traffic fatalities caused by speeding in the United States have declined since 2008, with less than 10,000 deaths recorded annually over recent years.

Automation for the nation
94 percent of severe automobile crashes are due to human error — but driving safety is taken much more seriously today than in the past, with roughly 90 percent of U.S. drivers wearing their seatbelts while driving in 2020. Over recent years, car manufacturers and developers have striven to reduce car crashes even further with partially and fully automated safety features such as forward collision warnings, lane departure warnings, rearview video systems, and automatic emergency braking. Self-driving vehicles are also set to take to the roads in the future, with car brands such as Toyota, Ford, and GM registering over 350 autonomous driving patents respectively in the United States.

CL: Road Fatalities: Per One Million Road Motor Vehicles data was reported at 3.131 Ratio in 2023. This records a decrease from the previous number of 3.496 Ratio for 2022. CL: Road Fatalities: Per One Million Road Motor Vehicles data is updated yearly, averaging 6.036 Ratio from Dec 1998 (Median) to 2023, with 26 observations. The data reached an all-time high of 13.149 Ratio in 1998 and a record low of 3.131 Ratio in 2023. CL: Road Fatalities: Per One Million Road Motor Vehicles data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Chile – Table CL.OECD.ITF: Road Traffic and Road Accident Fatalities: OECD Member: Annual. [COVERAGE] ROAD FATALITIES A road fatality is any person killed immediately or dying within 30 days as a result of an injury accident, excluding suicides. A killed person is excluded if the competent authority declares the cause of death to be suicide, i.e. a deliberate act to injure oneself resulting in death. For countries that do not apply the threshold of 30 days, conversion coefficients are estimated so that comparison on the basis of the 30-day definition can be made. VEHICLES A road motor vehicle is a road vehicle fitted with an engine whence it derives its sole means of propulsion, which is normally used for carrying persons or goods or for drawing, on the road, vehicles used for the carriage of persons or goods. [COVERAGE] ROAD FATALITIES Data do not include suicides. Since 2019, data include also killed after a road crash involving a train. [STAT_CONC_DEF] VEHICLES The stock of road motor vehicles is the number of road motor vehicles registered at a given date in a country and licenced to use roads open to public traffic. This includes road vehicles exempted from annual taxes or licence fee; it also includes imported second-hand vehicles and other road vehicles according to national practices. It should not include military vehicles. [STAT_CONC_DEF] ROAD FATALITIES Until 2018, data refer to fatalities within 24 hours after the crash occurred. An adjustment factor of 1.3 was used to comply with the 30-days definition, as suggested by the World Health Organisation (WHO). Since 2019, data refer to fatalities within 48 hours after the crash occurred. An adjustment factor of 1.2 was used to comply with the 30-days definition, as suggested by the World Health Organisation (WHO).

CL: Road Fatalities: Per One Million Vehicle-km data was reported at 42.928 Ratio in 2021. This records a decrease from the previous number of 47.360 Ratio for 2020. CL: Road Fatalities: Per One Million Vehicle-km data is updated yearly, averaging 45.296 Ratio from Dec 2018 (Median) to 2021, with 4 observations. The data reached an all-time high of 58.716 Ratio in 2018 and a record low of 42.928 Ratio in 2021. CL: Road Fatalities: Per One Million Vehicle-km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Chile – Table CL.OECD.ITF: Road Traffic and Road Accident Fatalities: OECD Member: Annual. [COVERAGE] ROAD FATALITIES A road fatality is any person killed immediately or dying within 30 days as a result of an injury accident, excluding suicides. A killed person is excluded if the competent authority declares the cause of death to be suicide, i.e. a deliberate act to injure oneself resulting in death. For countries that do not apply the threshold of 30 days, conversion coefficients are estimated so that comparison on the basis of the 30-day definition can be made. ROAD TRAFFIC Road traffic is any movement of a road vehicle on a given road network. When a road vehicle is being carried on another vehicle, only the movement of the carrying (active mode) is considered. [COVERAGE] ROAD FATALITIES Data do not include suicides. Since 2019, data include also killed after a road crash involving a train. ROAD TRAFFIC Data refer only to interurban road motor vehicle traffic. [STAT_CONC_DEF] ROAD FATALITIES Until 2018, data refer to fatalities within 24 hours after the crash occurred. An adjustment factor of 1.3 was used to comply with the 30-days definition, as suggested by the World Health Organisation (WHO). Since 2019, data refer to fatalities within 48 hours after the crash occurred. An adjustment factor of 1.2 was used to comply with the 30-days definition, as suggested by the World Health Organisation (WHO). ROAD TRAFFIC In 2019, there has been a change in the methodology.

Association between demographic factors and car accident death rate with fear of child’s own car accident across seven countries in Europe.

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.

We present the GLOBAL ROADKILL DATA, the largest worldwide compilation of roadkill data on terrestrial vertebrates. We outline the workflow (Fig. 1) to illustrate the sequential steps of the study, in which we merged local-scale survey datasets and opportunistic records into a unified roadkill large dataset comprising 208,570 roadkill records. These records include 2283 species and subspecies from 54 countries across six continents, ranging from 1971 to 2024.Large roadkill datasets offer the advantage ofpreventing the collection of redundant data and are valuable resources for both local and macro-scale analyses regarding roadkill rates, road and landscape features associated with roadkill risk, species more vulnerable to road traffic, and populations at risk due to additional mortality. The standardization of data - such as scientific names, projection coordinates, and units - in a user-friendly format, makes themreadily accessible to a broader scientific and non-scientific community, including NGOs, consultants, public administration officials, and road managers. The open-access approach promotes collaboration among researchers and road practitioners, facilitating the replication of studies, validation of findings, and expansion of previous work. Moreover, researchers can utilize suchdatasets to develop new hypotheses, conduct meta-analyses, address pressing challenges more efficiently and strengthen the robustness of road ecology research. Ensuring widespreadaccess to roadkill data fosters a more diverse and inclusive research community. This not only grants researchers in emerging economies with more data for analysis, but also cultivates a diverse array of perspectives and insightspromoting the advance of infrastructure ecology.MethodsInformation sources: A core team from different continents performed a systematic literature search in Web of Science and Google Scholar for published peer-reviewed papers and dissertations. It was searched for the following terms: “roadkill* OR “road-kill” OR “road mortality” AND (country) in English, Portuguese, Spanish, French and/or Mandarin. This initiative was also disseminated to the mailing lists associated with transport infrastructure: The CCSG Transport Working Group (WTG), Infrastructure & Ecology Network Europe (IENE) and Latin American & Caribbean Transport Working Group (LACTWG) (Fig. 1). The core team identified 750 scientific papers and dissertations with information on roadkill and contacted the first authors of the publications to request georeferenced locations of roadkill andofferco-authorship to this data paper. Of the 824 authors contacted, 145agreed to sharegeoreferenced roadkill locations, often involving additional colleagues who contributed to data collection. Since our main goal was to provide open access to data that had never been shared in this format before, data from citizen science projects (e.g., globalroakill.net) that are already available were not included.Data compilation: A total of 423 co-authors compiled the following information: continent, country, latitude and longitude in WGS 84 decimal degrees of the roadkill, coordinates uncertainty, class, order, family, scientific name of the roadkill, vernacular name, IUCN status, number of roadkill, year, month, and day of the record, identification of the road, type of road, survey type, references, and observers that recorded the roadkill (Supplementary Information Table S1 - description of the fields and Table S2 - reference list). When roadkill data were derived from systematic surveys, the dataset included additional information on road length that was surveyed, latitude and longitude of the road (initial and final part of the road segment), survey period, start year of the survey, final year of the survey, 1st month of the year surveyed, last month of the year surveyed, and frequency of the survey. We consolidated 142 valid datasets into a single dataset. We complemented this data with OccurenceID (a UUID generated using Java code), basisOfRecord, countryCode, locality using OpenStreetMap’s API (https://www.openstreetmap.org), geodeticDatum, verbatimScientificName, Kingdom, phylum, genus, specificEpithet, infraspecificEpithet, acceptedNameUsage, scientific name authorship, matchType, taxonRank using Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters) and link of the associatedReference (URL).Data standardization - We conducted a clustering analysis on all text fields to identify similar entries with minor variations, such as typos, and corrected them using OpenRefine (http://openrefine.org). Wealsostandardized all date values using OpenRefine. Coordinate uncertainties listed as 0 m were adjusted to either 30m or 100m, depending on whether they were recorded after or before 2000, respectively, following the recommendation in the Darwin Core Reference Guide (https://dwc.tdwg.org/terms/#dwc:coordinateUncertaintyInMeters).Taxonomy - We cross-referenced all species names with the Global Biodiversity Information Facility (GBIF) Backbone Taxonomy using Java and GBIF’s API (https://doi.org/10.15468/39omei). This process aimed to rectify classification errors, include additional fields such as Kingdom, Phylum, and scientific authorship, and gather comprehensive taxonomic information to address any gap withinthe datasets. For species not automatically matched (matchType - Table S1), we manually searched for correct synonyms when available.Species conservation status - Using the species names, we retrieved their conservation status and also vernacular names by cross-referencing with the database downloaded from the IUCNRed List of Threatened Species (https://www.iucnredlist.org). Species without a match were categorized as "Not Evaluated".Data RecordsGLOBAL ROADKILL DATA is available at Figshare27 https://doi.org/10.6084/m9.figshare.25714233. The dataset incorporates opportunistic (collected incidentally without data collection efforts) and systematic data (collected through planned, structured, and controlled methods designed to ensure consistency and reliability). In total, it comprises 208,570 roadkill records across 177,428 different locations(Fig. 2). Data were collected from the road network of 54 countries from 6 continents: Europe (n = 19), Asia (n = 16), South America (n=7), North America (n = 4), Africa (n = 6) and Oceania (n = 2).(Figure 2 goes here)All data are georeferenced in WGS84 decimals with maximum uncertainty of 5000 m. Approximately 92% of records have a location uncertainty of 30 m or less, with only 1138 records having location uncertainties ranging from 1000 to 5000 m. Mammals have the highest number of roadkill records (61%), followed by amphibians (21%), reptiles (10%) and birds (8%). The species with the highest number of records were roe deer (Capreolus capreolus, n = 44,268), pool frog (Pelophylax lessonae, n = 11,999) and European fallow deer (Dama dama, n = 7,426).We collected information on 126 threatened species with a total of 4570 records. Among the threatened species, the giant anteater (Myrmecophaga tridactyla, VULNERABLE) has the highest number of records n = 1199), followed by the common fire salamander (Salamandra salamandra, VULNERABLE, n=1043), and European rabbit (Oryctolagus cuniculus, ENDANGERED, n = 440). Records ranged from 1971 and 2024, comprising 72% of the roadkill recorded since 2013. Over 46% of the records were obtained from systematic surveys, with road length and survey period averaging, respectively, 66 km (min-max: 0.09-855 km) and 780 days (1-25,720 days).Technical ValidationWe employed the OpenStreetMap API through Java todetect location inaccuracies, andvalidate whether the geographic coordinates aligned with the specified country. We calculated the distance of each occurrence to the nearest road using the GRIP global roads database28, ensuring that all records were within the defined coordinate uncertainty. We verified if the survey duration matched the provided initial and final survey dates. We calculated the distance between the provided initial and final road coordinates and cross-checked it with the given road length. We identified and merged duplicate entries within the same dataset (same location, species, and date), aggregating the number of roadkills for each occurrence.Usage NotesThe GLOBAL ROADKILL DATA is a compilation of roadkill records and was designed to serve as a valuable resource for a wide range of analyses. Nevertheless, to prevent the generation of meaningless results, users should be aware of the followinglimitations:- Geographic representation – There is an evident bias in the distribution of records. Data originatedpredominantly from Europe (60% of records), South America (22%), and North America (12%). Conversely, there is a notable lack of records from Asia (5%), Oceania (1%) and Africa (0.3%). This dataset represents 36% of the initial contacts that provided geo-referenced records, which may not necessarily correspond to locations where high-impact roads are present.- Location accuracy - Insufficient location accuracy was observed for 1% of the data (ranging from 1000 to 5000 m), that was associated with various factors, such as survey methods, recording practices, or timing of the survey.- Sampling effort - This dataset comprised both opportunistic data and records from systematic surveys, with a high variability in survey duration and frequency. As a result, the use of both opportunistic and systematic surveys may affect the relative abundance of roadkill making it hard to make sound comparisons among species or areas.- Detectability and carcass removal bias - Although several studies had a high frequency of road surveys,the duration of carcass persistence on roads may vary with species size and environmental conditions, affecting detectability. Accordingly, several approaches account for survey frequency and target speciesto estimate more

Antigua and Barbuda: Traffic accident deaths per 100,000 people: The latest value from 2019 is 0 deaths per 100,000 people, unchanged from 0 deaths per 100,000 people in 2018. In comparison, the world average is 17.05 deaths per 100,000 people, based on data from 180 countries. Historically, the average for Antigua and Barbuda from 2000 to 2019 is 4.09 deaths per 100,000 people. The minimum value, 0 deaths per 100,000 people, was reached in 2005 while the maximum of 13 deaths per 100,000 people was recorded in 2001.

The number of road accidents per one million inhabitants in the United States was forecast to continuously decrease between 2024 and 2029 by in total 2,490.4 accidents (-14.99 percent). After the eighth consecutive decreasing year, the number is estimated to reach 14,118.78 accidents and therefore a new minimum in 2029. Depicted here are the estimated number of accidents which occured in relation to road traffic. They are set in relation to the population size and depicted as accidents per one million inhabitants.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of road accidents per one million inhabitants in countries like Mexico and Canada.

BackgroundDespite the considerable effort made during the last decades, emerging countries are still among the highest road safety concerns because they still account for most of the deaths caused by traffic crashes. Various studies suggest that one of the factors involved in this negative outcome could be road safety. However, this issue remains pending to be addressed in most emerging countries, including the Dominican Republic.AimThis study aimed to assess the beliefs and perceptions of Dominicans regarding some key road risky-related issues and to discuss them in the light of objective data.MethodsFor this cross-sectional study, the responses by a full sample of 1,260 Dominicans (50.1% men, 49.9% women) with a mean age of 39.4 years participating in a set of surveys conducted across the country, were used.ResultsAlthough Dominicans (especially women) seem to attribute high importance to road crashes, there is a low perceived likelihood of getting involved in a traffic crash. As for subjective versus objective data comparisons, perceived crash features and objective crash report data considerably match. However, the numbers largely differ in terms of crash frequency and importance and relevance given to road crashes, and their consequences. Further, perceptions of traffic violations and lack of law enforcement were pertinent predictors of the degree of relevance attributed to traffic crashes.ConclusionsOverall, the results of this study suggest that, despite a relative awareness of their actual traffic crash features, Dominicans systematically underestimate the causes, frequency, and consequences of these crashes, including yearly fatality rates. These outcomes suggest the need to strengthen road safety awareness and beliefs in further road safety actions and policymaking in the region.

In 2022, around 11 people per 1 million inhabitants died as a result of road accidents in Chile. New Zealand, with around seven fatalities per 1 million inhabitants, was second in the ranking.

ObjectivesThe 2013 World Health Organization Status Report on Road Safety estimated that approximately 1.24 million deaths occur annually due to road traffic crashes with most of the burden falling on low- and middle-income countries. The objective of this research is to study the prevalence of road traffic crashes in Mekelle, Tigray, Northern Ethiopia and to identify risk factors with the ultimate goal of informing prevention activities and policies.MethodsThis study used a cross-sectional design to measure the prevalence and factors associated with road traffic crashes among 4-wheeled minibus (n = 130) and 3-wheeled Bajaj (n = 582) taxi drivers in Mekelle, Ethiopia. Bivariate and multivariate logistic regression were used to evaluate the association between risk factors and drivers’ involvement in a road traffic crash within the 3 years prior to the survey.FindingsAmong the 712 taxi drivers, 26.4% (n = 188) of them reported involvement in a road traffic crash within the past 3 years. Drivers who listened to mass media had decreased likelihood of road traffic crash involvement (AOR = 0.51, 0.33–0.78), while speedy driving (AOR = 4.57, 3.05–7.44), receipt of a prior traffic punishment (AOR = 4.57, 2.67–7.85), and driving a mechanically faulty taxi (AOR = 4.91, 2.81–8.61) were strongly associated with road traffic crash involvement. Receiving mobile phone calls while driving (AOR = 1.91, 1.24–2.92) and history of alcohol use (AOR = 1.51, 1.00–2.28) were also associated with higher odds of road traffic crash involvement.ConclusionThe results of this study show that taxi drivers in Mekelle habitually place themselves at increased risk of road traffic crashes by violating traffic laws, especially related to speedy driving, mobile phone use, and taxi maintenance. This research can be used to support re-evaluation of the type, severity, and enforcement of traffic violation penalties.

Typical crash-related beliefs (hypothesized crash configurations) in the Dominican Republic.

This statistic displays the number of road traffic fatalities in European countries per 100,000 inhabitants in 2017. According to the data, Norway had the lowest number of road fatalities in 2017 with only 2 fatalities per 100,000 inhabitants, while Serbia had the worst road safety with 8.2 road deaths per 100,000 inhabitants in 2017.

A presentation about perspectives on road safety in southeast Asia. The presenter provides background on the Decade of Action for Road Safety, and discusses crash statistics and data systems globally. They then introduce a WHO report on the status of road safety in the southeast Asia region. Data are provided for countries in the region on road deaths, registered vehicles, speed limit enforcement, drink-driving law enforcement, and seatbelt and helmet use. The presenter also discusses relevant policies.

In Newark, a disturbing trend has emerged with fatal car accidents occurring in close proximity to hospitals. This ironic and tragic phenomenon highlights the stark reality of road safety challenges in urban areas. Despite the presence of medical facilities designed to save lives, the surrounding roads witness devastating accidents claiming them. Such occurrences not only underscore the urgency of addressing road safety measures but also raise questions about the effectiveness of emergency response systems. The juxtaposition of life-saving institutions and deadly accidents serves as a poignant reminder of the need for comprehensive efforts to enhance road safety, not only in Newark but across urban landscapes worldwide.