In 2021, ** percent of truck drivers in Europe were between 25 and 55 years old. The age group containing citizens aged above 50 years constituted ** percent of truck drivers in Europe, whereas the youngest age group, below ** year old, constituted ***** percent of the total truck-driver population.

The graph illustrates the number of truck drivers in the United States from 1997 to 2024. The x-axis represents the years, ranging from 1997 to 2024, while the y-axis denotes the number of truck drivers, spanning from 2,247,000 in 2010 to 3,064,890 in 2023. Throughout this period, the number of truck drivers generally increased, starting at 264,258 in 1997 and reaching its highest point in 2024. Notable fluctuations include significant decreases in 1998 and 2002, followed by steady growth in subsequent years. Overall, the data exhibits an upward trend in the number of truck drivers over the 27-year span. This information is presented in a line graph format, effectively highlighting the annual changes and long-term growth in truck driver numbers in the United States.

In 2021, ** percent of truck drivers in the United States were between 25 and 55 years old. The age group containing citizens older than ** constituted ** percent of truck drivers in the United States, whereas the youngest age group, beneath 25 years old, constituted only *** percent.

In 2021, ** percent of truck drivers in the Mexico were between 25 and 55 years old. The age group containing citizens aged above 50 years constituted ** percent of truck drivers in the Mexico, whereas the youngest age group, beneath ** year old, constituted ** percent of the total truck-driver population. In contrast, the European truck drivers were older, with some ** percent of them being above 55 years old.

Abstract This study aimed to identify the risk factors of Common Mental Disorders (CMD) using a sample of 565 Brazilian truck drivers. For data capture were applied the Self-Reporting Questionnaire (SRQ-20), Scale subscale of Psychosocial risks and questionnaire with socio-demographic, working and occupational stressors. The results obtained by multivariate binary logistic regression analysis, have explained the 39.9% of variation on CMD. The occupational stressor working hours is the predictor variable with highest impact, may implying in an increase of 5.41 times more chance of the trucker to present CMD. The results indicate actions by management level as work organization and public authorities with regard to the external work conditions.

This statistic depicts the total number of truck drivers in Canada's trucking industry from 2000 to 2021. In 2021, there were ******* truck drivers in Canada, an increase from ******* drivers in 2020.

Driverless Truck Market Outlook

According to our latest research, the global driverless truck market size reached USD 2.8 billion in 2024, reflecting a robust surge in adoption and deployment across key industries. The market is projected to register an impressive CAGR of 22.6% from 2025 to 2033, with the market size anticipated to reach USD 26.2 billion by 2033. This accelerated growth is primarily driven by advances in autonomous driving technology, increasing emphasis on operational efficiency, and a mounting need to address the global shortage of skilled truck drivers. As organizations worldwide seek to optimize logistics and reduce costs, the driverless truck industry is positioned for transformative expansion over the coming decade.

One of the primary growth drivers for the driverless truck market is the rapid evolution in artificial intelligence, sensor technology, and connectivity solutions. The integration of high-resolution LiDAR, radar, and advanced camera systems with sophisticated software algorithms has enabled trucks to operate autonomously with enhanced safety and precision. These technological advancements have not only improved the reliability of driverless trucks but also reduced the total cost of ownership for fleet operators. As regulatory frameworks mature and testing protocols become more standardized, the deployment of autonomous trucks is expected to accelerate, particularly in controlled environments such as logistics hubs, mining sites, and construction zones.

Another significant factor fueling market expansion is the growing demand for efficient logistics and freight transportation. E-commerce growth, globalized supply chains, and just-in-time delivery models are placing unprecedented pressure on logistics companies to enhance productivity and reduce turnaround times. Driverless trucks offer a compelling solution by enabling 24/7 operations, minimizing human error, and reducing labor costs. Additionally, these vehicles can optimize routes in real-time, leading to substantial fuel savings and lower emissions. The synergy between autonomous technology and digital logistics platforms is poised to revolutionize the freight industry, making it more agile, responsive, and sustainable.

The persistent shortage of qualified truck drivers across major economies, particularly in North America and Europe, is another catalyst for the adoption of driverless trucks. The trucking industry has grappled with high turnover rates, aging workforce demographics, and stringent working hour regulations, all of which have constrained capacity and increased operational costs. Autonomous trucks present a viable alternative, capable of filling the labor gap while enhancing safety and compliance. Governments and industry stakeholders are increasingly investing in pilot programs and public-private partnerships to accelerate the transition toward autonomous freight transport, further supporting market growth.

Regionally, North America remains at the forefront of the driverless truck market, accounting for the largest share in 2024, thanks to a robust innovation ecosystem, supportive regulatory environment, and significant investments by leading technology companies. Europe follows closely, driven by stringent emission norms and a strong focus on digital transformation in transportation. Meanwhile, the Asia Pacific region is emerging as a high-growth market, propelled by rapid industrialization, expanding logistics networks, and government initiatives to promote smart mobility solutions. Latin America and the Middle East & Africa are also witnessing gradual adoption, albeit at a slower pace, due to infrastructure and regulatory challenges. Overall, the global landscape is characterized by dynamic growth trajectories, with substantial opportunities for stakeholders across all regions.

Vehicle Type Analysis

The driverless truck market is segmented by vehicle type into light-duty trucks, medium-duty trucks, and heavy-duty trucks, each playing a distinct role in the autonomous transportation ecosystem. Light-duty trucks, traditionally used for last-mile deliveries and urban logistics, have rapidly adopted autonomous technologies due to their frequent operation in controlled, predictable environments. The proliferation of e-commerce and the surge in demand for efficient, contactless delivery solutions have accelerated the deployment of driverless light-duty trucks in metropolitan areas. These vehicles are equipped with advanced navigation systems and real-time data analytics,

Graph and download economic data for All Employees, Truck Transportation (CEU4348400001) from Jan 1990 to Sep 2025 about warehousing, trucks, transportation, establishment survey, employment, and USA.

Demographic and clinical characteristics of the participants (n = 949).

Kaggle Dataset: Synthetic Logistics Operations Database (2022-2024)

About this Dataset

What's Inside

A complete operational database from a fictional Class 8 trucking company spanning three years. This isn't scraped web data or simplified tutorial content—it's a realistic simulation built from 12 years of real-world logistics experience, designed specifically for analysts transitioning into supply chain and transportation domains.

The dataset contains 85,000+ records across 14 interconnected tables covering everything from driver assignments and fuel purchases to maintenance schedules and delivery performance. Each table maintains proper foreign key relationships, making this ideal for practicing complex SQL queries, building data pipelines, or developing operational dashboards.

Who This Is For

SQL Learners: Master window functions, CTEs, and multi-table JOINs using realistic business scenarios rather than contrived examples.

Data Analysts: Build portfolio projects that demonstrate understanding of operational metrics: cost-per-mile analysis, fleet utilization optimization, driver performance scorecards.

Aspiring Supply Chain Analysts: Work with authentic logistics data patterns—seasonal freight volumes, equipment utilization rates, route profitability calculations—without NDA restrictions.

Data Science Students: Develop predictive models for maintenance scheduling, driver retention, or route optimization using time-series data with actual business context.

Career Changers: If you're moving from operations into analytics (like the dataset creator), this provides a bridge—your domain knowledge becomes a competitive advantage rather than a gap to explain.

Why This Dataset Exists

Most logistics datasets are either proprietary (unavailable) or overly simplified (unrealistic). This fills the gap: operational complexity without confidentiality concerns. The data reflects real industry patterns:

• Fuel prices track the 2022 diesel spike and 2023-2024 decline
• Driver turnover sits at 15% annually (industry standard)
• Equipment utilization averages 65% (typical for dry van operations)
• On-time delivery performance ranges 85-95% (realistic service levels)
• Maintenance intervals follow Class 8 PM schedules

Dataset Structure

Core Entities (Reference Tables): - Drivers (150 records) - Demographics, employment history, CDL info - Trucks (120 records) - Fleet specs, acquisition dates, status - Trailers (180 records) - Equipment types, current assignments - Customers (200 records) - Shipper accounts, contract terms, revenue potential - Facilities (50 records) - Terminals and warehouses with geocoordinates - Routes (60+ records) - City pairs with distances and rate structures

Operational Transactions: - Loads (57,000+ records) - Shipment details, revenue, booking type - Trips (57,000+ records) - Driver-truck assignments, actual performance - Fuel Purchases (131,000+ records) - Transaction-level data with pricing - Maintenance Records (6,500+ records) - Service history, costs, downtime - Delivery Events (114,000+ records) - Pickup/delivery timestamps, detention - Safety Incidents (114 records) - Accidents, violations, claims

Aggregated Analytics: - Driver Monthly Metrics (5,400+ records) - Performance summaries - Truck Utilization Metrics (3,800+ records) - Equipment efficiency

Key Features

Temporal Coverage: January 2022 through December 2024 (3 years)

Geographic Scope: National operations across 25+ major US cities

Realistic Patterns: - Seasonal freight fluctuations (Q4 peaks) - Historical fuel price accuracy - Equipment lifecycle modeling - Driver retention dynamics - Service level variations

Data Quality: - Complete foreign key integrity - No orphaned records - Intentional 2% null rate in driver/truck assignments (reflects reality) - All timestamps properly sequenced - Financial calculations verified

Use Case Examples

Business Intelligence: Create executive dashboards showing revenue per truck, cost per mile, driver efficiency rankings, maintenance spend by equipment age, customer concentration risk.

Predictive Analytics: Build models forecasting equipment failures based on maintenance history, predict driver turnover using performance metrics, estimate route profitability for new lanes.

Operations Optimization: Analyze route efficiency, identify underutilized assets, optimize maintenance scheduling, calculate ideal fleet size, evaluate driver-to-truck ratios.

SQL Mastery: Practice window functions for running totals and rankings, write complex JOINs across 6+ tables, implement CTEs for hierarchical queries, perform cohort analysis on driver retention.

Sample Questions to Explore

1. Which routes generate the highest profit margin after fuel costs?
2. How does driver tenure correlate with fuel ef...

Demographic characteristics, sleep habits, and symptoms of insomnia.

Background:professional drivers can be subject to occupational musculoskeletal problems.Objective:to estimate the prevalence of musculoskeletal spine pain among truck drivers and identify the associated factors in a 12 month period prior to the research.Method:a cross-sectional study conducted in 2007 involving all 460 male truck drivers from a freight company. Information on sociodemographic, occupational and health factors was collected through a questionnaire. Univariate and multivariate logistic regression analysis were carried out to determine the association between musculoskeletal spine pain and the investigated factors.Results:prevalence of musculoskeletal pain was 53.5%, the highest being related to the spine column (38.5%) and the lumbar spine (28%). Factors associated with spine pain were: bad sleeping, tension resulting from fear of being attacked, killed, becoming ill or getting involved in accidents, as well as stress, tension and fatigue caused by discomfort.Conclusion:high prevalence of lumbar spine pain in the studied population was associated with external stressors, including fear of accidents and robberies, as well as those directly related to work organization such as fatigue resulting from lack of pauses and restrictions on sleeping hours, which leads to poor sleep quality.

The trucking industry has undergone a significant structural transformation over the past five years, marked by e-commerce-driven demand and technology-enabled efficiency gains. Small and medium-sized enterprises are increasingly relying on less-than-truckload services to optimize shipment economics by consolidating parcels across multiple shippers. Meanwhile, carriers have deployed advanced transportation management systems and real-time optimization software to accommodate fluctuating demand and meet accelerating delivery expectations. The sector has simultaneously faced persistent labor supply constraints. Aging workforce demographics and regulatory limitations on driving hours are forcing carriers to compete for driver talent through wage increases and benefit enhancements that have pressured operating profit. Some industry consolidation (Yellow Corporation's 2023 bankruptcy, UPS's divestment of its LTL business and significant acquisitions by XPO Logistics) concentrated market share among larger operators capable of absorbing technology investments and weathering prolonged freight downturns. Contract-based pricing models have provided relative stability compared to the volatility of the spot market. However, rising insurance, maintenance and equipment costs have offset the efficiency gains achieved through automation and network optimization. Industry revenue reached $1.0 trillion in 2025, growing 1.6% year-over-year, with the current five-year period recording a CAGR of 4.5%. The drop in profit margin reflects the general challenges of the freight trucking industry. As freight demand softened in 2023, labor and operating costs climbed. With elevated interest rates, the freight recession contributed to less revenue growth throughout 2024. If the Fed continues to lower interest rates and consumer spending expands, freight volumes are likely to accelerate through 2025 and 2026. However, tariffs on heavy-duty trucks and imported components, such as aluminum and steel, will likely contribute to higher Class 8 truck prices and significant capital expenditure requirements. Carriers seeking to maintain modern, compliant fleets may generate lower profit over the next five years. Autonomous driving technology continues advancing on regional and long-haul routes, promising efficiency gains through extended operating hours and reduced labor requirements. The Federal Reserve's interest rate cuts could continue to support consumer demand and reduce borrowing costs, which would help offset pressure from tariff-driven equipment price increases and persistent skilled labor shortages. The trucking industry faces simultaneous pressures from regulatory mandates, technological disruption and market consolidation that will reshape competitive dynamics through 2030. Electric vehicle regulations in California, Oregon and Washington are raising entry barriers for non-employer establishments and small fleets by requiring investments in charging infrastructure, specialized technician training and vehicle purchases at premium price points. This accelerates consolidation toward larger carriers with the capital resources to absorb these transition costs. Profit margin will face competing pressures as technology capital investments compete against wage demands from remaining skilled workers. The profit margin is expected to stabilize at 9.3% of revenue in 2030, a 0.2% gain from 2025. This is well below the 29.1% achieved in 2020 because nonemployer growth increased competition in an industry with rising labor and maintenance costs. Industry revenue is projected to climb at a CAGR of 1.6% through 2030, reaching $1.08 trillion.

This is a replication dataset for the publication titled: "Anti-Severe Acute Respiratory Syndrome Coronavirus 2 Immunoglobulin G Antibody Seroprevalence Among Truck Drivers and Assistants in Kenya." The serosurvey was conducted at three sites, one in Kilifi County in South-Eastern Kenya, and two in Busia County in Western Kenya. The dataset contains serology results alongside demographic and clinical information of the participants.

Registration information on interstate, intrastate non-hazmat, and intrastate truck and bus companies that operate in the United States and have registered with FMCSA. Contains contact information and demographic information (number of drivers, vehicles, commodities carried, etc).

Abstract BackgroundGlobally the trucking industry employs millions of people. Recently the prevalence of chronic pain in Southern African truck drivers was reported at 10%. We investigated factors associated with chronic pain in truck drivers including mental health, physical activity, and sleep, as no study has done so. MethodsSouthern African truck drivers were recruited at truck stops in Gauteng and Free State Provinces, South Africa (n=614). Chronic pain was defined as pain present for at least the last three months. Depressive symptoms were assessed with the Patient Health Questionnaire-9, post-traumatic stress disorder with the Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), exposure to traumatic events with the Life Events Checklist-5 (LEC-5) and daytime sleepiness with the Epworth Sleepiness Scale. Sleep quality was measured on a four-point Likert scale. Leisure-time physical activity was measured using the Godin-Shephard leisure-time physical activity questionnaire. Associations between these factors, demographic factors and chronic pain were investigated. ResultsSix hundred and fourteen male truck drivers were recruited. Multivariate analysis showed that working ≥ 2 nights/week (OR=2.68, 95% CI=1.55-4.68) was associated with chronic pain and physical activity was protective (OR=0.97, 95% CI 0.95-0.98). In an exploratory analysis, greater depressive symptoms (p=0.004), daytime sleepiness (p=0.01) and worse sleep quality (p=0.001) was associated with working ≥ 2 nights/week. Lower leisure-time physical activity was associated with worse sleep quality (p=0.006), but not daytime sleepiness or depressive symptoms (p>0.05). ConclusionsThere is a clear relationship between working nights and activity levels, and chronic pain, sleep quality, and depression in truck drivers.

OBJECTIVE To test whether the occupational conditions of professional truck drivers are associated with amphetamine use after demographic characteristics and ones regarding mental health and drug use are controlled for.METHODS Cross-sectional study, with a non-probabilistic sample of 684 male truck drivers, which was collected in three highways in Sao Paulo between years 2012 and 2013. Demographic and occupational information was collected, as well as data on drug use and mental health (sleep quality, emotional stress, and psychiatric disorders). A logistic regression model was developed to identify factors associated with amphetamine use. Odds ratio (OR; 95%CI) was defined as the measure for association. The significance level was established as p < 0.05.RESULTS The studied sample was found to have an average age of 36.7 (SD = 7.8) years, as well as low education (8.6 [SD = 2.3] years); 29.0% of drivers reported having used amphetamines within the twelve months prior to their interviews. After demographic and occupational variables had been controlled for, the factors which indicated amphetamine use among truck drivers were the following: being younger than 38 years (OR = 3.69), having spent less than nine years at school (OR = 1.76), being autonomous (OR = 1.65), working night shifts or irregular schedules (OR = 2.05), working over 12 hours daily (OR = 2.14), and drinking alcohol (OR = 1.74).CONCLUSIONS Occupational aspects are closely related to amphetamine use among truck drivers, which reinforces the importance of closely following the application of law (Resting Act (“Lei do Descanso”); Law 12,619/2012) which regulates the workload and hours of those professionals. Our results show the need for increased strictness on the trade and prescription of amphetamines in Brazil.

Abstract

Trucking Industry Survey as part of a major World Bank initiative called the Africa Infrastructure Country Diagnostic (AICD) study. The survey is carried out in several countries where the World Bank provides development loans. It is increasingly recognized that infrastructure services provide a critical platform for private sector activity and international trade. The trucking industry provides vital transportation services that facilitate both internal and external trade for the other productive sectors. The efficiency and quality of the services provided by the trucking industry is thus an important contributor to country competitiveness. In addition, as a major user of road infrastructure, trucking firms are uniquely placed to assess the functioning of road corridors. The objective of this study is to achieve a major improvement in the country level knowledge base of the infrastructure sectors in the region. The information obtained through the survey is precious as it will provide a baseline against which future improvements in infrastructure services can be measured, making it possible to monitor the results achieved from the current increase in financial flows. It should also provide a more solid empirical foundation for prioritizing investments and designing policy reforms in the infrastructure sectors in Africa.

Catalog of Trucking Surveys for nine sub-Saharan Africa countries is maintained by the Africa Transport unit (AFTTR).

The trucking surveys include nine sub-Saharan African countries. Each survey contains data for approximately 20 trucking companies and 60 truck owner-operators. Seven of the nine national surveys (i.e. Cameroon, Chad, Ghana, Burkina Faso, Kenya, Uganda and Zambia) were conducted for the "Transport Prices and Costs in Africa: A Review of the Main International Corridors" study by Supee Teravaninthorn and Gael Raballand (2008) mostly focusing on the trucking service on international corridors. The other two national trucking surveys (Malawi and Northern Mozambique) follow a slightly different approach with respect to sample selection mostly focusing on the link between the high agro-producing towns, the major cities and the exporting ports and using a revise survey instrument. All the surveys were performed by the Etude Economique Conseil (EEC) in 2007 and 2008 and financed by the World Bank.

Geographic coverage

The Trucking Survey in Uganda targeted trucking companies and companies conducting their own transportation.

Analysis unit

• Truckers/ Truking Operators
• Trucking Companies

Kind of data

Sample survey data [ssd]

Sampling procedure

A trucking company is defined as a company that conducts trucking as its main operation and that has five or more full-time paid employees. A company conducting its own transportation is a company for whom trucking is not its main operation, that conducts the majority of its own transportation and that has five or more full-time paid employees. The companies surveyed serve at least one of the following routes: o Kampala-Nairobi o Kampala-Eldoret o Kampala-Dar es salaam o Kampala-Mombassa o Kampala-Entebbe o Kampala-Malaba o Kampala-Mwanza o Kampala-Kigali

The survey also sampled a selection of truckers (trucking operators with less than five full-time permanent paid employees) that serve the main roads listed above.

Companies with five or more full-time paid permanent employees: A list of Ugandan trucking operators was obtained from the Statistical Office. This list was completed and updated during the pilot survey. Following the results of the validation process, a sample frame consisting of a population of 47 establishments was drawn.

An attempt was made to contact each of these establishments. During the survey, it resulted that 4 establishments were closed, 14 establishments were out of scope or were unreachable despite repeated attempts by phone, 8 establishments refused to participate, and 21 establishments agreed to participate resulting in 21 completed trucking questionnaires, among which 4 trucking companies conducting their own transportation.

Truckers In this survey, the trucker's stratum covers all establishments of the trucking industry with less than 5 employees. For many reasons, including the small size of establishments, their expected high rate of turnovers, the high level of ?informality? of establishments and consequently the difficulty to obtain trustworthy information from official sources, EEC Canada selected an aerial sampling approach to estimate the population of establishments and select the sample in this stratum according to the roads to be covered.

First, to randomly select individual truckers establishments for surveying, the following procedure was followed: i) select districts and specific zones of each district where there are lorry parks or where truckers usually off-loading; ii) count all truckers which generally stop in these specific lorry parks; iii) based on this count, create a virtual list and select establishments at random from that virtual list; and iv) based on the ratio between the number selected in each specific zone and the total population in that zone, create and apply a skip rule for selecting establishments in that zone.

The districts and the specific zones were selected at first according to our national sources. The EEC team then went in the field to verify these national sources and to count truckers. Once the count for each zone was completed, the numbers were sent back to EEC head office in Montreal.

At head office the following procedure was followed: the count by zone was converted into one list of sequential numbers for the whole survey region, and a computer program performed a random selection of the determined number of establishments from the list. Then, based on the number that the computer selected in each specific zone, a skip rule was defined to select truckers to survey in that zone. The skip rule for each zone was sent back to the EEC field team.

In Uganda, enumerators were sent to each zone with instructions as to how to apply the skip rule defined for that zone as well as how to select replacements in the event of a refusal or other cause of non-participation.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data entry and consistency check 1) When data entry was finished for the day, for each type of questionnaire for which additional cases were entered or existing cases were updated, that data file were exported to SPSS format using the provided export utility. 2) The resulting SPSS script was run to open the data in SPSS. 3) The consistency and completion tests script was run in order to generate data regarding the completion status of each case with respect to the consistency checks, and to generate a report detailing these results as well as the completion status of the whole sample with respect to sales.

BackgroundTruck drivers have unique health needs, and by virtue of their continuous travel, experience difficulty in accessing healthcare. Currently, planning for effective care is hindered by lack of knowledge about their health needs and about the impact of on-going programmes on this population’s health outcomes. We reviewed healthcare programmes implemented for sub-Saharan African truck drivers, assessed the evaluation methods, and examined impact on health outcomes.MethodsWe searched scientific and institutional databases, and online search engines to include all publications describing a healthcare programme in sub-Saharan Africa where the main clients were truck drivers. We consulted experts and organisations working with mobile populations to identify unpublished reports. Forest plots of impact and outcome indicators with unadjusted risk ratios and 95% confidence intervals were created to map the impact of these programmes. We performed a subgroup analysis by type of indicator using a random-effects model to assess between-study heterogeneity. We conducted a sensitivity analysis to examine both the summary effect estimate chosen (risk difference vs. risk ratio) and model to summarise results (fixed vs. random effects).ResultsThirty-seven publications describing 22 healthcare programmes across 30 countries were included from 5,599 unique records. All programmes had an HIV-prevention focus with only three expanding their services to cover conditions other primary healthcare services. Twelve programmes were evaluated and most evaluations assessed changes in input, output, and outcome indicators. Absence of comparison groups, preventing attribution of the effect observed to the programme and lack of biologically confirmed outcomes were the main limitations. Four programmes estimated a quantitative change in HIV prevalence or reported STI incidence, with mixed results, and one provided anecdotal evidence of changes in AIDS-related mortality and social norms. Most programmes showed positive changes in risk behaviours, knowledge, and attitudes. Our conclusions were robust in sensitivity analyses.ConclusionDiverse healthcare programmes tailored to the needs of truck drivers implemented in 30 sub-Saharan African countries have shown potential benefits. However, information gaps about availability of services and their effects impede further planning and implementation of effective healthcare programmes for truck drivers.

The overall objective of the i-DREAMS project is to setup a framework for the definition, development, testing and validation of a context-aware safety envelope for driving (‘Safety Tolerance Zone’), within a smart Driver, Vehicle & Environment Assessment and Monitoring System (i-DREAMS). Taking into account driver background factors and real-time risk indicators associated with the driving performance as well as the driver state and driving task complexity indicators, a continuous real-time assessment is made to monitor and determine if a driver is within acceptable boundaries of safe operation. Moreover, safety-oriented interventions were developed to inform or warn the driver real-time in an effective way as well as on an aggregated level after driving through an app- and web-based gamified coaching platform. The conceptual framework, which was tested in a simulator study and three stages of on-road trials in Belgium, Germany, Greece, Portugal and the United Kingdom on a total of 600 participants representing car, bus, and truck drivers, respectively. Specifically, the Safety Tolerance Zone (STZ) is subdivided into three phases, i.e. ‘Normal driving phase’, the ‘Danger phase’, and the ‘Avoidable accident phase’. For the real-time determination of this STZ, the monitoring module in the i-DREAMS platform continuously register and process data for all the variables related to the context and to the vehicle. Regarding the operator, however, continuous data registration and processing are limited to mental state and behavior. Finally, it is worth mentioning that data related to operator competence, personality, socio-demographic background, and health status, are collected via survey questionnaires. More information of the project can be seen from project website: https://idreamsproject.eu/wp/

This dataset contains naturalistic driving data of various trips of participants recruited in i-Dreams project. Various different types of events are recorded for different intensity levels such as headway, speed, acceleration, braking, cornering, fatigue and illegal overtaking. Running headway, speed, distance, wipers use, handheld phone use, high beam use and other data is also recorded. Driver characteristics are also available but not part of this sample data. In the i-Dreams project, raw data for a particular trip was collected via CardioID gateway, Mobileye, wristband or CardioWheel. These trip data are fused using a feature-based data fusion technique, namely geolocation through synchronization and support vector machines. The system provided by CardioID integrates several data streams, generated by the different sensors that make up the inputs of the i-Dreams system. The sample dataset is fused, processed as well as aggregated to produce consistent time series data of trips for a particular time interval such as 30 secs/ 60 secs or 2- minutes intervals. More datasets can be acquired for analysis purposes by following the data acquisition process given in the data description file.