Evaluation of an in-vehicle monitoring system (IVMS) to reduce risky driving behaviors in commercial drivers: Comparison of in-cab warning lights and supervisory coaching with videos of driving behavior

Comprehensive Assessment of Artificial Intelligence Tools for Driver Monitoring and Analyzing Safety Critical Events in Vehicles

Human factors are a primary cause of vehicle accidents. Driver monitoring systems, utilizing a range of sensors and techniques, offer an effective method to monitor and alert drivers to minimize driver error and reduce risky driving behaviors, thus helping to avoid Safety Critical Events (SCEs) and enhance overall driving safety. Artificial Intelligence (AI) tools, in particular, have been widely investigated to improve the efficiency and accuracy of driver monitoring or analysis of SCEs. To better understand the state-of-the-art practices and potential directions for AI tools in this domain, this work is an inaugural attempt to consolidate AI-related tools from academic and industry perspectives. We include an extensive review of AI models and sensors used in driver gaze analysis, driver state monitoring, and analyzing SCEs. Furthermore, researchers identified essential AI tools, both in academia and industry, utilized for camera-based driver monitoring and SCE analysis, in the market. Recommendations for future research directions are presented based on the identified tools and the discrepancies between academia and industry in previous studies. This effort provides a valuable resource for researchers and practitioners seeking a deeper understanding of leveraging AI tools to minimize driver errors, avoid SCEs, and increase driving safety.

Effectiveness of road safety interventions: An evidence and gap map

Background

Road Traffic injuries (RTI) are among the top ten leading causes of death in the world resulting in 1.35 million deaths every year, about 93% of which occur in low- and middle-income countries (LMICs). Despite several global resolutions to reduce traffic injuries, they have continued to grow in many countries. Many high-income countries have successfully reduced RTI by using a public health approach and implementing evidence-based interventions. As many LMICs develop their highway infrastructure, adopting a similar scientific approach towards road safety is crucial. The evidence also needs to be evaluated to assess external validity because measures that have worked in high-income countries may not translate equally well to other contexts. An evidence gap map for RTI is the first step towards understanding what evidence is available, from where, and the key gaps in knowledge.

Objectives

The objective of this evidence gap map (EGM) is to identify existing evidence from all effectiveness studies and systematic reviews related to road safety interventions. In addition, the EGM identifies gaps in evidence where new primary studies and systematic reviews could add value. This will help direct future research and discussions based on systematic evidence towards the approaches and interventions which are most effective in the road safety sector. This could enable the generation of evidence for informing policy at global, regional or national levels.

Search Methods

The EGM includes systematic reviews and impact evaluations assessing the effect of interventions for RTI reported in academic databases, organization websites, and grey literature sources. The studies were searched up to December 2019.

Selection Criteria

The interventions were divided into five broad categories: (a) human factors (e.g., enforcement or road user education), (b) road design, infrastructure and traffic control, (c) legal and institutional framework, (d) post-crash pre-hospital care, and (e) vehicle factors (except car design for occupant protection) and protective devices. Included studies reported two primary outcomes: fatal crashes and non-fatal injury crashes; and four intermediate outcomes: change in use of seat belts, change in use of helmets, change in speed, and change in alcohol/drug use. Studies were excluded if they did not report injury or fatality as one of the outcomes.

Data Collection and Analysis

The EGM is presented in the form of a matrix with two primary dimensions: interventions (rows) and outcomes (columns). Additional dimensions are country income groups, region, quality level for systematic reviews, type of study design used (e.g., case-control), type of road user studied (e.g., pedestrian, cyclists), age groups, and road type. The EGM is available online where the matrix of interventions and outcomes can be filtered by one or more dimensions. The webpage includes a bibliography of the selected studies and titles and abstracts available for preview. Quality appraisal for systematic reviews was conducted using a critical appraisal tool for systematic reviews, AMSTAR 2.

Main Results

The EGM identified 1859 studies of which 322 were systematic reviews, 7 were protocol studies and 1530 were impact evaluations. Some studies included more than one intervention, outcome, study method, or study region. The studies were distributed among intervention categories as: human factors (n = 771), road design, infrastructure and traffic control (n = 661), legal and institutional framework (n = 424), post-crash pre-hospital care (n = 118) and vehicle factors and protective devices (n = 111). Fatal crashes as outcomes were reported in 1414 records and non-fatal injury crashes in 1252 records. Among the four intermediate outcomes, speed was most commonly reported (n = 298) followed by alcohol (n = 206), use of seatbelts (n = 167), and use of helmets (n = 66). Ninety-six percent of the studies were reported from high-income countries (HIC), 4.5% from upper-middle-income countries, and only 1.4% from lower-middle and low-income countries. There were 25 systematic reviews of high quality, 4 of moderate quality, and 293 of low quality.

Authors' Conclusions

The EGM shows that the distribution of available road safety evidence is skewed across the world. A vast majority of the literature is from HICs. In contrast, only a small fraction of the literature reports on the many LMICs that are fast expanding their road infrastructure, experiencing rapid changes in traffic patterns, and witnessing growth in road injuries. This bias in literature explains why many interventions that are of high importance in the context of LMICs remain poorly studied. Besides, many interventions that have been tested only in HICs may not work equally effectively in LMICs. Another important finding was that a large majority of systematic reviews are of low quality. The scarcity of evidence on many important interventions and lack of good quality evidence-synthesis have significant implications for future road safety research and practice in LMICs. The EGM presented here will help identify priority areas for researchers, while directing practitioners and policy makers towards proven interventions.

Risky driving behaviors and employer motor vehicle safety policies among U.S. oil and gas extraction workers

INTRODUCTION

Over half of fatal occupational injuries in the oil and gas extraction (OGE) industry are due to transportation incidents. While driving for work is common in this industry and risky driving behaviors have been identified as contributing factors to fatal crashes among OGE workers, limited information is available on the frequency of risky driving behaviors and employer policies to reduce these behaviors.

METHODS

Researchers conducted a cross-sectional survey of OGE workers in three states. Responses from 363 OGE workers who drive as a part of their work duties were analyzed to evaluate relationships between self-reported risky driving behaviors (i.e., speeding, cell phone use, and driving unbelted) and awareness of motor vehicle safety policies by their employers.

RESULTS

Hands-free cell phone use was the most common risky driving behavior among participants (59.8%), while a hands-free cell phone ban was the least commonly reported employer motor vehicle safety policy (34.7%). Multiple logistic regression results identified longer work and commuting hours, lack of employer motor vehicle safety policies, having ever been in a work crash, and being employed by an operator to be significantly associated with risky driving behaviors.

CONCLUSIONS

Workers whose employers lacked motor vehicle safety policies were more likely to engage in risky driving behaviors.

PRACTICAL APPLICATIONS

Results of this survey support the implementation of motor vehicle safety interventions such as bans on texting and handheld and hands-free cell phone use, speed management, and in-vehicle monitoring systems by OGE employers as well as research focusing on the effectiveness of these interventions in OGE. Additional research could examine worker driving behaviors through self-reported data in combination with objective measures.

Optimization method to reduce the risky driving behaviors of ride-hailing drivers

INTRODUCTION

To promote the safety level of ride-hailing services, this study develops the Targeted and Differentiated Optimization Method of Risky Driving Behavior Education and Training (TDOM-RDBET) founded on driver type classification of high-risk drivers.

METHOD

Based on value and goal orientations, 689 drivers were classified into four driver types and were assigned to three groups, including an experimental group, a blank control group, and a general control group. This research preliminarily analyzes the effectiveness of the TDOM-RDBET to reduce mobile phone use while driving by assessing the main effects of the group and test session on the risk value ranking of mobile phone use while driving (AR), the frequency per 100 km of mobile phone use while driving (AF), and the frequency per 100 km of risky driving behaviors (AFR), as well as the interactive effects of the two factors on AR, AF, and AFR, based on a two-way analysis of variance (two-way ANOVA).

RESULTS

The results demonstrate an overall significant reduction in AR (F = 8.653, p = 0.003), AF (F = 11.027, p = 0.001), and AFR (F = 8.072, p = 0.005) for the experimental group after training. Moreover, significant interactive effects of the driver group × test session on AR (F = 7.481, p = 0.001) and AF (F = 15.217, p < 0.001) were found. AR was significantly lower for the experimental group than for the blank control group (p < 0.05) in the post-training condition. Moreover, AF was also significantly lower for the experimental group than for the blank control group (p < 0.05) and general control group (p < 0.05) in the post-training condition.

PRACTICAL APPLICATIONS

On the whole, it was preliminarily verified that the TDOM-RDBET is more effective than the general training method at modifying the risky driving behavior.

On the impact of advanced driver assistance systems on driving distraction and risky behaviour: An empirical analysis of irish commercial drivers

Advanced driver assistance systems (ADAS) present promising benefits in mitigating road collisions. However, these benefits are limited when risky drivers continue engaging in distraction events. While there is evidence that real-time warnings help improve driving behaviour, the sustained benefits of warning-based ADAS on reducing driving distraction in light commercial vehicle (LCV) drivers remain unclear. This research determines the effect of receiving instant distraction warnings over two years using a naturalistic driving dataset comprising around one million trips from 373 LCV drivers in the Republic of Ireland. Furthermore, the study applies Association Rule Mining (ARM) to find the contextual variables (e.g., speed limit, road type, traffic conditions) that increase the likelihood of distraction events. The results show that warning-based ADAS providing real-time warnings helps reduce distraction events triggering driver inattention, forward collision, and lane departure warnings. Over half of the studied fleet reduced these warnings by at least 50% - lane departure after two months and driver inattention and forward collision after six months. It is found that both passive and active monitoring systems, coupled with coaching and rewards, significantly reduce aggressive driving behaviours tied to harsh acceleration (by 76%) and harsh braking (by 65%). The results of ARM show that the driving context introduces explanatory information for road safety programs. Low-speed urban roads and the summer season increase the likelihood of driver inattention and forward collision warnings. In contrast, high-speed rural roads increase the likelihood of lane departure warnings. These research findings support road safety stakeholders in developing risk assessments based on warning-based ADAS, targeted campaigns to reduce driving distraction, and driving coaching programs.

An exploration of the role of driving experience on self-reported and real-world aberrant driving behaviors

A significant proportion of global road crashes are attributed to unsafe driving behaviors. The current study aimed to explore potential differences in driving behaviors across experienced and novice drivers using two separate approaches; a questionnaire study and an instrumented vehicle study (IVS). The analysis of 260 questionnaires and 1,372 traffic interactions within the IVS revelated that driving experience affects driving performance for different driving tasks. Factor analysis of the questionnaire data revealed the impact of driving errors, lapses, violations, and aggressive violations on the behavior of novice and experienced drivers. Behavioral models of novice and experienced drivers encountering other road users were determined using binary logistic regression. The results showed that novice drivers were more likely to engage in driving violations while experienced drivers were more likely to engage in aggressive violations. Unauthorized speeding, zigzag movements, using a mobile phone while driving, and unauthorized overtaking on roads were the most frequent driving violations by novice drivers. The most frequent aggressive violations by experienced drivers were tempting other drivers to create a race and chasing other drivers. These findings may be used as a framework to facilitate safer driving behaviors by reducing errors, lapses, violations and aggressive violations, and facilitating safety-promoting attitudes.

Driver injuries in heavy vs. light and medium truck local crashes, 2010-2019

OBJECTIVE

Multiple heavy truck driver injury studies exist, but there is a paucity of research on light and medium truck driver injuries. The objective of this study was to use first report of injury (FROI) data to: (a) compare demographic and injury characteristics; (b) assess workers' compensation (WC) claim disposition and lost work time status; and (c) describe injury scenarios by vehicle type for heavy truck and light/medium truck driver local crashes.

METHOD

Kentucky Department of Workers' Claims FROI quantitative and free text data were analyzed for years 2010-2019. Of 800 total FROIs, 451 involved heavy trucks and 349 involved light or medium trucks.

RESULTS

There was a higher light/medium truck driver crash FROI rate compared to the heavy truck driver crash FROI rate. There was a higher proportion of younger light/medium truck driver crash FROIs compared to younger heavy truck driver crash FROIs. The retail trade industry made up the largest percentage of light/medium truck local crash FROIs (47%); the transportation and warehousing industry was most frequently cited in heavy truck FROIs (46%). The heavy truck types most frequently identified in FROIs were semi-trucks (13%) and dump trucks (11%). The most common light/medium truck type identified was delivery trucks (30%). Most commonly, heavy truck crash FROIs involved rollovers, driving off/overcorrecting on narrow roadways, and driving downhill/unable to downshift. Light/medium truck crash FROIs most frequently involved being rear-ended, running red lights, and turning in front of other vehicles.

CONCLUSIONS

The utilization of WC FROI data highlighted top injury scenarios and specific vehicle types for targeting driver safety training among truck drivers, particularly light/medium truck drivers. Road safety policies regarding driver training, crash reviews, and in-vehicle monitoring systems are needed for truck drivers with previous crash injuries, especially for light and medium truck drivers.

PRACTICAL APPLICATIONS

Enhanced safety training on speeding on narrow roadways, on nearing intersections, and on downshifting on hills is needed for semi-truck, dump truck, and coal truck drivers with previous crash injuries. Rear-end crash prevention training (e.g., gradual stopping and checking mirrors) is needed for drivers of furniture, automotive parts and accessories, and groceries and soft drink delivery trucks with previous crash injuries.

Assessing the Australian occupational driver behavior questionnaire in U.S. taxi drivers: Different country, different occupation and different worker population

BACKGROUND

Promoting safe driver behaviors is an important aspect of road safety. To better understand road safety behaviors, there is a role for practical instruments that can validly measure typical road safety behaviors among occupational drivers. The Occupational Driver Behavior Questionnaire (ODBQ) was developed to assess road safety behaviors among home health nurses in Australia.

METHODS

We administered a cross-sectional survey to a sample of taxi drivers in two U.S. metropolitan areas. The survey included Newnam's ODBQ-12 and a study-specific 15-item version (ODBQ-15) assessing 4 different road safety behaviors with 3 more items added and motor-vehicle crashes in the past year. Logistic regression analyses examined the association of the road safety behaviors with motor vehicle crashes. A series of confirmatory factor analysis (CFA) models assessed the construct validity of the ODBQ-12 and ODBQ-15.

RESULTS

We pooled survey data from 497 Houston drivers and 500 Los Angeles drivers to assess study aims. CFA models examining the 12-item and the 15-item ODBQ versions had good model fit (Comparative Fit Index > 0.95, Tucker Lewis Index ≥ 0.95, root mean square error of approximation < 0.06, standardized root mean square residual ≤ 0.05). The ODBQ's road safety behaviors were significantly associated (p < 0.001) with crashes while working (ORs 0.51-0.75) and not working (ORs 0.57-0.84).

CONCLUSIONS

The ODBQ-12 and ODBQ-15 were both significantly associated with motor vehicle crashes among taxicab drivers in two large U.S. metropolitan areas. Researchers studying occupational drivers who transport passengers may want to consider using the ODBQ-15. The 3 additional items are meaningful to this workforce and are priority areas for international road safety efforts.

Unsafe Behaviors Analysis of Sideswipe Collision on Urban Expressways Based on Bayesian Network

The causes of crashes on urban expressways are mostly related to the unsafe behaviors of drivers before the crash. This study focuses on sideswipe collisions on urban expressways. Through real and visual crash data, 17 unsafe behaviors were identified for the analysis of sideswipe collisions on an urban expressway. The chains of high-risk and unsafe behaviors were then revealed to investigate the relationship between drivers’ unsafe behaviors and sideswipe collisions. A Bayesian network diagram of unsafe behaviors was used to obtain the correlation between unsafe behaviors and their influence. A topology diagram of unsafe behaviors was then constructed, and relational reasoning of typical behavioral chains was conducted. Finally, the unsafe behaviors and behavior chains that were likely to cause sideswipe collisions on the urban expressway were determined. The possibility of each behavior chain was quantified through the reasoning of variable structures constructed by the Bayesian network. The result shows that the significant influential single unsafe behavior leading to sideswipe collision on urban expressways was lane change without checking the rearview mirror or not scanning the road around and queue-jumping; moreover, based on unsafe behavior chains analysis, the most influential chains leading to sideswipe collision were: improper driving behavior in an emergency—failure to turn on signal when changing lanes—distracted and inattentive driving. Some safety precautions and countermeasures aimed at unsafe behaviors could be taken before the crash. The results of the study can be used to reduce the number of sideswipe collisions, thereby improving traffic safety on urban expressways.

In-Cabin Monitoring System for Autonomous Vehicles

In this paper, we have demonstrated a robust in-cabin monitoring system (IMS) for safety, security, surveillance, and monitoring, including privacy concerns for personal and shared autonomous vehicles (AVs). It consists of a set of monitoring cameras and an onboard device (OBD) equipped with artificial intelligence (AI). Hereafter, this combination of a camera and an OBD is referred to as the AI camera. We have investigated the issues for mobility services in higher levels of autonomous driving, what needs to be monitored, how to monitor, etc. Our proposed IMS is an on-device AI system that indigenously has improved the privacy of the users. Furthermore, we have enlisted the essential actions to be considered in an IMS and developed an appropriate database (DB). Our DB consists of multifaced scenarios important for monitoring the in-cabin of the higher-level AVs. Moreover, we have compared popular AI models applied for object and occupant recognition. In addition, our DB is available on request to support the research on the development of seamless monitoring of the in-cabin higher levels of autonomous driving for the assurance of safety and security.

Privacy-Preserved In-Cabin Monitoring System for Autonomous Vehicles

Fully autonomous vehicles (FAVs) lack monitoring inside the cabin. Therefore, an in-cabin monitoring system (IMS) is required for surveilling people causing irregular or abnormal situations. However, monitoring in the public domain allows disclosure of an individual’s face, which goes against privacy preservation. Furthermore, there is a contrary demand for privacy in the IMS of AVs. Therefore, an intelligent IMS must simultaneously satisfy the contrary requirements of personal privacy protection and person identification during abnormal situations. In this study, we proposed a privacy-preserved IMS, which can reidentify anonymized virtual individual faces in an abnormal situation. This IMS includes a step for extracting facial features, which is accomplished by the edge device (onboard unit) of the AV. This device anonymizes an individual’s facial identity before transmitting the video frames to a data server. We created different abnormal scenarios in the vehicle cabin. Further, we reidentified the involved person by using the anonymized virtual face and the reserved feature vectors extracted from the suspected individual. Overall, the proposed approach preserves personal privacy while maintaining security in surveillance systems, such as for in-cabin monitoring of FAVs.

Reversing Poor Safety Records: Identifying Best Practices to Improve Fleet Safety

Commercial motor vehicle safety is of utmost importance, as crashes involving commercial motor vehicles often result in significant property damage, injuries, fatalities, and financial loss for fleets. However, fleet managers are often unsure what strategies other fleets have used to successfully improve safety. To identify best practices, researchers completed case studies with nine commercial motor vehicle fleets that successfully improved their safety performance. A content analysis was performed, and the successful strategies were organized into the Haddon Matrix. Results showed that there was no one single strategy that fleets used to improve safety. Instead, fleets relied on a comprehensive approach focusing on pre-crash countermeasures, including addressing hiring practices, driver training, fleet safety culture, safety technologies, scheduling, and maintenance. However, an enhanced safety culture and advanced safety technology were identified as critical components to their safety improvement. Results from this study may help fleets understand what their peers have used to successfully improve safety and which strategies may not be as helpful.

Workers' compensation claim counts and rates by injury event/exposure among state-insured private employers in Ohio, 2007-2017

INTRODUCTION

This study analyzed workers' compensation (WC) claims among private employers insured by the Ohio state-based WC carrier to identify high-risk industries by detailed cause of injury.

METHODS

A machine learning algorithm was used to code each claim by U.S. Bureau of Labor Statistics (BLS) event/exposure. The codes assigned to lost-time (LT) claims with lower algorithm probabilities of accurate classification or those LT claims with high costs were manually reviewed. WC data were linked with the state's unemployment insurance (UI) data to identify the employer's industry and number of employees. BLS data on hours worked per employee were used to estimate full-time equivalents (FTE) and calculate rates of WC claims per 100 FTE.

RESULTS

140,780 LT claims and 633,373 medical-only claims were analyzed. Although counts and rates of LT WC claims declined from 2007 to 2017, the shares of leading LT injury event/exposures remained largely unchanged. LT claims due to Overexertion and Bodily Reaction (33.0%) were most common, followed by Falls, Slips, and Trips (31.4%), Contact with Objects and Equipment (22.5%), Transportation Incidents (7.0%), Exposure to Harmful Substances or Environments (2.8%), Violence and Other Injuries by Persons or Animals (2.5%), and Fires and Explosions (0.4%). These findings are consistent with other reported data. The proportions of injury event/exposures varied by industry, and high-risk industries were identified.

CONCLUSIONS

Injuries have been reduced, but prevention challenges remain in certain industries. Available evidence on intervention effectiveness was summarized and mapped to the analysis results to demonstrate how the results can guide prevention efforts. Practical Applications: Employers, safety/health practitioners, researchers, WC insurers, and bureaus can use these data and machine learning methods to understand industry differences in the level and mix of risks, as well as industry trends, and to tailor safety, health, and disability prevention services and research.

Factors associated with different levels of daytime sleepiness among Korean construction drivers: a cross-sectional study

Background

Commercial vehicle accidents are the leading cause of occupational fatalities and an increased risk of traffic accidents is associated with excessive fatigue, other health problems as well as poor sleep during work. This study explores individual and occupational factors associated with different levels of daytime sleepiness and identifies their association with driving risk among occupational drivers working at construction sites.

Methods

This cross-sectional and correlational study adopted a self-reported questionnaire of Korean construction drivers (N = 492). The data were collected from October 2018 to February 2019 using a battery of six validated instruments about participants’ sociodemographic, health-related, and occupational characteristics. One-way ANOVA and multinomial logistic regression were conducted using IBM SPSS WIN/VER 25.0, with a two-tailed alpha of .05.

Results

Based on the Epworth Sleepiness Scale, “moderate” (31.7%) and “severe” (10.2%) daytime sleepiness groups were identified. There were significant differences in break time, driving fatigue, depressive symptom, subjective sleep quality, physical and mental health, and driving risk among the three groups (all p-values < .001). Driving fatigue (Adjusted Odds Ratio [aOR] = 1.08, 1.17), depressive symptoms (aOR = 0.91, 0.98), subjective sleep quality (aOR = 1.18 in moderate only), and driving over the speed limit (aOR = 1.43, 2.25) were significant factors for determining “moderate” and “severe” daytime sleepiness groups, respectively.

Conclusion

A significant number of construction drivers experience excessive daytime sleepiness; thus it is important to reduce the negative impact of driving fatigue and other factors on daytime sleepiness. Our study findings suggest that occupational health care providers should pay attention to development and implementation of health management interventions to reduce driving fatigue that incorporate the drivers’ physical, mental, and occupational factors. Professional organizations need to establish internal regulations and public policies to promote health and safety among occupational drivers who specifically work at construction sites.

Video feedback intervention for cognitively impaired older drivers: A randomized clinical trial

INTRODUCTION

This clinical trial aimed to determine whether in-car video feedback about unsafe driving events (UDE) to cognitively impaired older drivers and family members leads to a reduction in such driving behaviors.

METHODS

We randomized 51 cognitively impaired older drivers to receive either (1) a weekly progress report with recommendations and access to their videos, or (2) video monitoring alone without feedback over 3 months.

RESULTS

UDE frequency/1000 miles was reduced by 12% in feedback (rate ratio [RR] = 0.88, 95% confidence interval [CI] = .58-1.34), while remaining constant with only monitoring (RR = 1.01, 95% CI = .68-1.51). UDE severity/1000 miles was reduced by 37% in feedback (RR = 0.63, 95% CI = .31-1.27), but increased by 40% in monitoring (RR = 1.40, 95% CI = .68-2.90). Cognitive impairment moderated intervention effects (P = .03) on UDE frequency.

DISCUSSION

Results suggest the potential to improve driving safety among mild cognitively impaired older drivers using a behavior modification approach aimed at problem behaviors detected in their natural driving environment.

Evaluating the impact of Heavy Goods Vehicle driver monitoring and coaching to reduce risky behaviour

Determining the impact of driver-monitoring technologies to improve risky driving behaviours allows stakeholders to understand which aspects of onboard sensors and feedback need enhancement to promote road safety and education. This study investigates the influence of camera monitoring on Heavy Goods Vehicle (HGV) drivers' risky behaviours. We also assess whether monitoring affects individual driving events further when coupled with safe driving practices coaching. We evaluate the outcome of those practices on three telematics incidents heavily reliant on driving errors and violations, i.e., the number of vehicle harsh braking, harsh cornering and over speeding incidents. The objective is to understand how frequently individual incidents caused by risky driving behaviour occur (a) without camera monitoring and without any coaching; (b) after camera installation; and (c) after camera installation and coaching. We investigate two commercial HGV companies (Company 1 and Company 2) with 263 and 269 vehicles, respectively, over a 16 months period, from which the first 8 months contain data collected before the installation of cameras (baseline) and the rest of the dataset contains incident counts after the installation of cameras (intervention). Company 1 provides coaching during the intervention phase while Company 2 does not offer coaching. Our analysis considers the baseline and the intervention phases during the same seasons to eliminate any possible bias due to the influence of weather on driving behaviour. Results show an overall significant reduction in the mean frequency of harsh braking incidents from baseline to intervention by 16.82% in Company 1 and 4.62% in Company 2, and a significant reduction in the mean frequency of over speeding incidents from baseline to intervention by 34.29% in Company 1 and 28.13% in Company 2. Furthermore, the effect of coaching has a significant difference in reducing the frequency of harsh braking (p = .011) and harsh cornering (p < .001) compared to just camera monitoring. These results suggest that coaching interventions are more effective in reducing driving errors while monitoring reduces both driving errors and violations.

The impact of a crash prevention program in a large law enforcement agency

BACKGROUND

Motor vehicle crashes (MVCs) remain a leading cause of death for US law enforcement officers. One large agency implemented a crash prevention program with standard operating policy changes, increased training, and a marketing campaign. This was a scientific evaluation of that crash prevention program.

METHODS

MVC and motor vehicle injury (MVI) data for law enforcement officers were compared using an autoregressive integrated moving average (ARIMA) model. Two law enforcement agencies who had not implemented a crash prevention program were controls.

RESULTS

After program implementation, overall, MVC rates significantly decreased 14% from 2.2 MVCs per 100 000 miles driven to 1.9 (P = .008). MVC rates did not decrease in the control agencies. Overall, MVI rates significantly decreased 31% from 3.4 per 100 officers to 2.1 (P = .0002). MVC rates did not decrease in the control agencies. MVC rates for patrol officers significantly decreased 21% from 3.1 per 100 000 miles to 2.4. MVI rates for patrol officers significantly decreased 48% from 3.2 per 100 officers to 1.6 (P < .0001).

CONCLUSIONS

Crash and injury rates can be reduced after implementation of a crash prevention program and the largest impacts were seen in patrol officers.

Analytical observational study of nonfatal motor vehicle collisions and incidents in a light-vehicle sales and service fleet

Motor vehicle crashes (MVCs) are a significant cause of lost-workday injuries, and consistently the leading cause of work-related fatalities in the United States for all industries combined. Prevention research has focused mainly on collisions fatal to the drivers of large trucks. This analytical observational study addresses gaps in the literature by: conducting a descriptive analysis of motor vehicle claim events involving light-vehicle drivers in a large health care industry fleet; identifying risk factors for work-related MVCs and injuries based on vehicle miles traveled; and providing details on circumstances of these events. The study examined 8068 motor vehicle events resulting in vehicle damage, property damage, or injury reported by 6680 U.S.-based drivers in a light-vehicle sales and service fleet operated by a health care company over a 4 ½-year period (January 2010 through June 2014). Thirty-three percent (n = 2660) of the events were collisions. Collisions were segmented as recoverable or non-recoverable according to whether the company could recover costs from another party, and mileage-based collision and injury rates were calculated by gender, age, tenure, and vehicle type. Differences in collision and injury rates between groups of interest (for example, tenure and age categories) were assessed with Poisson regression techniques adjusted using generalized estimating equations (GEE) for repeated observations on the same employee over time. Age, gender, and job tenure were significant collision risk factors, and risk patterns for recoverable and non-recoverable collisions were similar to those for total collisions. Collisions per million miles (CPMM) were significantly higher for drivers 21-24.9 years of age compared to drivers age 25-54.9 years (9.58 CPMM vs 4.96 CPMM, p = .025), drivers employed for less than 2 years compared to those employed 2 or more years (6.22 CPMM vs 4.82 CPMM, p < .001), for female drivers compared to male drivers (6.37 CPMM vs 4.16 CPMM, p < .001), and for drivers of passenger cars compared to all other vehicles (5.27 CPMM vs 4.48 CPMM, p < .001). Among collisions between the employee's vehicle and another vehicle in transport, those where the front of one vehicle hit another vehicle at an angle were the most likely to result in injury to the employee driver or another party (26%), followed by rear-end collisions (25%). Special attention should be given to preventing collisions among newly-hired employees, and to preventing angle and rear-end collisions, which were the most common types of collisions and also were most likely to result in injury than all other collisions combined.

A systematic review of oil tanker truck disasters: Identifying prevention targets

INTRODUCTION

Oil tanker truck disasters have been reported worldwide; however, the circumstances, causes, and health effects of these disasters have not been described. To address this gap, we performed a systematic review using PRISMA criteria to better understand this public health problem and identify prevention targets.

METHODS

The academic and lay literatures were systematically searched for terms related to oil tanker truck disasters. Reports about civilian oil tanker truck disasters that occurred from 1997-2017 were included. Details about the disasters were summarized, including circumstances, identifiable causes, and health effects.

RESULTS

The search yielded 4713 Nexis Uni articles, 199 Google results, and one PubMed article; 951 records met inclusion criteria, describing 224 oil tanker truck explosions or fires. At least 2909 people died as a result of these disasters, and 3038 additional people were hospitalized. Almost all deaths (94%) occurred in low- and low-middle-income countries (LMIC). This may largely be due to scooping - the practice of collecting spilled oil from disabled tanker trucks for use or resale. Using the Haddon matrix, potential targets for future disaster prevention were identified.

CONCLUSIONS

These data highlight the circumstances, causes, and health burden related to oil tanker truck disasters. Most began as collisions or rollovers, but nearly half of the fatalities involved scooping. The findings suggest opportunities to promote road safety, improve scene safety and security protocols used by drivers and first responders, and promote public understanding of the dangers of scooping to prevent mass casualty disasters from disabled tanker trucks, particularly in LMIC.