Driving behaviors associated with emergency service vehicle crashes in the U.S. fire service

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.

Data mining approach to explore emergency vehicle crash patterns: A comparative study of crash severity in emergency and non-emergency response modes

Emergency vehicle crashes, involving police vehicles, ambulances, and fire trucks, pose a serious traffic safety concern causing severe injury and deaths to first responders and other road users. However, limited research is available focusing on the contributing factors and their interactions related to these crashes. This research aims to address this gap by 1) identifying patterns of emergency vehicle crashes based on severity levels in both emergency and non-emergency modes and 2) comparing the associations by response modes for the related fatal, nonfatal injury, and no-injury crashes. Two national crash databases, Fatality Analysis Reporting System (FARS) and Crash Report Sampling System (CRSS), were utilized for police-reported emergency vehicle crashes from January 2016 to February 2020. Association rule mining (ARM) was employed to reveal the association between factors that strongly contributed to these crashes. The generated rules were validated using the lift increase criterion (LIC). The results showed the complex nature of risk factors influencing the severity of emergency vehicle crashes. The fatal consequences of speeding with no seatbelt usage were evident for emergency mode, whereas none of these risky driving attributes was observed for non-emergency mode. In addition, the analysis identified the risk of fatal emergency vehicle crashes involving pedestrians in dark-lighted conditions in both response modes. Regarding nonfatal injury severity, angle collisions were more likely to occur at urban intersections during emergencies, while rear-end crashes were more frequent on segments with a posted speed limit of 40-45 mph during non-emergency incidents. The outcomes also revealed that the no-injury crashes involving fire trucks exhibited different patterns depending on the response mode. The findings of this study can guide in making effective strategies to improve safe driving behavior of first responders. The identified associations provide insights into the factors that can be controlled to ensure safe operation of emergency vehicles on the road.

A Retrospective, Single-Agency Analysis of Ambulance Crashes during a 3-Year Period: Association with EMS Driver Characteristics and a Telematics-Measured Safe Driving Score

BACKGROUND

Driver demographics and aggressive driving behavior are established risk factors for traffic accidents, yet their role in ambulance crashes remains poorly studied. We reviewed all ambulance crashes that occurred in our emergency medical services (EMS) agency during a 3-year period, and examined incidence rates (IR) by driver characteristics and telematics-measured driver behavior.

METHODS

This retrospective study was conducted in a U.S. EMS agency that operates 75 Type III ambulances and requires personnel to document all ambulance collisions, regardless of severity. Crashes reported between September 2017 and August 2020 were reviewed, and established criteria were used to classify injury and vehicle damage severity. Serious crashes were defined as events with any injury and/or functional or disabling damage. A vehicle telematics system installed fleet-wide in 2017 continuously captures driver-specific data, including miles driven and indicators related to speeding, harsh cornering and braking, and seatbelt use. A composite score characterizes compliance with safe driving behaviors (1 = low compliance to 5 = high compliance). Crash IR per 100,000 miles, IR ratios (IRR), and Poisson regression were used in analysis. Driver sex, age, agency tenure, miles driven, and safe driving score were examined.

RESULTS

Clinicians reported 214 crashes and the IR of any crash and serious crash were 2.1 and 0.63 per 100,000 miles, respectively. Injuries occurred in 8% of crashes and were all of low acuity. About one third of crashes produced functional (21%) or disabling (8%) vehicle damage, and the ambulance required towing in 10%. In a multivariate model, female sex (IRR = 1.50, 95%CI = 1.13-1.97), age 18-24 (IRR = 1.67, 95%CI = 1.06-2.66), and being in the lowest quartile of safe driving score (IRR = 1.51, 95%CI = 1.14-2.02) were EMS driver factors independently associated with an increased risk of any collision.

CONCLUSION

Most ambulance crashes are minor events, but the proportion that result in injury and/or functional or disabling vehicle damage may be as high as one-third. Poor driver compliance with objectively measured safe driving behaviors may increase risk for collisions independent of driver sex and age. The EMS industry would benefit from additional studies that examine the full spectrum of ambulance crashes and expand understanding of EMS driver-related risk factors.

Driving Speeds in Urgent and Non-Urgent Ambulance Missions during Normal and Reduced Winter Speed Limit Periods—A Descriptive Study

Objective: Most traffic research on emergency medical services (EMS) focuses on investigating the time saved with emergency response driving. Evidence regarding driving speed during non-urgent ambulance missions is lacking. In contrast, this descriptive study compared registered driving speeds to the road speed limit in urgent A-missions and non-urgent D-missions. Specifically, the study examined driving speeds during normal speed limits, periods of reduced winter speed limits, and speeding during non-urgent D-missions. Methods: Urgent A-missions and non-urgent D-missions were included. Registered ambulance locations and speed data from Pirkanmaa Hospital District, Finland between 1 January 2018 and 31 December 2018 were used. Ambulance locations were linked to OpenStreetMap digital road network data. The registered driving speed distribution was reported as quartiles by the effective road speed limit. Furthermore, the results during the normal speed limit and reduced winter speed limit periods were reported separately. Driving speeds in non-urgent missions were compared with current Finnish traffic violation legislation. Results: As expected, the urgent A-missions exceeded the speed limits during both the normal speed limit and reduced winter speed limit periods. On the smallest streets with speed limits of 30 km/h, the driving speeds in urgent missions were lower than the speed limit. The driving speeds in non-urgent D-missions were broadly similar throughout the whole year on high-speed roads, and mostly on lower speed limit roads. However, within the 30 km/h speed limits, the mean speed in non-urgent missions appeared to increase during the winter. One-fifth of the registered non-urgent D-missions were speeding. Conclusions: Speeding is common in urgent A-missions and non-urgent D-missions throughout the year. Stricter guidelines for EMS are needed to increase driving safety.

Driving the ambulance: an essential component of emergency medical services: an integrative review

Background

The transport of patients from one location to another is a fundamental part of emergency medical services. However, little interest has been shown in the actual driving of the ambulance. Therefore, this review aimed to investigate how the driving of the ambulance affects the patient and the medical care provided in an emergency medical situation.

Methods

A systematic integrative review using both quantitative and qualitative designs based on 17 scientific papers published between 2011 and 2020 was conducted.

Results

Ambulance driving, both the actual speed, driving pattern, navigation, and communication between the driver and the patient, influenced both the patient’s medical condition and the possibility of providing adequate care during the transport. The driving itself had an impact on prehospital time spent on the road, safety, comfort, and medical issues. The driver’s health and ability to manage stress caused by traffic, time pressure, sirens, and disturbing moments also significantly influenced ambulance transport safety.

Conclusions

The driving of the ambulance had a potential effect on patient health, wellbeing, and safety. Therefore, driving should be considered an essential part of the medical care offered within emergency medical services, requiring specific skills and competence in both medicine, stress management, and risk approaches in addition to the technical skills of driving a vehicle. Further studies on the driving, environmental, and safety aspects of being transported in an ambulance are needed from a patient’s perspective.

Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement

INTRODUCTION

Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study.

METHOD

A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the 'all driver' sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans.

RESULTS

With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures.

CONCLUSIONS

The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.