Frequency and cost of crashes, fatalities, and injuries involving disabled vehicles.

OBJECTIVE: To quantify the total number and cost of crashes, fatalities, and injuries that could be addressed by improved conspicuity of disabled vehicles to approaching traffic. METHODS: Using the Fatality Analysis Reporting System (FARS) and the Crash Report Sampling System (CRSS) this study defines three crash scenarios where insufficient conspicuity of a disabled vehicle ("low conspicuity emergency") resulted in injury or death: Scenario 1) Moving vehicle strikes non-moving vehicle following an initial event; Scenario 2) Pedestrian (primarily a motorist who has exited their vehicle) is struck while tending to a disabled or stopped vehicle; and Scenario 3) A vehicle departs the roadway and crashes unnoticed and rescue initiation is delayed significantly. RESULTS: Annually, between the years 2016 and 2018, an estimated 71,693 people were involved in low conspicuity emergency events, including 566 fatalities and 14,371 injuries. Most (95 %) of these cases occurred under scenario 1. Notable, however, is the severity of scenario 2 crashes where the majority were severely injured (22 %) or killed (19 %). Based on the FARS data, nearly 300 people were killed under scenario 2 each year and cases have increased 27 % since 2014. Overall, crashes under these three scenarios resulted in an annual estimated $8.8 billion in societal costs, including the economic costs of medical payments and wage loses in addition to the value of quality of life lost due to death or disability. Scenario 1 crashes resulted in an average of $4.3 billion in losses, scenario 2 crashes in $3.4 billion in losses, and scenario 3 crashes in $1.2 billion in losses annually. CONCLUSIONS: A significant number of people die or are injured in low conspicuity events every year; an estimated 1.55 deaths and nearly 40 injuries per day. This analysis highlights the risks to a special subset of pedestrians: motorists who exited their vehicles to attend to a disabled or stopped vehicle. These deaths and injuries that result from crashes related to low-conspicuity events are preventable. Countermeasures to reduce the incidence and severity of the crash scenarios studied should be explored.

Statistical modeling of cargo tank truck crashes: Rollover and release of hazardous materials.

INTRODUCTION: Cargo Tank Trucks (CTTs) are a primary surface transportation carrier of hazardous materials (hazmat) in the United States and CTT rollover crashes are the leading cause of injuries and fatalities from hazmat transportation incidents. CTTs are susceptible to rollover crashes because of their size, distribution of weight, a higher center of gravity, and the surging and sloshing of liquid cargo during transportation. This study identified and quantified the effects of various factors on the probability of rollover and release of hazmat in traffic crashes where a CTT was involved. METHOD: Bayesian Model Averaging (BMA)-based logistic regression models were estimated with rollover and hazmat release as the binary response variables, and crash, truck, roadway, environment, and driver characteristics as the explanatory variables. 2010-2016 police-reported CTT-involved crash data from Nebraska and Kansas was utilized. Receiver Operating Characteristic (ROC) curves confirmed appropriateness of the modeling approach for inference and prediction on the crash dataset. RESULTS: CTTs are more likely to rollover in crashes while turning and changing lanes relative to going straight; side impacts (side collisions) and severe crosswinds increased the likelihood of rollovers; tractor and semi-trailer body style decreased the probability of rollover, while truck tractors are more prone to rollovers; collisions with fixed objects and higher posted speeds increased the rollover probability; rollovers and intersection crash locations increased the likelihood of hazmat release. CONCLUSIONS: The findings can assist stakeholders (policy-makers, private shippers, and CTT drivers) in restricting CTTs' operations for safety; scheduling, routing, and fleet planning; and low-level decision-making (e.g., emergency stopping or local routing). Practical Applications: This study identified and quantified the effects of different factors on the conditional probability of rollover and release of hazmat in CTT-involved crashes. The findings may assist stakeholders in decision-making towards safe operations of CTTs for transportation of hazmat.

Field effectiveness evaluation of advanced driver assistance systems.

OBJECTIVE: Advanced driver assistance systems (ADAS) are a class of vehicle technologies designed to increase safety by providing drivers with timely warnings and autonomously intervening to avoid hazardous situations. Though laboratory testing suggests that ADAS technologies will greatly impact crash involvement rates, real-world evidence that characterizes their effectiveness is still limited. This study evaluates and quantifies the association of ADAS technologies with the likelihood of a moderate or severe crash for new-model BMWs in the United States. METHODS: Vehicle ADAS option information for the cohort of model year 2014 and later BMW passenger vehicles sold after January 1, 2014 (n = 1,063,503), was coded using VIN-identified options data. ADAS technologies of interest include frontal collision warning with autonomous emergency braking, lane departure warning, and blind spot detection. BMW Automated Crash Notification system data (from January 2014 to November 2017) were merged with vehicle data by VIN to identify crashed vehicles (n = 15,507), including date, crash severity (delta V), and area of impact. Using Cox proportional hazards regression modeling, the study calculates the adjusted hazard ratio for crashing among BMW passenger vehicles with versus without ADAS technologies. The adjusted percentage reduction in moderate and severe crashes associated with ADAS is interpreted as one minus the hazard ratio. RESULTS: Vehicles equipped with both autonomous emergency braking and lane departure warning were 23% less likely to crash than those not equipped (hazard ratio [HR] = 0.77; 95% confidence interval [CI], 0.73-0.81), controlling for model year, vehicle size and body type. Autonomous emergency braking and lane departure warning generally occur together, making it difficult to tease apart their individual effects. Blind spot detection was associated with a 14% reduction in crashes after controlling for the presence of autonomous emergency braking and lane departure warning (HR =0.86; 95% CI, 0.744-0.99). Differences were observed by vehicle type and crash type. The combined effect of autonomous emergency braking and lane departure warning was greater in newer model vehicles: Equipped vehicles were 13% less likely to crash (HR =0.87; 95% CI, 0.79-0.95) among 2014 model year vehicles versus 34% less likely to crash (HR =0.66; 95% CI, 0.57-0.77) among 2017 model year vehicles. CONCLUSION: This robust cohort study contributes to the growing evidence on the effectiveness of ADAS technologies.

Ten-year review of BMW Accident Research Program-investigated cases.

OBJECTIVE: Over the past 10 years, the BMW Accident Research Program (ARP) has investigated how and why occupants are injured in motor vehicle crashes by reconstructing the crash. This research discusses the 2006-2017 ARP case study methodology and comprehensively describes the cases investigated over the past decade. METHODS: Accident research program cases are selected according to emerging trends and issues identified by BMW. Driver interviews, inspection approvals, police reports, and medical records are obtained. ARP case investigations involve a multidisciplinary team of engineers, automobile crash experts, and a trauma team. For each case, the team reconstructs the crash and explores in detail the crash characteristics, injury outcomes, as well as case significance and countermeasures that could have prevented the crash or mitigated the severity of the crash or injuries sustained. RESULTS: The ARP investigated 476 BMW-involved crashes between 2006 and 2017 in the United States. The majority of the crash investigations involved a frontal crash (55%). The other crash types included rollover (17%), nearside (13%), farside (9%), and rear crashes (5%). Crash characteristics such as roadway departure (26%), fatality (8%), elderly (>65 years old) occupant crashes (7%), crashes preceded by a medical event (4%), and crashes preceded by the driver falling asleep at the wheel (4%) are particularly informative in regards to advanced driver assistance systems (ADAS) role. The distribution of Maximum Abbreviated Injury Scale (MAIS) scores for the occupants were AIS 1 (23%), AIS 2 (33%), AIS 3 (10%), AIS 4 (4%), and AIS 5 + (7%); 16% of crashes involved uninjured occupants and 7% included no injury information. CONCLUSIONS: In-depth case reviews of moderate and severe crashes remain vital to determine emerging trends, patterns of crash injury, and analysis of driver assistance systems and other factors with potential to prevent the crash or limit severity.

Potentially preventable prehospital deaths from motor vehicle collisions.

BACKGROUND: In 2011, about 30,000 people died in motor vehicle collisions (MVCs) in the United States. We sought to evaluate the causes of prehospital deaths related to MVCs and to assess whether these deaths were potentially preventable. METHODS: Miami-Dade Medical Examiner records for 2011 were reviewed for all prehospital deaths of occupants of 4-wheeled motor vehicle collisions. Injuries were categorized by affected organ and anatomic location of the body. Cases were reviewed by a panel of 2 trauma surgeons to determine cause of death and whether the death was potentially preventable. Time to death and hospital arrival times were determined using the Fatality Analysis Reporting System (FARS) data from 2002 to 2012, which allowed comparison of our local data to national prevalence estimates. RESULTS: Local data revealed that 39% of the 98 deaths reviewed were potentially preventable (PPD). Significantly more patients with PPD had neurotrauma as a cause of death compared to those with a nonpreventable death (NPD) (44.7% vs. 25.0%, P =.049). NPDs were significantly more likely to have combined neurotrauma and hemorrhage as cause of death compared to PPDs (45.0% vs. 10.5%, P <.001). NPDs were significantly more likely to have injuries to the chest, pelvis, or spine. NPDs also had significantly more injuries to the following organ systems: lung, cardiac, and vascular chest (all P <.05). In the nationally representative FARS data from 2002 to 2012, 30% of deaths occurred on scene and another 32% occurred within 1 h of injury. When comparing the 2011 FARS data for Miami-Dade to the remainder of the United States in that year, percentage of deaths when reported on scene (25 vs. 23%, respectively) and within 1 h of injury (35 vs. 32%, respectively) were similar. CONCLUSIONS: Nationally, FARS data demonstrated that two thirds of all MVC deaths occurred within 1 h of injury. Over a third of prehospital MVC deaths were potentially preventable in our local sample. By examining injury patterns in PPDs, targeted intervention may be initiated.

Etiology of fatal thoracic aortic injuries: Secondary data analysis.

OBJECTIVES: Motor vehicle crashes remain a leading cause of death in the United States (US). Thoracic aortic dissection due to blunt trauma remains a major injury mechanism, and up to 90% of these injuries result in death on the scene. The objective of this study is to understand the modern risk factors and etiology of fatal thoracic aortic injuries in the current US fleet. METHODS: Using a unique, linked, Fatality Analysis Reporting System (FARS) and Multiple Cause of Death (MCOD) database from 2000-2010, 144,169 drivers over 16 years of age who suffered fatal injuries were identified. The merged database provides an unparalleled fidelity for identifying thoracic aortic injuries due to motor vehicle accidents. Thoracic aortic injuries were defined by ICD-10 codes S250. Univariate and multivariate logistic regression models for presence of any thoracic aortic injuries were fitted. Age, gender, BMI weight categories, vehicle class, model year, crash type/direction, severity of crash damage, airbag deployment location, and seatbelt use, fatal injury codes, and location of injury were considered. Odds ratios (OR) and corresponding 95% confidence intervals (95%CI) are calculated. RESULTS: There were 2953 deaths (2.10%) related to thoracic aortic injuries that met the inclusion criteria. Nearside crashes were associated with an increased odds (OR = 1.42, 1.1-1.83), while rollover crashes (OR =.44,.29-.66) were associated with a reduced odds of fatal thoracic aortic injury. Using backward selection on the full multivariate model, the only significant model effects that remained were vehicle type, crash type, body region, and injury type. CONCLUSIONS: The increased prevalence of fatal thoracic aortic injury in nearside crashes, increasing age, and vehicle type provide some insight into the current US fleet. Important factors, including model year, had significantly lower levels of the injury in univariate analysis, demonstrating the effect of safety improvements in newer model vehicles. Further study of this fatal injury is warranted, including comparisons of those who survive the injury.

Disaster complexity and the Santiago de Compostela train derailment.

This disaster complexity case study examines Spain's deadliest train derailment that occurred on July 24, 2013 on the outskirts of Santiago de Compostela, Galicia, Spain. Train derailments are typically survivable. However, in this case, human error was a primary factor as the train driver powered the Alvia train into a left curve at more than twice the posted speed. All 13 cars came off the rails with many of the carriages careening into a concrete barrier lining the curve, leading to exceptional mortality and injury. Among the 224 train occupants, 80 (36%) were killed and all of the remaining 144 (4%) were injured. The official investigative report determined that this crash was completely preventable.

The benefits and tradeoffs for varied high-severity injury risk thresholds for advanced automatic crash notification systems.

OBJECTIVES: The objectives of this study are to (1) characterize the population of crashes meeting the Centers for Disease Control and Prevention (CDC)-recommended 20% risk of Injury Severity Score (ISS)>15 injury and (2) explore the positive and negative effects of an advanced automatic crash notification (AACN) system whose threshold for high-risk indications is 10% versus 20%. METHODS: Binary logistic regression analysis was performed to predict the occurrence of motor vehicle crash injuries at both the ISS>15 and Maximum Abbreviated Injury Scale (MAIS) 3+ level. Models were trained using crash characteristics recommended by the CDC Committee on Advanced Automatic Collision Notification and Triage of the Injured Patient. Each model was used to assign the probability of severe injury (defined as MAIS 3+ or ISS>15 injury) to a subset of NASS-CDS cases based on crash attributes. Subsequently, actual AIS and ISS levels were compared with the predicted probability of injury to determine the extent to which the seriously injured had corresponding probabilities exceeding the 10% and 20% risk thresholds. Models were developed using an 80% sample of NASS-CDS data from 2002 to 2012 and evaluations were performed using the remaining 20% of cases from the same period. RESULTS: Within the population of seriously injured (i.e., those having one or more AIS 3 or higher injuries), the number of occupants whose injury risk did not exceed the 10% and 20% thresholds were estimated to be 11,700 and 18,600, respectively, each year using the MAIS 3+ injury model. For the ISS>15 model, 8,100 and 11,000 occupants sustained ISS>15 injuries yet their injury probability did not reach the 10% and 20% probability for severe injury respectively. Conversely, model predictions suggested that, at the 10% and 20% thresholds, 207,700 and 55,400 drivers respectively would be incorrectly flagged as injured when their injuries had not reached the AIS 3 level. For the ISS>15 model, 87,300 and 41,900 drivers would be incorrectly flagged as injured when injury severity had not reached the ISS>15 injury level. CONCLUSIONS: This article provides important information comparing the expected positive and negative effects of an AACN system with thresholds at the 10% and 20% levels using 2 outcome metrics. Overall, results suggest that the 20% risk threshold would not provide a useful notification to improve the quality of care for a large number of seriously injured crash victims. Alternately, a lower threshold may increase the over triage rate. Based on the vehicle damage observed for crashes reaching and exceeding the 10% risk threshold, we anticipate that rescue services would have been deployed based on current Public Safety Answering Point (PSAP) practices.

Influence of injury risk thresholds on the performance of an algorithm to predict crashes with serious injuries.

This paper presents methods to estimate crash injury risk based on crash characteristics captured by some passenger vehicles equipped with Advanced Automatic Crash Notification technology. The resulting injury risk estimates could be used within an algorithm to optimize rescue care. Regression analysis was applied to the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) to determine how variations in a specific injury risk threshold would influence the accuracy of predicting crashes with serious injuries. The recommended thresholds for classifying crashes with severe injuries are 0.10 for frontal crashes and 0.05 for side crashes. The regression analysis of NASS/CDS indicates that these thresholds will provide sensitivity above 0.67 while maintaining a positive predictive value in the range of 0.20.

Analysis of large truck rollover crashes.

OBJECTIVES: Close to a tenth of all large truck crashes result from rolling over during some maneuver. The aim of this study was to identify causes of these serious events as well as preventive measures that could be taken to reduce their number. METHODS: Detailed descriptions of 231 rollovers provided by field investigators were analyzed to identify causes. The descriptions addressed crash location, the nature of the crash, effect upon the vehicles involved, injuries and treatment, and contributing conditions. Causes were inferred from the nature of the crash. RESULTS: Almost half of the rollover crashes resulted from failing to adjust speed to curves, loads, brake condition, road surfaces, and intersections. A second major contributor involved lack of attention, including general inattention, misdirected attention, falling asleep, and distraction. The third major factor involved control errors, including oversteering, understeering, overcorrecting for errors, and minor control errors. The remainder were not driving errors and included those of other drivers, those occurring before the truck took to the road, and the condition of the vehicle before it was driven. CONCLUSIONS: Although they account for but a tenth of all large truck crashes, rollovers result from causes that are relatively unique to the vehicle and where it is driven. Programs could improve safety through the use of video to expose truck drivers to the situations causing rollovers, along with simulation allowing drivers to experience the consequences of errors without the harmful results of actual rollovers.

Analysis of large truck rollover crashes.

The Large Truck Crash Causation Study undertaken by the Federal Motor Carrier Safety Administration describes 239 crashes in which a truck rolled over. In-depth analysis revealed almost half resulted from failing to adjust speed to curves in the road, (mostly on-and off-ramps), the load being carried, condition of the brakes, road surface, and intersection conditions. A second major crash contributor involved attention: simply being inattentive, dozing or falling asleep, and distraction, all leading to situations where a sudden direction change resulted in a rollover. The third large crash contributor involved steering: over-steering to the point of rolling over, not steering enough to stay in lane, and overcorrecting to the point of having to counter-steer to remain on the road. Finally, loads are a frequent problem when drivers fail to take account of their weight, height or security, or when loading takes place before they are assigned. Instruction in rollover prevention, like most truck driver training, comes through printed publications. The use of video would help drivers recognize incipient rollovers while currently available simulation would allow drivers to experience the consequences of mistakes without risk.

Characteristics of crashes that increase the risk of serious injuries.

The advent of Automatic Crash Notification Systems (ACN) offers the possibility of immediately locating crashes and of determining the crash characteristics by analyzing the data transmitted from the vehicle. A challenge to EMS decision makers is to identify those crashes with serious injuries and deploy the appropriate rescue and treatment capabilities. The objective of this paper is to determine the crash characteristics that increase the risk of serious injury. Within this paper, regression models are presented which relate occupant, vehicle and impact characteristics to the probability of serious injury using the Maximum Abbreviated Injury Scale Level (MAIS). The accuracy of proposed models were evaluated using National Automotive Sampling System/ Crashworthiness Data System (NASS/CDS) and Crash Injury Research and Engineering Network (CIREN) case data. Cumulatively, the positive prediction rate of models identifying the likelihood of MAIS3 and higher injuries was 74.2%. Crash mode has a significant influence of injury risk. For crashes with 30 mph deltaV, the risk of MAIS3+ injury for each mode is 38.9%, 83.8%, 47.8% and 19.9% for frontal, near side, far side and rear impact crashes, respectively. In addition to deltaV, a number of crash variables were identified that assist in the accurate prediction of the probability of MAIS 3+ injury. These variables include occupant age, partial ejection, safety belt usage, intrusion near the occupant, and crashes with a narrow object. For frontal crashes, added crash variables include air bag deployment, steering wheel deformation, and multiple impact crashes. The quantitative relationship between each of these crash variables and injury risk has been determined and validated by regression analysis based on NASS/CDS and CIREN data.