Percent Of Crashes Research Articles

Advancing investigation of automated vehicle crashes using text analytics of crash narratives and Bayesian analysis

Vehicle automation, manifested in self-driving cars, promises to provide safe mobility by reducing human errors. While the testing of automated vehicles (AVs) has improved their performance in recent years, automation technologies face challenges such as uncertainty of safety impacts in mixed traffic with human-driven vehicles. This study aims to examine the gaps in AV safety performance and identify what will be required on a preferential basis for AVs to guarantee an acceptable level of safety performance, especially in mixed traffic, by conducting a thorough analysis of crashes involving levels 2–3 AVs. Based on 260 AV collision reports from California from 2019 to 2021, this study extracts crash-related variables from crash records in a standardized form, crash locations, and, notably, crash narratives reported by AV manufacturers. This study untangles the complex interrelationships among pre-crash conditions, AV driving modes, crash types, and crash outcomes by applying a path-analytic framework with the frequentist and Bayesian approaches. Results show that 51.9 percent of crashes were rear-ends. Particularly, AVs become more vulnerable to rear-end collisions in the automated driving mode than in the conventional mode, given a crash. Additionally, the automated driving mode would not significantly affect the chance of a sideswipe collision, injury, or AV damage levels. Another interesting finding is that manual disengagement is more likely to happen when an AV interacts with a transit vehicle right before a crash occurs while having a negative relationship with injury crashes. Moreover, to reduce injury crashes, AVs would need more thorough testing to adapt to the critical roadway and infrastructure features such as intersections, ramps, and slip lanes; and roadway infrastructure would require improvements to support transportation automation. The risk factors identified in this study can be considered in AV safety assessment scenarios and future operations of mixed traffic. This study demonstrates that AV crash narrative data can be leveraged to improve knowledge of AV safety in mixed traffic.

Crash Classification by Congestion Type for Highways

Effective management of highway networks requires a thorough understanding of the conditions under which vehicular crashes occur. Such an understanding can and should inform related operational and resource allocation decisions. This paper presents an easily implementable methodology that can classify all reported crashes in terms of the operational conditions under which each crash occurred. The classification methodology uses link-based speed data. Unlike previous secondary collision identification schemes, it neither requires an a priori identification of the precipitating incident nor definition of the precipitating incident’s impact area. To accomplish this objective, the methodology makes use of a novel scheme for distinguishing between recurrent and non-recurrent congestion. A 500-crash case study was performed using a 274 km section of the I-40 in North Carolina. Twelve percent of the case study crashes were classified as occurring in non-recurrent congestion. Thirty-seven percent of the crashes in non-recurrent congestion classified were identified within unreported primary incidents or crashes influence area. The remainder was classified as primary crashes occurring in either uncongested conditions (84%) or recurrent congestion (4%). The methodology can be implemented in any advanced traffic management system for which crash time and link location are available along with corresponding archived link speed data are available.

Potential Occupant Injury Reduction in the U.S. Vehicle Fleet for Lane Departure Warning–Equipped Vehicles in Single-Vehicle Crashes

Objective: Single-vehicle collisions involve only 10 percent of all occupants in crashes in the United States, yet these same crashes account for 31 percent of all fatalities. Along with other vehicle safety advancements, lane departure warning (LDW) systems are being introduced to mitigate the harmful effects of single-vehicle collisions. The objective of this study is to quantify the number of crashes and seriously injured drivers that could have been prevented in the United States in 2012 had all vehicles been equipped with LDW.Methods: In order to estimate the potential injury reduction benefits of LDW in the vehicle fleet, a comprehensive crash and injury simulation model was developed. The model's basis was 481 single-vehicle collisions extracted from the NASS-CDS for year 2012. Each crash was simulated in 2 conditions: (1) as it occurred and (2) as if the driver had an LDW system. By comparing the simulated vehicle's off-road trajectory before and after LDW, the reduction in the probability of a crash was determined. The probability of a seriously injured occupant (Maximum Abbreviated Injury Score [MAIS] 3+) given a crash was computed using injury risk curves with departure velocity and seat belt use as predictors. Each crash was simulated between 18 and 216 times to account for variable driver reaction, road, and vehicle conditions. Finally, the probability of a crash and seriously injured driver was summed over all simulations to determine the benefit of LDW.Results and Conclusions: A majority of roads where departure crashes occurred had 2 lanes and were undivided. As a result, 58 percent of crashes had no shoulder. LDW will not be as effective on roads with no shoulder as on roads with large shoulders. LDW could potentially prevent 28.9 percent of all road departure crashes caused by the driver drifting out of his or her lane, resulting in a 24.3 percent reduction in the number of seriously injured drivers.The results of this study show that LDW, if widely adopted, could significantly mitigate a harmful crash type. Larger shoulder width and the presence of lane markings, determined by manual examination of scene photographs, increased the effectiveness of LDW. This result suggests that highway systems should be modified to maximize LDW effectiveness by expanding shoulders and regularly painting lane lines.

Light-Vehicle Occupancy and Severe Injury by Vehicle and Crash Type

Purpose: This study investigated the number of occupants involved and severely injured in light-vehicle crashes by vehicle and crash type. Methods: 1994–2010 NASS-CDS data were analyzed to estimate the risk of severely injured occupants involved in motor vehicle crashes. Crashes were grouped by front, side, rear, and rollovers. The effect of occupancy and vehicle type was investigated. Light vehicles were less than 4536 kg with model year 1994+. The annual number of Maximum Abbreviated Injury Scale (MAIS) 4+F injuries was estimated and the risk was determined using all occupants with known injury (MAIS 0–6+F). Results: There were 21,869 MAIS 4+F injuries annually, with 62.7 percent involving passenger cars, 15.9 percent sport utility vehicles (SUVs), 15.3 percent trucks, and 6.1 percent vans. In 36.6 percent of van crashes, MAIS 4+F occurred when there were 4+ occupants and 13.0 percent with 7+ occupants in the vehicle. Overall, 12.8 percent of severely injured were in crashes involving 4+ occupants and 1.4 percent with 7+ occupants. In addition, 30.1 percent of MAIS 4+F occurred in rollovers and only 5.8 percent of crashes were rollovers. Rollovers had the highest injury risks, irrespective of the number of occupants. There was a trend for an increased fraction of injury in frontal crashes and a lower fraction in rollovers. There were high risks in side and rear crashes with 5+ occupants. Conclusions: On average, there were 1.35 occupants involved in a crash per vehicle. Severely injured occupants were uncommon in 5+ occupancy vehicles. There was a trend for increased injury risks with occupancy. The risk was 1.65 times higher in vehicles with 7+ occupants compared to those with a driver only.

Bicycle Crashes in Different Riding Environments in the Australian Capital Territory

Introduction: Cyclists are increasingly overrepresented in traffic crash casualties in Australia. There is evidence that better cycling infrastructure increases participation, but whether it reduces the numbers of injured cyclists is less clear. This study examined injury outcomes of crashes in different cycling environments. Methods: Adult cyclists injured on- and off-road were recruited from emergency departments from November 2009 to May 2010 in the Australian Capital Territory. Eligible participants (n = 313/372, 84.1%) were interviewed and their injury self-reports were corroborated with medical records where available. Participants who had crashed in transport-related areas (n = 202, 64.5%) are the focus of this article. Results: Participants had crashed in traffic (39.1%), in cycle lanes (7.9%), on shared paths (36.1%), and on footpaths (16.8%). Based on average weekly traffic counts, the crash involvement rate per 1000 cyclists was 11.8 on shared paths compared to 5.8 on cycle lanes. Over half of the participants (52.0%) were injured in single-vehicle bicycle crashes. The remainder involved other road users, including motor vehicles (20.8%), other bicycles (18.8%), pedestrians (6.4%), and animals (2.0%). Pedestrians were involved in 16.4 percent of crashes on shared paths. Minor injuries (Abbreviated Injury Scale [AIS] 1) were sustained by 58.4 percent of cyclists, moderately severe injuries (AIS 2) were sustained by 36.1 percent of cyclists, and 5.4 percent of cyclists were seriously injured (AIS 3+). The average treatment required was 1.8 days with 7.5 days off work and cost to the cyclist of $869 excluding medical treatment. Cyclists who crashed on shared paths or in traffic had higher injury severity scores (ISS; 4.4, 4.0) compared to those in cycle lanes or on footpaths (3.3, 3.4) and required more treatment days (2.8, 1.7 versus 0.0, 0.2). Conclusions: Fewer cyclists were injured in on-road cycle lanes than in other cycling environments, and a high proportion of injuries were incurred on shared paths. This study highlights an urgent need to determine appropriate criteria and management strategies for paths classified as suitable for shared or segregated usage. Supplemental materials are available for this article. Go to the publisher's online edition of Traffic Injury Prevention to view the supplemental files.

Bicyclist–Bicyclist Crashes—A Medical and Technical Crash Analysis

Background: The purpose of this study was to analyze the actual injury situation of bicyclists focusing on accidents involving more than one bicyclist. A medical and technical analysis was performed as a basis for preventive measures. Methods: Technical and medical data were collected at the scene, shortly after the accident. Technical analysis included speed at crash, type of collision, impact angle, environment, lane used, and relative velocity. Medical analysis included injury patterns and severity (Abbreviated Injury Scale [AIS], Injury Severity Score [ISS]). Results: Five hundred seventy-eight injured bicyclists in 289 accidents from 1999 to 2008 were included into the study. Sixty-one percent were male (n = 350) and 39 percent were female (n = 228). Sixty-seven percent ranged between 18 and 64 years of age, 12 percent each between 13 and 17 years of age and older than 65 years, 8 percent between 6 and 12 years, and 1 percent between 2 and 5 years. Ninety-two percent of crashes took place in urban areas and 8 percent in rural areas. Ninety-seven percent of crashes occurred in dry conditions and 3 percent in wet conditions. Eighty-three percent of all accidents occurred during the daytime, 10 percent at night, and 7 percent at dawn. The helmet use rate was only 7.5 percent for all involved bicyclists. The mean Abbreviated Injury Scale (AIS) score was 1.31. Conclusion: The prevalence of bicycle-to-bicycle crashes is high. Most of these accidents occur in urban areas. Bicyclists should be considered as minimally or unprotected road users, with an unsatisfactorily low rate of helmet use. Though the average level and patterns of injuries is moderate, most of the severe injuries involved the head and extremities. However, there was no significant correlation between frequent helmet use and sustained injuries to the head of major AIS.

Severe-to-Fatal Injury Risks in Crashes With Two Front-Seat Occupants by Seat Belt Use

Purpose: This study investigates the risk of severe-to-fatal injury (Maximum Abbreviated Injury Score, MAIS 4+F) in crashes with two front-seat occupants. It determines the relative risk of injury in the same crash by belt use and seating position. Methods: 1993–2008 NASS-CDS was analyzed for crashes with occupants in both front-outboard seats. The effect of belt use was investigated for the driver and passenger. Light vehicles were included with model year 1990+. Injury severity was subdivided into MAIS 0–2 and 4+F in NASS-CDS to compare no-to-moderate injury with severe-to-fatal injury. Standard errors were calculated in SAS and the z-test was used to determine the significance of differences in risk. Relative risks were determined by seat belt use and seating position; odds ratios were determined for one or both occupants being severely injured. Results: In 76.7 percent of the MAIS 4+F crashes, either the driver (32.4%) or passenger (44.3%) was severely injured, rather than both occupants (23.3%). When both were belted, one occupant was severely injured in 86.5 percent of crashes. When both were unbelted, both occupants were severely injured in 68.9 percent of the crashes. Both occupants were belted in 74.7 percent of the cases and unbelted in 18.6 percent. In 6.7 percent of crashes, either the driver (4.4%) or passenger (2.3%) was unbelted when the other was belted. The highest risk occurred with an unbelted driver and belted passenger (4.98 ± 0.73% vs. 1.97 ± 0.38%, z = 3.65, p < .05). When both occupants were belted, the driver and passenger injury risk was similar (0.459 ± 0.098% vs. 0.449 ± 0.047%, z = 0.10, ns). Conclusions: In crashes with two front occupants, typically one occupant was severely injured, not both. Overall, the odds ratio was 3.28 for one compared to two occupants being severely injured; and, risks vary by seat belt use and seating position. The highest relative risk for unbelted versus belted occupants was 9.22 when both occupants were severely injured in the same crash.

Looming Detection Among Drivers of Different Ages

Rear-end collisions make up about 25 percent of all automobile crashes, 27 percent of crashes resulting in injuries, and 5 percent of all fatal car crashes in the U.S. We believe that one factor in these crashes is that some people are less able to detect when they are closing in on another car, and that this ability can be measured. Thirty participants in three age groups participated in this study. Testing included the detection of size change (looming), estimation of change in looming, manual lateral tracking, synthetic tail-following, and low-fidelity driving. Participants' visual and cognitive abilities were also tested. Results indicated that the looming detection task possessed a respectable amount of retest reliability and yielded a measure that provided stable data after one session. The results further indicated that the loom tests showed minimal relationships with other temporal visual functions, indicating that looming detection is a separate function. Finally, a clear relationship between age and looming detection was shown, but neither gender nor handedness appeared to have an effect.

Definition of the Light Vehicle Off-Roadway Crash Problem for the Intelligent Vehicle Initiative

Off-roadway crashes involving light vehicles (passenger cars, sport utility vehicles, vans, and pickup trucks) were analyzed based on the 1998 General Estimates System crash database to support the development of countermeasure systems as part of the U.S. Department of Transportation’s Intelligent Vehicle Initiative. An off-roadway crash occurs when a vehicle in transport departs the road due to loss of control or crossing the edge of the roadway. Approximately 924,000 such crashes occurred in the United States in 1998. These crashes are grouped into six distinct precrash scenarios (3 × 2 matrix) based on vehicle movements (going straight, negotiating a curve, or initiating a maneuver) and critical events (departed roadway edge or lost control). These scenarios are described statistically in terms of their physical setting, which consists of the roadway type, land use, relation to junction, number of travel lanes, and speed limit. Moreover, factors that might have contributed to the cause of these crashes are identified. According to this analysis, 85 percent of these off-roadway crashes occurred on nonfreeways, 64 percent in rural areas, 83 percent away from junctions, 60 percent on two lanes of travel, and 62 percent below the 89-km/h (55-mph) speed limit. In addition, speeding and alcohol were reported in 25 and 20 percent of these crashes, respectively. Finally, inclement environmental conditions or driver inattention or distraction might have contributed to about 42 percent of these crashes.