The effectiveness of safety belts in preventing fatalities
Abstract
The effectiveness of safety belts in preventing fatalities to drivers and right front passengers is estimated by applying the double pair comparison method to 1974 or later model year cars coded in the Fatal Accident Reporting System. The method focuses on “subject” occupants (drivers or right front passengers) and “other” occupants (any except the subject occupant). Fatality risks to belted and unbelted subject occupants are compared using the other occupant to estimate exposure. In this study, drivers and right front passengers are subject occupants; choosing other occupants differing in age, seating positions, and belt use, generated 46 essentially independent estimates of safety belt effectiveness. The weighted average and standard error of these is (41 ± 4)%. This finding agrees with the 40%–50% range reported in a recent major review and synthesis by the National Highway Traffic Safety Administration. Combining this with the present determination gives (43 ± 3)%; that is, if all presently unbelted drivers and right front passengers were to use the provided three point lap/shoulder belt, but not otherwise change their behavior, fatalities to this group would decline by (43 ± 3)%.
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Emerging trends and influential outsiders of transportation science
2023, Transportation LettersFifty years of evolution of transportation research is revisited based on bibliometric indicators of nearly 50,000 articles, the collective publication of all transportation journals. A multitude of objective indicators all consistently determined four major divisions in the field: (i) network analysis and traffic flow, (ii) economics of transportation and logistics, (iii) travel behaviour, and (iv) road safety. Trending themes of research within the abovementioned divisions respectively are: (i) macroscopic fundamental diagram and public transport network design, (ii) nil (no distinct trending topic), (iii) land-use, active transportation, residential self-selection, travel experience/satisfaction, social exclusion and transport/spatial equity, and (iv) statistical modelling of road accidents. Furthermore, clusters of research related to topics of (a) shared mobility, (b) electric mobility, and (c) autonomous mobility constitute trending topics that are each a cross between multiple divisions of the field. These outcomes document major directions to which the transportation research is headed. Additional outcome is determination of influential outsiders, seminal articles published by non-transportation journals that have proven instrumental in the development of transportation science.
Protective devices such as seat belts and airbags have improved the safety of motor vehicle occupants, but limited data suggest they may be associated with increased blunt bowel (small bowel or colon) injuries (BI). Unfortunately, this risk is unquantified.
We analyzed the National Trauma Data Bank (2017-2019) using ICD-10 codes to identify adult motor vehicle occupants with BI who underwent surgical repair. We used logistic regression modeling to compare the risk of undergoing surgical repair for BI after using a protective device.
Of 2,848,592 injured patients, 475,546 (16.7%) were motor vehicle occupants. Only 1.2% (n = 5627/475,546) of patients underwent a bowel repair or resection. Using a seat belt only was associated with an adjusted OR of 2.09 (95% CI 1.91, 2.28) for undergoing a bowel repair/resection when adjusting for Injury Severity Score (ISS) and age. Airbag deployment without a seat belt had an adjusted OR of 1.46 (95% CI 1.31, 1.62), while both devices combined conferred an OR of 3.27 (95% CI 3.02, 3.54). However, using a seat belt was protective against death with an OR of 0.50 (95% CI 0.48, 0.53), adjusted for age, sex, Charlson Comorbidity Score, and ISS.
Seat belts and airbags are essential public health safety interventions and protect against death in motor vehicle-associated injuries. However, patients involved in MVCs with airbag deployment or while wearing a seat belt are at an increased risk of bowel injury requiring surgery compared to unrestrained patients, despite these events being relatively uncommon.
Quantifying and recommending seat belt reminder timing using naturalistic driving video data
2022, Journal of Safety ResearchIntroduction: To better understand the timing of when people buckle their seat belt, an analysis of a naturalistic driving study was used. The study provided a unique perspective inside of the vehicle where the entire seat belt was visible from the time the driver entered the vehicle to one minute of driving forward or 32 kph. Method: Seat belt buckling behavior was identified for 30 drivers. An additional 10 drives for 13 of these drivers were identified for a seat belt sequencing, which identified the points when the vehicle was put into ignition, shifted, when vehicle movement began, and when the seat belt was buckled. The speed at belt closure was also identified. The timing from ignition to buckle and to shifting into forward gear were examined to identify the speed and appropriate timing for seat belt reminders. Results: The data show that drivers were buckled in over 92% of the 3,102 drives. In addition, in 70% of those total drives, the drivers were buckled before the vehicle began movement. Of greater interest for seat belt reminders/interlocks are those drives when drivers buckle after movement. When considering time from ignition to seat belt closure, the mean was 27.5 s. Because higher speeds are typically reached when traveling forward rather than reverse, it was important to know the time duration from shifting into drive to buckling. With this consideration, the mean to buckle dropped to 16.2 s. The mean speed at buckling when traveling forward was 15.3 kph. From the regression analysis, the input variables ‘Age,’ ‘Sex,’ ‘Weight,’ ‘Environment,’ and ‘Weather’ are significant contributors in predicting the log odds of a driver putting on seatbelt. Conclusions: With the understanding that higher speeds lead to an increased risk of injury and/or death and with the results of the analysis, a recommendation of a 30 s time from forward shift and a 25 kph (6.9 m/s) threshold for reminder systems should be implemented. The regression analysis also validates that most of the predicted seat belt buckling times are within 30 s. Practical Applications: This would reduce perception of nuisance alerts and protect the driver from higher speed unbuckled crashes. The seat belt buckling time prediction model also demonstrates good potential for developing tailored buckling warning system for different drivers.
Predicting multiple types of traffic accident severity with explanations: A multi-task deep learning framework
2022, Safety SciencePredicting traffic accident severity is essential for traffic accident prevention and vulnerable road user safety. Furthermore, the explainability of the prediction is crucial for practitioners to extract relevant risk factors and implement corresponding countermeasures. Most extant research ignores the property loss severity of traffic accidents and fails to predict different levels of death and property loss severity. Moreover, while the explainability of traditional models is easy to achieve, an explainable design of deep neural network (DNN) is extremely deficient in existing research. Few attempts that incorporate neural networks suffer from the lack of multiple hidden layers and the negligence of structural information when explaining predictions. In this study, we propose a multi-task DNN framework for predicting different levels of injury, death, and property loss severity. The multi-task and deep learning design enables a comprehensive and precise analysis of traffic accident severity. Unlike many black-box DNN algorithms, our framework could identify key factors that cause the three types of traffic accident severity via layer-wise relevance propagation, which generates explanations based on the structure and weights of DNN. Based on the experiments conducted using Chinese traffic accident data, our proposed model predicts traffic accident severity risks with good accuracy and outperforms state-of-the-art methods. Furthermore, the case studies show that the key factors provided by our framework are more reasonable and informative than the explanations provided by baseline methods. Our model is the first multi-task learning model and the first DNN-based model for traffic accident severity prediction to the best of our knowledge.
Structural anatomy and temporal trends of road accident research: Full-scope analyses of the field
2021, Journal of Safety ResearchIntroduction: Scholarly research on road accidents over the past 50 years has generated substantial literature. We propose a robust search strategy to retrieve and analyze this literature. Method: Analyses was focused on estimating the size of this literature and examining its intellectual anatomy and temporal trends using bibliometric indicators of its articles. Results: The size of the literature is estimated to have exceeded N = 25,000 items as of 2020. At the highest level of aggregation, patterns of term co-occurrence in road accident articles point to the presence of six major divisions: (i) law, legislation & road trauma statistics; (ii) vehicular safety technology; (iii) statistical modelling; (iv) driving simulator experiments of driving behavior; (v) driver style and personality (social psychology); and (vi) vehicle crashworthiness and occupant protection division. Analyses identify the emergence of various research clusters and their progress over time along with their respective influential entities. For example, driver injury severity ” and crash frequency show distinct characteristics of trending topics, with research activities in those areas notably intensified since 2015 Also, two developing clusters labelled autonomous vehicle and automated vehicle show distinct signs of becoming emerging streams of road accident literature. Conclusions: By objectively documenting temporal patterns in the development of the field, these analyses could offer new levels of insight into the intellectual composition of this field, its future directions, and knowledge gaps. Practical Applications: The proposed search strategy can be modified to generate specific subsets of this literature and assist future conventional reviews. The findings of temporal analyses could also be instrumental in informing and enriching literature review sections of original research articles. Analyses of authorships can facilitate collaborations, particularly across various divisions of accident research field.
Impact speed and the risk of serious injury in vehicle crashes
2020, Accident Analysis and PreventionThe current guiding philosophies in road safety have stated aims of zero deaths and serious injuries. Speed has previously been highlighted as a key factor in the outcome of a crash but the literature to date has yet to provide a robust relationship between impact speed and the risk of serious injury for crashes other than pedestrian crashes. This study aimed to determine the relationship between impact speed and the risk of serious injury in light vehicle crashes.
Crash data from the US based National Automotive Sampling System – Crashworthiness Data System collected from 2011 to 2015 were used in the analysis when there was a known impact speed from an event data recorder (EDR) and a known injury outcome. The analysis was conducted at the vehicle level. Data from a total of 1274 vehicles were used in logistic regressions, with the presence or absence of a serious injury as the binary dependent variable, and impact speed as the continuous independent variable. Individual risk curves were produced for front, side, rear and head on impacts. Impact speed was found to have a highly significant positive relationship to risk of serious injury for all impact types examined. The risk of serious injury reaches 1% at 28 km/h for head on impacts, 51 km/h for side impacts, 64 km/h for front impacts, and 67 km/h for rear impacts. The results emphasise the importance of measures that reduce impact speeds, be they road designs, vehicle technologies or enforced speed limit reductions, and highlight the need to prevent head on impacts.