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The Advanced Automatic Crash Notification (AACN) system needs to predict injury accurately, to provide appropriate treatment for seriously injured occupants involved in motor vehicle crashes. This study investigates the possibility of improving the accuracy of the AACN system, using vehicle deformation parameters in car-to-car (C2C) side impacts.This study was based on car-to-car (C2C) crash data from NASS-CDS, CY 2004-2014. Variables from Kononen's algorithm (published in 2011) were used to build a "base model" for this study. Two additional variables, intrusion magnitude and max deformation location, are added to Kononen's algorithm variables (age, belt usage, number of events, and delta-v) to build a "proposed model." This proposed model operates in two stages: In the first stage, the AACN system uses Kononen's variables and predicts injury severity, based on which emergency medical services (EMS) is dispatched; in the second stage, the EMS team conveys deformation-related information, for accurate prediction of serious injury.Logistic regression analysis reveals that the vehicle deformation location and intrusion magnitude are significant parameters in predicting the level of injury. The percentage of serious injury decreases as the deformation location shifts away from the driver sitting position. The proposed model can improve the sensitivity (serious injury correctly predicted as serious) from 50% to 63%, and overall prediction accuracy increased from 83.5% to 85.9%.The proposed method can improve the accuracy of injury prediction in side-impact collisions. Similar opportunities exist for other crash modes also.

Objective: Road crashes are increasing every year in low- and middle-income countries compared to high-income countries, which show decreasing trends. One theory may be because of differences in enforcement of laws, vehicle safety, road standards, and many other factors. A detailed review was made of 5 death trends (total number of deaths/100,000 population and percentages of 4-wheeler, pedestrian, motorized 2/3-wheeler, bicyclist) and the underlying patterns in different countries and regions across the world. This review was done to understand the main reasons for the variances to focus on efficient improvement strategies related to future vehicle and road safety issues. Methods: A self-organizing map (SOM) technique is used to map the nonlinear relationships among different attributes. Overall, 176 countries with 44 attributes were considered from the World Health Organization's (WHO) Global Status Report on Road Traffic Crashes database. Of the 44 attributes, 5 related to accident deaths were considered as response attributes. Results: Very distinct and unique cause–effect patterns for 3 clusters were observed from SOM results. High-income countries were found to have a lower total number of deaths/100,000 population. One theory espouses that this was due to those countries maintaining high vehicle standards and policies, whereas it was quite a different situation for low-income countries. Even though helmet laws were available in Association of South East Asian Nations + 6 (ASEAN + 6) countries, the percentage of 2/3-wheeler deaths may be higher due a lack of enforcement of those laws. Percentage of deaths involving 4-wheeler vehicles was higher in certain countries in the Persian Gulf, the United States, Australia, and New Zealand. This may be due to the fact that these countries have a number of rural areas where drivers drive at highway speeds versus some lower income countries with more urban areas where drivers operate vehicles at slower speeds. Countries with a lack of laws protecting bicyclists saw higher death percentages among bicyclists. The percentage of bicyclist deaths was also higher in areas with no helmet requirement and no investment in infrastructure improvements. The percentage of pedestrian deaths was high when there was no policy to separate road users, especially in low-income African countries. Deaths can be reduced by enforcement of laws and practicing good safety standards related to road traffic. Conclusions: Future vehicle and road safety strategies should consider using advanced statistical tools like SOM to advance safety. Based on a triple-layer (vehicle, infrastructure, and society) safety approach, strict regulations and enforcement are effective measures to reduce fatalities in low- and middle-income countries. On the other hand, introduction of more advanced vehicle technologies will be useful in countries with high gross national incomes (GNIs). Hence, a proper balance of different countermeasures based on economic zones could be effective to reduce total world traffic casualties.

A comprehensive analysis is performed to evaluate the effect of weight, height and body mass index (BMI) of occupants on side impact injuries at different body regions. The accident dataset for this study is based on the National Automotive Sampling System-Crashworthiness Data System (NASS-CDS) for accident year 2000-08. The mean BMI values for driver and front passenger are estimated from all types of crashes using NASS database, which clearly indicates that mean BMI has been increasing over the years in the USA. To study the effect of BMI in side impact injuries, BMI was split into three groups namely (1) thin (BMI21), (2) normal (BMI 24-27), (3) obese (BMI30). For more clear identification of the effect of BMI in side impact injuries, a minimum gap of three BMI is set in between each adjacent BMI groups. Car model years from MY1995-1999 to MY2000-2008 are chosen in order to identify the degree of influence of older and newer generation of cars in side impact injuries. Impact locations particularly side-front (F), side-center (P) and side-distributed (Y) are chosen for this analysis. Direction of force (DOF) considered for both near side and far side occupants are 8 o'clock, 9 o'clock, 10 o'clock and 2 o'clock, 3 o'clock and 4 o'clock respectively. Age60 years is also one of the constraints imposed on data selection to minimize the effect of bone strength on the occurrence of occupant injuries. AIS2+ and AIS3+ injury risk in all body regions have been plotted for the selected three BMI groups of occupant, delta-V 0-60kmph, two sets (old and new) of car model years. The analysis is carried with three approaches: (a) injury risk percentage based on simple graphical method with respect to a single variable, (b) injury distribution method where the injuries are marked on the respective anatomical locations and (c) logistic regression, a statistical method, considers all the related variables together. Lower extremity injury risk appears to be high for thin BMI group. It is found that BMI does not have much influence on head injuries but it is influenced more by the height of the occupant. Results of logistic analysis suggest that BMI, height and weight may have significant contribution towards side impact injuries across different body regions.