Characteristics of rear-end crashes involving passenger vehicles with automatic emergency braking

Abstract

Objectives: Automatic emergency braking (AEB) is a proven effective countermeasure for preventing front-to-rear crashes, but it has not yet fully lived up to its estimated potential. This study identified the types of rear-end crashes in which striking vehicles with AEB are overrepresented to determine whether the system is more effective in some situations than in others, so that additional opportunities for increasing AEB effectiveness might be explored.

Methods: Rear-end crash involvements were extracted from 23?U.S. states during 2009–2016 for striking passenger vehicles with and without AEB among models where the system was optional. Logistic regression was used to examine the odds that rear-end crashes with various characteristics involved a striking vehicle with AEB, controlling for driver and vehicle features.

Results: Striking vehicles were significantly more likely to have AEB in crashes where the striking vehicle was turning relative to when it was moving straight (odds ratio [OR]?=?2.35; 95% confidence interval [CI], 1.76, 3.13); when the struck vehicle was turning (OR = 1.66; 95% CI, 1.25, 2.21) or changing lanes (OR = 2.05; 95% CI, 1.13, 3.72) relative to when it was slowing or stopped; when the struck vehicle was not a passenger vehicle or was a special use vehicle relative to a car (OR = 1.61; 95% CI, 1.01, 2.55); on snowy or icy roads relative to dry roads (OR = 1.83; 95% CI, 1.16, 2.86); or on roads with speed limits of 70+ mph relative to those with 40 to 45?mph speed limits (OR = 1.49; 95% CI, 1.10, 2.03). Overall, 25.3% of crashes where the striking vehicle had AEB had at least one of these overrepresented characteristics, compared with 15.9% of strikes by vehicles without AEB.

Conclusions: The typical rear-end crash occurs when 2 passenger vehicles are proceeding in line, on a dry road, and at lower speeds. Because atypical crash circumstances are overrepresented among rear-end crashes by striking vehicles with AEB, it appears that the system is doing a better job of preventing the more typical crash scenario. Consumer information testing programs of AEB use a test configuration that models the typical rear-end crash type. Testing programs promoting good AEB performance in crash circumstances where vehicles with AEB are overrepresented could guide future development of AEB systems that perform well in these additional rear-end collision scenarios.     

The use of proactive risk management to reduce emergency service vehicle crashes among firefighters

IntroductionEmergency service vehicle crashes (ESVCs), including rollovers and collisions with other vehicles and fixed objects, are a leading cause of death among U.S. firefighters. Risk management (RM) is a proactive intervention to identifying and mitigating occupational risks and hazards. The goal of this study was to assess the effect of RM in reducing ESVCs. Methods: Three fire departments (A, B and C), representing urban and suburban geographies, and serving medium to large populations, participated in facilitated RM programs to reduce their ESVCs. Interventions were chosen by each department to address their department-specific circumstances and highest risks. Monthly crash rates per 10,000 calls were calculated for each department an average of 28 months before and 23 months after the start of the RM programs. Interrupted time series analysis was used to assess the effect of the RM programs on monthly crash rates. Poisson regression was used to estimate the number of crashes avoided. Economic data from Department A were analyzed to estimate cost savings. Results: Department A had a 15.4% (P = 0.30) reduction in the overall monthly crash rate immediately post-RM and a 1% (P = 0.18) decline per month thereafter. The estimated two-year average cost savings due to 167 crashes avoided was $253,100 (95%CI= $192,355 – $313,885). Department B had a 9.7% (P = 0.70) increase in the overall monthly crash rate immediately post-RM and showed no significant changes in their monthly crash rate. Department C had a 28.4% (P = 0.001) reduction in overall monthly crash rate immediately post-RM and a 1.2% (P = 0.09) increase per month thereafter, with an estimated 122 crashes avoided. Conclusions: RM programs have the potential to reduce ESVCs in the fire service and their associated costs; results may vary based on the interventions chosen and how they are implemented. Practical applications: Risk management may be an effective and broadly implemented intervention to reduce ESVCs in the US fire service.     

餐厨垃圾发酵制备生物燃料丁醇的研究

餐厨垃圾含丰富有机质,是潜在的廉价优质生物质资源;丁醇是继乙醇后的一种极具潜力的新型生物燃料。探索了以餐厨垃圾糖化液为原料,通过微生物发酵制取生物燃料丁醇。首先从实验室现有5株产丁醇梭菌中,优选出Clostridium beijerinckii NCIMB 8052作为丁醇生产菌,并考察在未添加任何营养物质,且不调节pH的条件下(简称非调控状态),以糖化液作为底物进行丁醇发酵的可行性。结果表明:非调控状态发酵的丁醇产量仅为5.96 g/L,并产生"酸崩"抑制;添加0.3%(w/V)Ca CO_3提高了糖化液pH缓冲能力,可解决"酸崩"问题,并将丁醇产量、总溶剂产量及丁醇生产速率分别提高了57.8%、53.1%和90.6%。这证明餐厨垃圾做为原料用于燃料丁醇生产是完全可行的。     

Risk factors affecting injury severity determined by the MAIS score

Objective: Traffic crashes result in a loss of life but also impact the quality of life and productivity of crash survivors. Given the importance of traffic crash outcomes, the issue has received attention from researchers and practitioners as well as government institutions, such as the European Commission (EC). Thus, to obtain detailed information on the injury type and severity of crash victims, hospital data have been proposed for use alongside police crash records. A new injury severity classification based on hospital data, called the maximum abbreviated injury scale (MAIS), was developed and recently adopted by the EC. This study provides an in-depth analysis of the factors that affect injury severity as classified by the MAIS score.

Method: In this study, the MAIS score was derived from the International Classification of Diseases. The European Union adopted an MAIS score equal to or greater than 3 as the definition for a serious traffic crash injury. Gains are expected from using both police and hospital data because the injury severities of the victims are detailed by medical staff and the characteristics of the crash and the site of its occurrence are also provided. The data were obtained by linking police and hospital data sets from the Porto metropolitan area of Portugal over a 6-year period (2006–2011). A mixed logit model was used to understand the factors that contribute to the injury severity of traffic victims and to explore the impact of these factors on injury severity. A random parameter approach offers methodological flexibility to capture individual-specific heterogeneity. Additionally, to understand the importance of using a reliable injury severity scale, we compared MAIS with length of hospital stay (LHS), a classification used by several countries, including Portugal, to officially report injury severity. To do so, the same statistical technique was applied using the same variables to analyze their impact on the injury severity classified according to LHS.

Results: This study showed the impact of variables, such as the presence of blood alcohol, the use of protection devices, the type of crash, and the site characteristics, on the injury severity classified according to the MAIS score. Additionally, the sex and age of the victims were analyzed as risk factors, showing that elderly and male road users are highly associated with MAIS 3+ injuries. The comparison between the marginal effects of the variables estimated by the MAIS and LHS models showed significant differences. In addition to the differences in the magnitude of impact of each variable, we found that the impact of the road environment variable was dependent on the injury severity classification.

Conclusions: The differences in the effects of risk factors between the classifications highlight the importance of using a reliable classification of injury severity. Additionally, the relationship between LHS and MAIS levels is quite different among countries, supporting the previous conclusion that bias is expected in the assessment of risk factors if an injury severity classification other than MAIS is used.     

Characteristics and availability of fatal road-crash databases in 20 countries worldwide

INTRODUCTION: This study examined the characteristics and availability of fatal road-crash databases worldwide. METHOD: The study involved two parts. First, the major international road databases were briefly reviewed. Second, the national databases in 20 selected countries were examined. RESULTS: (a) the available international databases of fatal road crashes typically include aggregated data; (b) there is a national database of fatal road crashes in each country examined; (c) all countries provide aggregated crash data, but there are substantial restrictions on the availability of disaggregated data; and (d) overall, the crash data at the accident level are relatively similar, but there are substantial differences in the information at the person level. CONCLUSION: The results imply that international road safety research would greatly benefit from expanded availability of disaggregated fatal crash data worldwide.     

Crash data analysis: collective vs. individual crash level approach

INTRODUCTION: Traffic safety literature has traditionally focused on identification of location profiles where "more crashes are likely to occur" over a period of time. The analysis involves estimation of crash frequency and/or rate (i.e., frequency normalized based on some measure of exposure) with geometric design features (e.g., number of lanes) and traffic characteristics (e.g., Average Annual Daily Traffic [AADT]) of the roadway location. In the recent past, a new category of traffic safety studies has emerged, which attempts to identify locations where a "crash is more likely to occur." The distinction between the two groups of studies is that the latter group of locations would change based on the varying traffic patterns over the course of the day or even within the hour. METHOD: Hence, instead of estimation of crash frequency over a period of time, the objective becomes real-time estimation of crash likelihood. The estimation of real-time crash likelihood has a traffic management component as well. It is a proactive extension to the traditional approach of incident detection, which involves analysis of traffic data recorded immediately after the incident. The units of analysis used in these studies are individual crashes rather than counts of crashes. RESULTS: In this paper, crash data analysis based on the two approaches, collective and at individual crash level, is discussed along with the advantages and shortcomings of the two approaches.     

Using conditional inference forests to identify the factors affecting crash severity on arterial corridors

Introduction

The study aims at identifying traffic/highway design/driver-vehicle information significantly related with fatal/severe crashes on urban arterials for different crash types. Since the data used in this study are observational (i.e., collected outside the purview of a designed experiment), an information discovery approach is adopted for this study.

Method

Random Forests, which are ensembles of individual trees grown by CART (Classification and Regression Tree) algorithm, are applied in numerous applications for this purpose. Specifically, conditional inference forests have been implemented. In each tree of the conditional inference forest, splits are based on how good the association is. Chi-square test statistics are used to measure the association. Apart from identifying the variables that improve classification accuracy, the methodology also clearly identifies the variables that are neutral to accuracy, and also those that decrease it.

Results

The methodology is quite insightful in identifying the variables of interest in the database (e.g., alcohol/ drug use and higher posted speed limits contribute to severe crashes). Failure to use safety equipment by all passengers and presence of driver/passenger in the vulnerable age group (more than 55 years or less than 3 years) increased the severity of injuries given a crash had occurred. A new variable, ‘element’ has been used in this study, which assigns crashes to segments, intersections, or access points based on the information from site location, traffic control, and presence of signals.

Impact

The authors were able to identify roadway locations where severe crashes tend to occur. For example, segments and access points were found to be riskier for single vehicle crashes. Higher skid resistance and k-factor also contributed toward increased severity of injuries in crashes.     

Pedestrians under influence (PUI) crashes: Patterns from correspondence regression analysis

Introduction: Alcohol-related impairment is a key contributing factor in traffic crashes. However, only a few studies have focused on pedestrian impairment as a crash characteristic. In Louisiana, pedestrian fatalities have been increasing. From 2010 to 2016, the number of pedestrian fatalities increased by 62%. A total of 128 pedestrians were killed in traffic crashes in 2016, and 34.4% of those fatalities involved pedestrians under the influence (PUI) of drugs or alcohol. Furthermore, alcohol-PUI fatalities have increased by 120% from 2010 to 2016. There is a vital need to examine the key contributing attributes that are associated with a high number of PUI crashes. Method: In this study, the research team analyzed Louisiana’s traffic crash data from 2010 to 2016 by applying correspondence regression analysis to identify the key contributing attributes and association patterns based on PUI involved injury levels. Results: The findings identified five risk clusters: intersection crashes at business/industrial locations, mid-block crashes on undivided roadways at residential and business/residential locations, segment related crashes associated with a pedestrian standing in the road, open country crashes with no lighting at night, and pedestrian violation related crashes on divided roadways. The association maps identified several critical attributes that are more associated with fatal and severe PUI crashes. These attributes are dark to no lighting, open country roadways, and non-intersection locations. Practical Applications: The findings of this study may be used to help design effective mitigation strategies to reduce PUI crashes.     

The impact of traffic barrier geometric features on crash frequency and injury severity of non-interstate highways

Introduction: The main objective of this research is to investigate the effect of traffic barrier geometric characteristics on crashes that occurred on non-interstate roads. Method: For this purpose, height, side-slope rate, post-spacing, and lateral offset of about 137 miles of traffic barriers were collected on non-interstate (state, federal aid primary, federal aid secondary, and federal aid urban) highways in Wyoming. In addition, crash reports recorded between 2008 and 2017 were added to the traffic barrier dataset. The safety performance of traffic barriers with regards to their geometric features was analyzed in terms of crash frequency and crash severity using random-parameters negative binomial, and random-parameters ordered logit models, respectively. Results: From the results, box beam barriers with a height of 27–29 inches were less likely to be associated with injury and fatal injury crashes compared to other barrier types. On the other hand, the likelihood of a severe injury crash was found to be higher for box beam barriers with a height taller than 31 inches. Both W-beam and box beam barriers with a post-spacing between 6.1 and 6.3 inches reduced the probability of severe injury crashes. In terms of the crash frequency, flare traffic barriers had a lower crash frequency compared to parallel traffic barriers. Non-interstate roads without longitudinal rumble strips were associated with a higher rate of traffic barrier crashes.