汽车碰撞散落物再现模型研究

选取交通事故现场遗留的典型散落物作为试验对象,制作散落物承载装置并模拟真实环境下的车辆碰撞过程,利用VC3000加速计实时计测试验车相关运行参数,在干燥沥青路面进行散落物抛撒试验,深入分析散落物在地面的分布形态,利用ADAMS仿真分析散落物与车辆之间的相对运动过程,探讨路面散落物分布场的特征参数与碰撞车速的关系。     

Crash prediction model for two-lane rural highways in the Ashanti region of Ghana

Crash Prediction Models (CPMs) have been used elsewhere as a useful tool by road Engineers and Planners. There is however no study on the prediction of road traffic crashes on rural highways in Ghana. The main objective of the study was to develop a prediction model for road traffic crashes occurring on the rural sections of the highways in the Ashanti Region of Ghana. The model was developed for all injury crashes occurring on selected rural highways in the Region over the three (3) year period 2005–2007. Data was collected from 76 rural highway sections and each section varied between 0.8 km and 6.7 km. Data collected for each section comprised injury crash data, traffic flow and speed data, and roadway characteristics and road geometry data. The Generalised Linear Model (GLM) with Negative Binomial (NB) error structure was used to estimate the model parameters. Two types of models, the ‘core’ model which included key exposure variables only and the ‘full’ model which included a wider range of variables were developed. The results show that traffic flow, highway segment length, junction density, terrain type and presence of a village settlement within road segments were found to be statistically significant explanatory variables (p < 0.05) for crash involvement. Adding one junction to a 1 km section of road segment was found to increase injury crashes by 32.0% and sections which had a village settlement within them were found to increase injury crashes by 60.3% compared with segments with no settlements. The model explained 61.2% of the systematic variation in the data. Road and Traffic Engineers and Planners can apply the crash prediction model as a tool in safety improvement works and in the design of safer roads. It is recommended that to improve safety, highways should be designed to by-pass village settlements and that the number of junctions on a highway should be limited to carefully designed ones.     

碰撞分析中的有限元应用

交通事故的发生有时不可避免,为了将损失降到最低对车辆碰撞特性进行分析十分重要。整个车身是由薄壁结构组成的,因此在碰撞事故发生时,车身是主要的吸能部件。文章针对碰撞事故,对碰撞特性及车身的吸能情况,以及在碰撞分析中经常使用到的一些有限元分析软件进行了分析,指出多刚体动力学分析软件（MADYMO）是乘员约束系统整合及优化设计的首选工程软件,通过对大型结构的动态有限元分析,提高了研究效率。     

轿车侧面碰撞新车评价程序及提高轿车侧面碰撞性能的措施

本文简要介绍了美国高速公路运输安全管理局颁布的侧向碰撞新车评价程序－－ＬＩＮＣＡＰ星级评价程序。应用该程序对国外某轿车进行了侧向碰撞计算机模拟及试验，提出了提高该车型侧面碰撞性能的部分措施。文中侧重于计算机侧向碰撞模拟分析工作，模拟结果与结果的对比分析表明所做的碰撞模拟与试验是吻合的，提出的结构修改方案是有效的。     

On accommodating spatial dependence in bicycle and pedestrian injury counts by severity level

This paper proposes a new spatial multivariate count model to jointly analyze the traffic crash-related counts of pedestrians and bicyclists by injury severity. The modeling framework is applied to predict injury counts at a Census tract level, based on crash data from Manhattan, New York. The results highlight the need to use a multivariate modeling system for the analysis of injury counts by road-user type and injury severity level, while also accommodating spatial dependence effects in injury counts.     

Age related annual crash incidence rate ratios in professional drivers of heavy goods vehicles

ObjectivesEvidence concerning crash risk for older heavy vehicle drivers is sparse, making it difficult to assess if it is prudent to encourage older drivers to remain in the workforce in a climate of labour shortages. The objective of this study was to estimate annual crash rate ratios of older male heavy vehicle drivers relative to their middle aged peers.MethodsData utilized in this study includes all crashes meeting inclusion criteria involving heavy goods vehicles, categorised as rigid trucks and articulated trucks; this data was recorded by the New South Wales Roads and Traffic Authority. The exposure to the risk of a crash was represented by distance travelled for each vehicle type and year, by age of driver, as estimated by the Australian Survey of Motor Vehicle Use. Negative binomial regression modelling was applied to estimate annual crash incidence rate ratios for male drivers in various age groups.ResultsA total of 26,146 crashes occurred in New South Wales during 1999–2006, involving a total of 54,191 vehicles; removing observations that did not meet the inclusion criteria, 19,736 observations remained representing 12,501 crashes. For rigid trucks, the incidence rate ratio for drivers aged 65+ years, compared to 45–54 year olds, was 0.74 (95% CI 0.51, 0.98). For articulated trucks, the annual crash incidence rate ratio for drivers aged 65+ years compared to 45–54 year olds was 1.4 (95% CI 0.96, 1.9), and that for drivers aged 55–64 years compared to 45–54 year olds was 1.1 (95% CI 0.83, 1.3).ConclusionsOlder male professional drivers of heavy goods vehicles have lower risk of crashes in rigid vehicles, possibly due to accrued driving experience and self-selection of healthy individuals remaining in the workforce. Thus, encouraging these drivers to remain in the workforce is appropriate in the climate of labour shortages, as this study provides evidence that to do so would not endanger road safety.     

Crash risk factors associated with injury severity of teen drivers

This paper focuses on identifying crash risk factors associated with injury severity of teen drivers. Crash data obtained from the Highway Safety and Information System (HSIS) for the entire state of North Carolina, for years 2011 to 2013, was used for analysis and modeling. Among all the crashes during the study period, a total of 62,990 crashes involving teen drivers (15 to 19?years) were analyzed. A partial proportionality odds model was developed to identify factors contributing to injury severity of teen drivers. The results obtained indicate that teen drivers driving sports utility vehicles and pickup trucks are more likely to be severely injured when compared to teen drivers driving passenger cars. Teen drivers are more likely to be severely injured on weekdays, particularly during peak hours. The chances of teen drivers getting involved in severe injury crashes on Tuesdays and Fridays is higher when compared to Sundays. Age, gender, road configuration, terrain, adverse weather condition, and access control are observed to have a significant effect on teen driver's injury severity.     

Application of traffic microsimulation for evaluating safety performance of urban signalized intersections

The primary objective of this paper is to provide a statistical relationship between traffic conflicts estimated from microsimulation and observed crashes in order to evaluate safety performance, in particular the effect of countermeasures. A secondary objective is to assess the effect of conflict risk tolerance and number of simulation runs on the estimates of countermeasure effects so obtained. Conflicts were simulated for a sample of signalized intersections from Toronto, Canada, using VISSIM microscopic traffic simulation and several crash–conflict relationships were obtained. A separate sample of treated intersections from Toronto was used to compare countermeasure effects from the integrated crash–conflict expression to a conventional, but rigorous crash-based Empirical Bayes before-and-after analysis that was already done, with the results published, for the same sites and treatment. The countermeasure considered for this investigation involved changing the left turn signal operation for the treated intersection sample from permissive to protected-permissive. The results support the view that countermeasure effects can be estimated reliably from conflicts derived from microsimulation, and more so when a suitable number of simulation runs and conflict tolerance thresholds are used in the crash–conflict relationship.     

Exploring the feasibility of classification trees versus ordinal discrete choice models for analyzing crash severity

A cross-median crash (CMC) is one of the most severe types of crashes in which a vehicle crosses the median and sometimes collides with opposing traffic. A study of severity of CMCs in the state of Wisconsin was conducted by Lu et al. in 2010. Discrete choice models, namely ordinal logit and probit models were used to analyze factors related to the severity of CMCs. Separate models were developed for single and multi-vehicle CMCs. Although 25 different crash, roadway, and geometric variables were used, only 3 variables were found to be statistically significant which were alcohol usage, posted speed, and road conditions. The objective of this research was to explore the feasibility of GUIDE Classification Tree method to analyze the severity of CMCs to discover if any additional information could be revealed.A dataset of CMCs in the state of Wisconsin between 2001 and 2007, used in the study by Lu et al. was used to develop three different GUIDE Classification Trees. Additionally, the effects of variable types (continuous or discrete), misclassification costs, and tree pruning characteristics on models results were also explored. The results were directly compared with discrete choice models developed in the study by Lu et al. showing that the GUIDE Classification Trees revealed new variables (median width and traffic volume) that affect CMC severity and provided useful insight on the data. The results of this research suggest that the use of Classification Tree analysis should at least be considered in conjunction with regression-based crash models to better understand factors affecting crashes. Classification Tree models were able to reveal additional information about the dependent variable and offer advantages with respect to multicollinearity and variable redundancy issues.     

Crash prediction with behavioral and physiological features for advanced vehicle collision avoidance system

Real-time crash prediction is the key component of the Vehicle Collision Avoidance System (VCAS) and other driver assistance systems. The further improvements of predictability requires the systemic estimation of crash risks in the driver-vehicle-environment loop. Therefore, this study designed and validated a prediction method based on the supervised learning model with added behavioral and physiological features. The data samples were extracted from 130 drivers’ simulator driving, and included various features generated from synchronized recording of vehicle dynamics, distance metrics, driving behaviors, fixations and physiological measures. In order to identify the optimal configuration of proposed method, the Discriminant Analysis (DA) with different features and models (i.e. linear or quadratic) was tested to classify the crash samples and non-crash samples. The results demonstrated the significant improvements of accuracy and specificity with added visual and physiological features. The different models also showed significant effects on the characteristics of sensitivity and specificity. These results supported the effectiveness of crash prediction by quantifying drivers’ risky states as inputs. More importantly, such an approach also provides opportunities to integrate the driver state monitoring into other vehicle-mounted systems at the software level.