Effect of driver,roadway, collision,and vehicle characteristics on crash severity: a conditional logistic regression approach

The aim of the study is to examine the factors that appear to have a higher potential for serious injury or death of drivers in traffic accidents in Turkey, such as collision type, roadway surface, vehicle speed, alcohol/drug use, and restraint use. Driver crash severity is the dependent variable of this study with two categories, fatal and non-fatal. Due to the binary nature of the dependent variable, a conditional logistic regression analysis was found suitable. Of the 16 independent variables obtained from Turkish police accident reports, 11 variables were found most significantly associated with driver crash severity. They are age, education level, restraint use, roadway condition, roadway type, time of day, collision location, collision type, number and direction of vehicles, vehicle speed, and alcohol/drug use. This study found that belted drivers aged 18–25 years involving two vehicles travelling in the same direction, in an urban area, during the daytime, and on an avenue or a street have better chances of survival in traffic accidents.     

A machine-learning method for improving crash injury severity analysis: a case study of work zone crashes in Cairo,Egypt

Abstract

The quality of vehicular collision data is crucial for studying the relationship between injury severity and collision factors. Misclassified injury severity data in the crash dataset, however, may cause inaccurate parameter estimates and consequently lead to biased conclusions and poorly designed countermeasures. This is particularly true for imbalanced data where the number of samples in one class far outnumber the other. To improve the classification performance of the injury severity, the paper presents a robust noise filtering technique to deal with the mislabels in the imbalanced crash dataset using the advanced machine learning algorithms. We examine the state-of-the-art filtering algorithms, including Iterative Noise Filtering based on the Fusion of Classifiers (INFFC), Iterative Partitioning Filter (IPF), and Saturation Filter (SatF). In the case study of Cairo (Egypt), the empirical results show that: (1) the mislabels in crash data significantly influence the injury severity predictions, and (2) the proposed M-IPF filter outperforms its counterparts in terms of the effectiveness and efficiency in eliminating the mislabels in crash data. The test results demonstrate the efficacy of the M-IPF in handling the data noise and mitigating the impacts thereof.     

The effect of natural and built environmental characteristics on pedestrian-vehicle crash severity in Ghana

Despite the benefits of walking as a means of travelling, walking can be quite hazardous. Pedestrian-vehicle crashes remain a major concern in Ghana as they account for the highest percentage of fatalities. The objective of this study is to determine the effect of both natural and built environmental features on pedestrian-vehicle crash severity in Ghana. The study is based on an extensive pedestrian-vehicle crash dataset extracted from the National Road Traffic Accident Database at the Building and Road Research Institute (BRRI) of the Council for Scientific and Industrial Research (CSIR), Ghana. Using a multinomial logit modelling framework, possible determinants of pedestrian-vehicle crash severity were identified. The study found that fatal crashes are likely to occur during unclear weather conditions, on weekends, at night time where there are no lights, on curved and inclined roads, on untarred roads, at mid-blocks and on wider roads. The developed model and its interpretations will make important contributions to road crash analysis and prevention in Ghana with the possibility of extension to other developing countries. These contributing factors could inform policy makers on road design and operational improvements.     

一种基于混合智能算法的协作中继选择新方法

针对多用户多中继场景下协作通信系统的中继选择问题,提出了一种基于混合智能算法的协作中继选择新方法。不同于现有的为每个源节点分配一个中继节点的中继选择方法,新方法建立了为每个源节点分配一个或多个中继节点的优化模型,以最大化多用户多中继协作系统的最小接收信噪比为优化目标,采用结合了模拟退火与遗传算法的混合智能算法来搜寻中继选择问题的最优解。仿真结果表明,所提方法可显著提高目的端的接收信噪比,且算法具有较强的全局搜索和快速寻优能力。