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Development of Driver Behavior’s Accident Prediction Models

机译：驾驶员行为的事故预测模型的开发

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Amman, the capital city of Jordan, has been expanding in terms of size, investment, and growth of vehicles. Such a growth has lead to traffic jams and delays experienced at all levels of services, and higher accident frequency level at several locations, which resulted in loss of people’s lives, and causing major economical and social concerns in the country. Statistical models were employed to analyze accident frequency for Amman. The objectives were to identify hazardous locations by developing accident prediction models. Accident data was collected and linked to independent variables. Several models were developed to identify the relationship between accident frequency and key behavioral characteristics of drivers. Different types of high-accident locations were identified, classified, and ranked according to their hazardous degrees by using statistical techniques. Findings indicated that the short distance between vehicles, lane changing, and non-yielding right-of-way variables were the most critical causes of accidents. A priority ranking for countermeasures was recommended to reduce accidents and improve the overall driving safety at hazardous locations based on the developed models. Recommendations were made for the way in which accidents on these locations would be treated. Suggestions were made for the practical and theoretical development for further research.
机译：约旦的首都安曼一直在扩大规模，投资和车辆增长。这种增长导致各种服务水平的交通拥堵和延误，并且在几个地点的事故频率更高，导致人员丧生，并引起该国的重大经济和社会关注。采用统计模型分析安曼的事故频率。目的是通过开发事故预测模型来识别危险位置。收集事故数据并将其链接到自变量。开发了几种模型来识别事故发生频率与驾驶员关键行为特征之间的关系。使用统计技术，根据不同的危险程度，对不同类型的高事故发生地点进行识别，分类和排序。研究结果表明，车辆之间的短距离，车道变更和非屈服的路权变量是造成事故的最主要原因。建议根据开发的模型对措施进行优先排序，以减少事故并提高危险位置的总体驾驶安全性。提出了处理这些地点事故的建议。提出了进一步发展的理论和实践建议。

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