致命道路交通事故相关因素分析：一种机器学习方法
Analyzing Factors Associated with Fatal Road Crashes: A Machine Learning Approach
摘要：
道路交通伤害在全球造成巨大的人力和经济负担。了解导致致命伤害的风险因素至关重要。在这项研究中，我们提出了一个模型，采用混合集成机器学习分类器结构从顺序最小优化和决策树，以确定危险因素，导致致命的道路伤害。该模型的构建，训练，测试和验证使用黎巴嫩道路事故平台（LRAP）数据库的8482道路碰撞事故，死亡率作为结果变量。采用敏感性分析研究多因素对死亡率的影响。七个选定的独立变量与死亡率的发生，即，碰撞类型，受伤严重程度，空间集群ID，碰撞时间（小时）。从模型数据分析中获得的证据将被政策制定者和主要利益攸关方采用，以深入了解与致命道路碰撞相关的主要促成因素，并将知识转化为安全方案和强化的道路政策。
英文摘要：
Road traffic injury accounts for a substantial human and economic burden globally. Understanding risk factors contributing to fatal injuries is of paramount importance. In this study, we proposed a model that adopts a hybrid ensemble machine learning classifier structured from sequential minimal optimization and decision trees to identify risk factors contributing to fatal road injuries. The model was constructed, trained, tested, and validated using the Lebanese Road Accidents Platform (LRAP) database of 8482 road crash incidents, with fatality occurrence as the outcome variable. A sensitivity analysis was conducted to examine the influence of multiple factors on fatality occurrence. Seven out of the nine selected independent variables were significantly associated with fatality occurrence, namely, crash type, injury severity, spatial cluster-ID, and crash time (hour). Evidence gained from the model data analysis will be adopted by policymakers and key stakeholders to gain insights into major contributing factors associated with fatal road crashes and to translate knowledge into safety programs and enhanced road policies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
