Using traffic conviction correlates to identify high accident-risk drivers
Using traffic conviction correlates to identify high accident-risk drivers
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DOI：
10.1016/s0001-4575(02)00098-2
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发表时间：
2003-11-01
影响因子：
5.9
通讯作者：
Peck, RC
中科院分区：
文献类型：
--
作者：
Gebers, MA;Peck, RC
加州机动车管理局的主要任务之一是保护公众不受代表不可接受的高事故风险的驾驶员的伤害。要最大限度地实现这一目标，就需要制定和执行查明高风险驱动因素的战略。加州的一个这样的系统是该部门的疏忽操作员点系统。该系统为移动违规和事故扣分，并授权该部门对符合疏忽操作员初步定义的司机采取司机控制行动。本研究探讨了预测事故的可行性方程构建预测定罪的一般驾驶人群。更好地识别未来事故风险增加的驾驶员的方程式或模型将增加通过许可后控制行动预防的事故数量。虽然结果并不支持先前的研究结果，关键的引文方程做以及或更好地比关键的事故方程在预测随后的事故参与，典型相关的方法，同时考虑随后的事故和引文率产生了14.9%的改善分类准确性或“命中率”识别事故涉及的司机。(C)2003爱思唯尔科技有限公司版权所有。
One of the primary missions of the California Department of Motor Vehicles is to protect the public from drivers who represent unacceptably high accident risks. Optimum fulfillment of this objective requires the development and implementation of strategies for identifying high-risk drivers. One such system in California is the department's negligent operator point system. This system assigns points to moving violations and accidents and authorizes the department to take driver control actions against drivers who meet the prima facie definition of a negligent operator. The present study explored the viability of predicting accidents from equations constructed to predict convictions for the general driving population. Equations or models that better identify drivers at increased risk of future accident involvement would increase the number of accidents prevented through post license control actions. Although the results did not support prior findings that equations keyed to citations do as well as or better than equations keyed to accidents in predicting subsequent accident involvement, a canonical correlation approach considering subsequent accident and citation rates simultaneously produced a 14.9% improvement in the classification accuracy or "hit rate" for identifying accident-involved drivers. (C) 2003 Elsevier Science Ltd. All rights reserved.