组合赋权-模糊综合评价的安全驾驶行为分析

驾驶行为日趋复杂化与个性化，使其逐步成为影响交通安全的活跃因素。为了对城市道路中驾驶行为安全风险等级进行评估，减少交通事故的发生，针对传统人或车因子单向给定的评价方法，从驾驶员操纵安全、车辆行驶工况安全两个方面，选取速度、发动机冷却液温度等7个指标构建驾驶行为评估体系。运用K-means聚类分别对两个维度进行聚类分析，将驾驶行为类型进行划分。应用层次分析法(Analytic Hierarchy Process, AHP)与熵权法(Entropy Weight Method, EWM)的组合赋权法求取权重并结合模糊综合评价法对目标层进行评估，将驾驶行为划分为安全型、风险型、危险型。为了获取汽车行驶过程中的运行状态数据，利用车载诊断设备(On Board Diagnostics, OBD)进行驾驶状态数据采集，分析不同的安全风险等级行为的诱因，为相关车管单位选择和考核驾驶员提供合理依据。

Analysis of Safe Driving Behavior Based on Combination Weighting and Fuzzy Comprehensive Evaluation

Driving behavior is becoming more complex and personalized. It gradually become an active factor affecting traffic safety. In order to evaluate the safety risk level of driving behavior on urban roads, reduce traffic accidents. Against the traditional evaluation method is to give the person or vehicle factor unidirectionally. From the two aspects of driver operation safety and vehicle driving condition safety. Seven evalution were estabilshed that including speed and engine coolant temperature. K-means algorithm is used to perform cluster analysis on the two dimensions, and the driving behavior types are divided. The weight values of various evalution factors obtained by using the combination model of analytic hierarchy process (AHP) and entropy weight method (EWM) and evaluate the target layer combined with the fuzzy comprehensive evaluation method, divide driving behavior into safe, risky, and dangerous. In order to obtain the driving state data, OBD is used to collect driving state data. Analyzing the incentives for behaviors with different security risk levels. Providing reasonable basis for relevant vehicle management units to select and evaluate drivers.