This paper attempts to shed light on the temporal evolution of driving safety efficiency with the aim to acquire insights useful for both driving behavior and road safety improvement. Data exploited herein are collected from a sophisticated platform that uses smartphone device sensors during a naturalistic driving experiment, at which the driving behavior from a sample of two hundred (200) drivers during 7-months is continuously recorded in real time. The main driving behavior analytics taken into consideration for the driving assessment include distance travelled, acceleration, braking, speed and smartphone usage. The analysis is performed using statistical, optimization and machine learning techniques. The driver’s safety efficiency index is estimated both in total and in several consecutive time windows to allow for the investigation of safety efficiency evolution in time. Initial data analysis results to the most critical components of microscopic driving behaviour evolution, which are used as inputs in the k-means algorithm to perform the clustering analysis. The main driving characteristics of each cluster are identified and lead to the conclusion that there are three main driving groups of the a) moderate drivers, b) unstable drivers and c) cautious drivers.

本文试图阐明驾驶安全效率的时间演变，以期获得对驾驶行为和道路安全改进均有用的见解。本文利用的数据是从在自然驾驶实验期间使用智能手机设备传感器的复杂平台收集的，在该平台上，实时连续记录了两百（200）名驾驶员在7个月内的驾驶行为。驾驶评估中考虑的主要驾驶行为分析包括行驶距离，加速，制动，速度和智能手机使用情况。使用统计，优化和机器学习技术执行分析。驾驶员的安全效率指数在总的和连续的几个时间窗口中都可以估算，以便及时调查安全效率的变化。初始数据分析结果是微观驾驶行为演变的最关键组成部分，这些数据在k均值算法中用作输入以执行聚类分析。确定了每个组的主要驾驶特性，并得出结论，有以下三个主要驾驶群体：a）中度驾驶者，b）不稳定驾驶者和c）谨慎驾驶者。