背景:这项研究解决了缺乏量化驾驶员对道路的熟悉程度的方法,这带来了更高的撞车风险。
方法:我们提出了一种使用DrivingApp数据评估行驶路线多样性和熟悉度的新方法。一种基于智能手机的研究工具,用于收集旅行级别的信息,包括驾驶曝光和全球定位系统(GPS)数据,从年轻的新手司机(15-19岁)到年长的司机(67-78岁)。利用这些数据,我们开发了一种基于GPS数据的算法来分析行驶路线的唯一性。该算法通过比较已识别用户的每个行程来创建相同的路线行程(SRT)数组,采用统计确定的GPS坐标接近度和行程重叠的阈值。使用通用线性模型(GLM)建立最优阈值来检查距离,反复观察。将调整后的宽度优先搜索方法应用于SRT阵列以防止双重计数或跳闸遗漏。生成的列表被分类为地理上不同的路线,或独特的路线(URs)。
结果:将算法输出与地理地图进行手动比较,得出的总体精度为0.93,精度为0.91。该算法产生两个主要输出:驾驶多样性(URs数量)的度量和从Rescorla-Wagner模型得出的基于路线的熟悉度的度量。为了评估这些措施的效用,在年轻新手司机数据集上使用高斯混合模型聚类算法,揭示了两个不同的群体:当具有较高的路线多样性时,具有较低的路线熟悉度的低频驾驶群体,而高频驱动组则相反。在年长的司机组中,发现URs的数量与老年抑郁评分之间存在显着相关性,或者步行的步态速度。
结论:这些研究结果表明,路线多样性和熟悉度可以补充现有的措施,以了解驾驶安全以及驾驶行为与身体和心理结果的关系。
BACKGROUND: This study addresses the lack of methods to quantify driver
familiarity with roadways, which poses a higher risk of crashes.
METHODS: We present a new approach to assessing driving route diversity and
familiarity using data from the DrivingApp, a smartphone-based research tool that collects trip-level information, including driving exposure and global positioning system (GPS) data, from young novice drivers (15-19 years old) to older drivers (67-78 years old). Using these data, we developed a GPS data-based algorithm to analyze the uniqueness of driving routes. The algorithm creates same route trip (SRT) arrays by comparing each trip of an identified user, employing statistically determined thresholds for GPS coordinate proximity and trip overlap. The optimal thresholds were established using a General Linear Model (GLM) to examine distance, and repeated observations. The Adjusted Breadth-First Search method is applied to the SRT arrays to prevent double counting or trip omission. The resulting list is classified as geographically distinct routes, or unique routes (URs).
RESULTS: Manual comparison of algorithm output with geographical maps yielded an overall precision of 0.93 and accuracy of 0.91. The algorithm produces two main outputs: a measure of driving diversity (number of URs) and a measure of route-based
familiarity derived from the Rescorla-Wagner model. To evaluate the utility of these measures, a Gaussian mixture model clustering algorithm was used on the young novice driver dataset, revealing two distinct groups: the low-frequency driving group with lower route
familiarity when having higher route diversity, whereas the high-frequency driving group with the opposite pattern. In the older driver group, there was a significant correlation found between the number of URs and Geriatric Depression Score, or walking gait speed.
CONCLUSIONS: These findings suggest that route diversity and
familiarity could complement existing measures to understand driving safety and how driving behavior is related to physical and psychological outcomes.