Driving Behavior Evaluation Based on DBSCAN and Kmeans++ Clustering

Ruifeng Wang, Wenyuan Zheng, Miaohua Huang, Guohang Li
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引用次数: 1

Abstract

Public transportation plays an important role in residents’ daily travel, and it’s important to improve the comprehensive driving ability of drivers. This paper establishes a scientific and comprehensive evaluation system for bus drivers’ driving behavior. First, based on the big data of the operation of new energy buses, the kinematic segments are divided into consideration of the temperature changes in four seasons and traffic conditions, and the index features under the three dimensions of driving behavior security, energy consumption and passenger comfort are excavated, and then combined with DBCSAN and Kmeans++, the driving behavior of each dimension is clustered and classified, which improves the reliability of the clustering and the rationality of the classification. Finally, based on the weighted scoring method, the classification of the driving behavior is determined by combining the temperature of the four seasons and the proportion of different traffic conditions. The driver’s comprehensive driving behavior scoring model is established to realize the quantitative evaluation of the driver’s driving behavior. The model can reasonably evaluate the driver’s comprehensive driving ability and help the driver to improve their comprehensive driving skills.
基于DBSCAN和kmeans++聚类的驾驶行为评价
公共交通在居民日常出行中占有重要地位,提高驾驶员的综合驾驶能力具有重要意义。本文建立了一套科学、全面的公交司机驾驶行为评价体系。首先,基于新能源客车运行大数据,考虑四季温度变化和交通状况对其运动学段进行划分,挖掘驾驶行为安全、能耗和乘客舒适度三个维度下的指标特征,然后结合DBCSAN和kmeans++对各维度的驾驶行为进行聚类分类;提高了聚类的可靠性和分类的合理性。最后,基于加权评分法,结合四季气温和不同交通状况的比例,确定驾驶行为的分类。建立驾驶员驾驶行为综合评分模型,实现对驾驶员驾驶行为的定量评价。该模型可以合理评价驾驶员的综合驾驶能力,帮助驾驶员提高综合驾驶技能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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