Graph method for driving behavior optimization based on “SAF-ECO” description of behavior characteristics

IF 2.4 3区 工程技术 Q3 TRANSPORTATION Journal of Transportation Safety & Security Pub Date : 2022-10-07 DOI:10.1080/19439962.2022.2129893
Hang Qi, Xiaohua Zhao, Yiping Wu, Yang Ding, Yang Bian
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引用次数: 1

Abstract

Abstract Considering the fact that driving behavior data possesses characteristics of strong real-time, poor stability, and continuous change, this study proposes the Individual Driving Behavior Graph Construction Method (DBGCM), which visually presents the time trajectory of driving behavior to explore safety-ecological (SAF-ECO) characteristics of individual drivers. The results can be applied in the analysis of driving safety ecology and as a reference for driving behavior optimization. This study is based on the micro-driving behavior data collected by the on-board diagnostic devices (OBD), which can create a graph on individual driver behavior characteristics via nodes and time axis as its elements. Additionally, the method of Longest Common Subsequence (LCSS) is proposed to identify the similarity among different driving behavior graphs. The data results of taxi drivers under different SAF-ECO levels lead to the conclusion that the driving behavior characteristics graph analysis is consistent with the SAF-ECO classification. The similarity of graphs among “safe and non-eco” drivers is higher than that within other categories. Finally, the research discusses in detail the data requirements, method verification, and future applications. The reasonable coupling characteristic description of “SAF-ECO” driving behavior is conducive to the enhancement of drivers’ self-management ability, driving education, and customization for drivers.
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基于“SAF-ECO”行为特征描述的驾驶行为优化图法
摘要针对驾驶行为数据实时性强、稳定性差、持续变化等特点,提出了个体驾驶行为图构建方法(DBGCM),通过可视化地呈现驾驶行为的时间轨迹,探索驾驶员个体的安全生态(safe -eco)特征。研究结果可用于驾驶安全生态分析,为驾驶行为优化提供参考。本研究以车载诊断设备(OBD)采集的微驾驶行为数据为基础,以节点和时间轴为元素,构建驾驶员个体行为特征图。此外,提出了最长公共子序列(LCSS)方法来识别不同驾驶行为图之间的相似度。不同SAF-ECO等级下出租车司机的数据结果表明,驾驶行为特征图分析与SAF-ECO分类是一致的。与其他类别相比,“安全与非生态”司机之间的相似度更高。最后,对数据需求、方法验证和未来应用进行了详细讨论。合理的“SAF-ECO”驾驶行为耦合特征描述,有利于提高驾驶员自我管理能力,有利于驾驶员驾驶教育,有利于驾驶员定制。
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CiteScore
6.00
自引率
15.40%
发文量
38
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