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

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.