基于运动特征模式的出租车行驶状态综合研判

Comprehensive Identification of Taxi Driving State Based on Kinematic Feature Pattern

摘要:

基于行驶轨迹全球导航卫星系统（GNSS）数据，提出了出租车运动学片段提取规则和方法。根据主成分分析（PCA）及累积贡献率，确定了8个表征运动学片段的关键指标；结合K?均值聚类算法，挖掘出租车运动特征模式。为了确保运动特征模式关键指标权重的客观合理性，采用考虑指标关联性的CRITIC（criteria importance through intercriteria correlation）法和考虑指标离散程度的熵权法，构建了基于纳什均衡的组合赋权的多准则妥协解排序（VIKOR）评价模型，用于多时空情景下出租车运动特征模式评价和出租车行驶状态研判。结果表明，基于纳什均衡的组合赋权法可以有效融合CIRTIC法与熵权法对评价指标的优势，获得更合理的权重系数。就安全性、效率和舒适性而言，出租车行驶状态在主干路和次干路上优于在支路上。早高峰出租车行驶安全性最佳，平峰和晚高峰相对一般。

Abstract:

Based on GNSS data， the rule and method of extracting taxi kinematic segments were proposed. According to the principal component analysis （PCA） and cumulative contribution rate， 8 key indicators were determined to represent kinematic segments. Combined with K-mean clustering algorithm， the taxi kinematic feature patterns were obtained. In order to ensure the objective rationality of the weight of key indicators， the CRITIC method considering the correlation of indicators and the entropy weight method considering the dispersion degree of indicators were adopted. The VIKOR evaluation model based on Nash equilibrium combination weight was established to evaluate the driving state of the taxi under multi spatio-temporal scenarios. The results show that the combined weighting method based on the Nash equilibrium can effectively integrate the merits of the CIRTIC method and entropy weight method and obtain a more reasonable weight coefficient. In terms of overall safety， efficiency and comfort， the driving condition of the main road and secondary road is better than that of the branch road. The safety of taxi driving is the best in the morning peak， and relatively ordinary in the evening peak and flat peak.