基于改进K-prototypes与GBDT的城市干道车辆出行群体辨识模型

Vehicle Travl Group Identification Model of Urban Arterial Road Based on Improved K-prototypes and GBDT

为了掌握城市干道交通运行规律,向交通管理部门制定相关交通需求管理政策提供理论依据,提出了一种基于组合模型的城市干道车辆出行群体辨识模型。基于青岛市胶州湾隧道过车数据,从出行强度、出行时间与出行习惯3个维度构建了出行特征指标体系以全面刻画车辆个体的出行行为。基于相关性分析剔除了冗余指标以避免对辨识研究的影响。针对混合属性出行特征指标数据,使用改进K-prototypes算法以有效地实现车辆出行群体划分,将其与GBDT算法相结合,建立了一种基于改进K-prototypes与GBDT的辨识模型,随机选取10000个样本开展辨识研究。结果表明:研究道路存在5类车辆出行群体:高频通勤群体、低频通勤群体、营运群体、频次稳定群体与普通群体,对于这5类车辆出行群体,平均识别准确率为97.75%,最高识别准确率可达99.47%。

In order to identify the traffic operation law of urban arterial road and support basis for traffic management departments to formulate relevant traffic demand management policies,a vehicle travel group identification model of urban arterial road based on combined model was proposed.In this study,a travel characteristic indicator system was constructed from dimensions of travel intensity,travel time,travel habits for comprehensively describing the travel behavior based on the traffic bayonet data of Qingdao Jiaozhou Bay Tunnel.The redundant indicator was eliminated based on the correlation analysis to avoid the impact on identification research.For the mixed attribute travel characteristic indicator data,the improved K-prototypes algorithm was used to effectively classify the vehicle travel groups,and combined with GBDT,the identification model based on improved K-prototypes and GBDT was established.By randomly selecting 10000 samples to conduct identification research,the result shows that there are 5 vehicle travel groups for the road in this research,including high-frequency commuter groups,low-frequency commuter groups,operating groups,frequency stable groups,and ordinary groups.For the 5 vehicle travel groups,the average identification accuracy rate exceeds 97.75%,and the highest identification accuracy rate can reach 99.47%.