以地铁(Automatic Fare Collection System AFC)刷卡数据,即城市轨道交通自动售检票系统记录的刷卡数据为对象,综合运用时间维度和空间维度构建乘客分类指标,采用k-means聚类算法划分乘客群体.利用角门西站和西直门站连续3个工作日的地...以地铁(Automatic Fare Collection System AFC)刷卡数据,即城市轨道交通自动售检票系统记录的刷卡数据为对象,综合运用时间维度和空间维度构建乘客分类指标,采用k-means聚类算法划分乘客群体.利用角门西站和西直门站连续3个工作日的地铁刷卡数据进行实例分析,结果表明将乘客分为4类时效果客观且最佳.本研究乘客分类的结果明确了不同类型乘客的出行特征与出行需求,在地铁运营过程中可以据此调整运行策略,提升轨道交通服务水平,为乘客营造更加安全与舒适的环境.展开更多
Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on...Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use.Thanks to the advances in data collection and processing techniques,multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns.In this paper,based on a week’s operation data of 6600 passenger cars in Shanghai,we develop a systematic approach for identifying trips and travel purposes,and classify vehicles into four categories using a Gaussian-Mixed-Model.A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles.Wefind that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles.The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction,but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.展开更多
文摘以地铁(Automatic Fare Collection System AFC)刷卡数据,即城市轨道交通自动售检票系统记录的刷卡数据为对象,综合运用时间维度和空间维度构建乘客分类指标,采用k-means聚类算法划分乘客群体.利用角门西站和西直门站连续3个工作日的地铁刷卡数据进行实例分析,结果表明将乘客分为4类时效果客观且最佳.本研究乘客分类的结果明确了不同类型乘客的出行特征与出行需求,在地铁运营过程中可以据此调整运行策略,提升轨道交通服务水平,为乘客营造更加安全与舒适的环境.
基金supported by the project of the National Natural Science Foundation of China(No.71734004)。
文摘Understanding the characteristics of passenger vehicle use is the prerequisite for effective urban management.However,it has been challenging in the existing literature due to the lack of continuously observed data on passenger vehicle use.Thanks to the advances in data collection and processing techniques,multi-day vehicle trajectory data generated from volunteered passenger cars provide new opportunities for examining in depth how people travel in regular patterns.In this paper,based on a week’s operation data of 6600 passenger cars in Shanghai,we develop a systematic approach for identifying trips and travel purposes,and classify vehicles into four categories using a Gaussian-Mixed-Model.A new method is proposed to identify vehicle travel regularities and we use the Z Test to explore differences in travel time and route choices between four types of vehicles.Wefind that commercially used vehicles present high travel intensity in temporal and spatial aspects and the use intensity in elevated roads is higher for household-used commuting vehicles than semi-commercially used vehicles.The methodologies and conclusions of this paper may provide not only theoretical support for future urban traffic prediction,but also guidance for employing customized active traffic demand management measures to alleviate traffic congestion.
