文章编号： 1672-5328（2018）02-0070-06

江志彬1，刘伟2，韩彦钊1，陈菁菁2
(1.同济大学交通运输工程学院，道路与交通工程教育部重点实验室，上海201804；2.上海申通地铁集团有限公 司技术中心，上海201103)

摘要： 客流分析是轨道交通运营组织的基础，传统的客流分析方法无法从海量的乘客历史出行大数 据中提取与挖掘乘客出行规律和特征。大数据可视化为获得洞察大规模复杂客流数据能力提供支 撑。基于城市轨道交通网络实际运营需求，从客流认知、可视化、人机交互的综合视角出发，基于 GIS 地图、网络迁徙图、日历图、散点图、弦图等可视化图形，研究大规模复杂网络OD客流、断 面、进出站和换乘客流大数据可视化的运营需求关键技术与实现方法。对上海城市轨道交通网络客 流大数据进行实例分析，可视化展示结果有利于运营管理人员掌握网络客流时空变化特征以及演变 规律，为制定科学行车与客运组织方案提供决策依据。

关键词： 城市轨道交通；客流；大数据；可视化；客运组织

中图分类号： U491.1+2

文献标识码：A

Big Passenger Flow Data Visualization for Urban Rail Transit Network

Jiang Zhibin1, LiuWei2, Han Yanzhao1, Chen Jingjing2
(1.School of Transportation Engineering, Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, Shanghai 201804, China; 2.Technical Center of Shanghai Shentong Metro Group Co. Ltd., Shanghai 201103, China)

Abstract： Analyzing passenger flow is fundamental for effective rail transit operation. However, the traditional analysis methods cannot extract and explore the characteristics of passenger travel from the historical big passenger flow data. The big data visualization enables the analysis of large-scale complex passenger flow data. Based on the actual operational needs, and comprehensive perspective of passenger flow cognition, visualization, and human- computer interaction, this paper discusses the key techniques and method for the big data visualization such as origin-destination, passenger volume, passenger entering/departing station, and number of transfer passengers in the big-scale complex network using GIS map, network migration map, calendar map, scatter plot, chord diagram and other visual graphics. A case study in Shanghai urban rail transit network is presented to illustrate the applications of the techniques. The results show that visualized presentation is helpful for operation management personnel to understand the temporal and spatial changes and evolution of passenger flows in the network, and provides valuable information for making effective operation decisions.

Keywords： urban rail transit; passenger flow; big data; visualization; passenger transportation operation