In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever b...In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination （OD） information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.展开更多
交通智能(IC)卡可以记录居民的移动出行,反映居民的源-目的地(OD)信息;但智能卡记录的OD流数据规模大,直接可视化空间分布容易导致视觉杂乱,并且多元数据类型多,更难以和流数据结合对比分析。首先,针对直接可视化大规模OD数据的空间分...交通智能(IC)卡可以记录居民的移动出行,反映居民的源-目的地(OD)信息;但智能卡记录的OD流数据规模大,直接可视化空间分布容易导致视觉杂乱,并且多元数据类型多,更难以和流数据结合对比分析。首先,针对直接可视化大规模OD数据的空间分布容易视觉遮挡的问题,提出基于正交非负矩阵分解(ONMF)的流聚类方法。所提方法对源-目的地数据聚类后再可视化,可以减少不必要的遮挡。然后,针对多元时空数据类型多难以结合对比分析的问题,设计了公交站点多元时序数据视图。该可视化方法将公交站点的流量大小和空气质量、空气温度、相对湿度、降雨量这四类多元数据在同一时间序列上编码,提高了视图的空间利用率并且可以对比分析。再次,为了辅助用户探索分析,开发了基于OD流和多元数据的交互式可视分析系统,并设计了多种交互操作提升用户探索效率。最后,基于新加坡交通智能卡数据集,从聚类效果和运行时间对该聚类方法评估。结果显示,在用轮廓系数评估聚类效果上,所提方法比原始方法提升了0.028,比用K均值聚类方法提升了0.253;在运行时间上比聚类效果较好的ONMFS(ONMF through Subspace exploration)方法少了254 s。通过案例分析和系统功能对比验证了系统的有效性。展开更多
基金This work was supported by the National High- Tech Research and Development Plan of China （863） （2011AA010502）, the National Natural Science Foundation of China （Grant No. 61103093）, the Doctoral Fund of Ministry of Education of China （20091102110017）, the International Science ＆ Technology Cooperation Program of China （2010DFB 13350）, the Supported Project （SKLSDE-2012ZX-16） of the State Key Laboratory of Software Development Environment, and the Fundamen- tal Research Funds for the Central Universities. We are thankful to Bei- jing Municipal Committee of Transportation, Beijing Metro Network Con- trol Center, Beijing Mass Transit Railway Operation Corporation Limited, and Beijing MTR Corporation for their great help.
文摘In order to provide citizens with safe, convenient and comfortable services and infrastructure in a metropolis, the prediction of passenger flows in the metro-net of subway system has become more important than ever before. Al- though a great number of prediction methods have been pre- sented in the field of transportation, all of them belong to the station oriented approach, which is not well suited to the Bei- jing subway system. This paper proposes a novel metro-net oriented method, called the probability tree based passenger flow model, which is also based on historic origin-destination （OD） information. First it learns and obtains the appearance probabilities for each kind of OD pair. For the real-time origin datum, the destination datum is calculated, and then several kinds of passenger flow in the metro-net can be pre- dicted by gathering all the contributions. The results of exper- iments, using the historical data of Beijing subway, show that although the proposed method has lower performance than existing prediction approaches for forecasting exit passenger flows, it is able to predict several additional kinds of passen- ger flow in stations and throughout the subway system; and it is a more feasible, suitable, and advanced passenger flow prediction model for Beijing subway system.
文摘交通智能(IC)卡可以记录居民的移动出行,反映居民的源-目的地(OD)信息;但智能卡记录的OD流数据规模大,直接可视化空间分布容易导致视觉杂乱,并且多元数据类型多,更难以和流数据结合对比分析。首先,针对直接可视化大规模OD数据的空间分布容易视觉遮挡的问题,提出基于正交非负矩阵分解(ONMF)的流聚类方法。所提方法对源-目的地数据聚类后再可视化,可以减少不必要的遮挡。然后,针对多元时空数据类型多难以结合对比分析的问题,设计了公交站点多元时序数据视图。该可视化方法将公交站点的流量大小和空气质量、空气温度、相对湿度、降雨量这四类多元数据在同一时间序列上编码,提高了视图的空间利用率并且可以对比分析。再次,为了辅助用户探索分析,开发了基于OD流和多元数据的交互式可视分析系统,并设计了多种交互操作提升用户探索效率。最后,基于新加坡交通智能卡数据集,从聚类效果和运行时间对该聚类方法评估。结果显示,在用轮廓系数评估聚类效果上,所提方法比原始方法提升了0.028,比用K均值聚类方法提升了0.253;在运行时间上比聚类效果较好的ONMFS(ONMF through Subspace exploration)方法少了254 s。通过案例分析和系统功能对比验证了系统的有效性。
