Ranking Station Importance With Human Mobility Patterns Using Subway Network Datasets
Ranking Station Importance With Human Mobility Patterns Using Subway Network Datasets
复制标题
使用地铁网络数据集根据人类移动模式对车站重要性进行排名
DOI：
10.1109/tits.2019.2920962
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发表时间：
2020
期刊：
影响因子：
--
通讯作者：
Zhibo Wang
中科院分区：
文献类型：
--
作者：
Feng Xia;Jinzhong Wang;Xiangjie Kong;Da Zhang;Zhibo Wang
复杂网络已经成为一个活跃的跨学科研究领域的各种网络的实证研究的启发。地铁网络是交通领域复杂网络的一个实例，近年来在网络分析中受到越来越多的关注。分析人的流动模式，特别是在地铁站排名密切结合城市地铁规划和个人的旅行经验，仍然是一个悬而未决的问题。本文提出了一种新的站点重要性排序方法（SIRank），利用人类的移动模式和改进的PageRank算法。具体而言，通过分析上海地铁系统的人员流动模式，我们证明了静态和动态特性，使用两个网络模型（上海地铁静态网络和上海地铁客运网络）。特别是，SIRank侧重于始发地和目的地之间的双向客流分析，以迭代方式生成每个车站的重要性值。我们使用真实世界的地铁交易数据集实现了一系列实验来说明SIRank的有效性。结果表明，SIRank中前5名站点的命中率达到60%，远高于加权混合指数排序（WMIRank）和节点度排序（NDRank）。
Complex networks have become an active interdisciplinary field of research inspired by the empirical study of various networks. A subway network is a real-world example of complex networks in the transportation domain, which has attracted growing attention in network analysis recently. Analyzing human mobility patterns, specifically in ranking subway stations closely bounded by urban subway planning and individuals’ travel experience, is still an open issue. In this paper, we propose a novel ranking method of station importance (SIRank) by utilizing human mobility patterns and improved PageRank algorithm. Specifically, by analyzing human mobility patterns of the subway system in Shanghai, we demonstrate both static and dynamic characteristics using two network models (Shanghai subway static network and Shanghai subway passenger network). In particular, the SIRank focuses on bi-directional passenger flow analysis between origins and destinations to iteratively generate the importance value for each station. We implement a range of the experiments to illustrate the effectiveness of SIRank using the real-world subway transaction datasets. The results demonstrate that the hit ratio in SIRank reaches 60% in the top five stations, which is much higher than that of ranking by a weighted mixed index (WMIRank) and ranking by node degree (NDRank) approaches.