Human mobility prediction and unobstructed route planning in public transport networks
文献类型：会议论文
| 作者 | Shang, Shuo (1) ; Guo, Danhuai (3) ; Liu, Jiajun (2) ; Liu, Kuien (4) |
| 出版日期 | 2014 |
| 会议名称 | 15th IEEE International Conference on Mobile Data Management, IEEE MDM 2014 |
| 会议日期 | July 15, 2014 - July 18, 2014 |
| 会议地点 | Brisbane, QLD, Australia |
| 页码 | 43-48 |
| 通讯作者 | Shang, Shuo |
| 中文摘要 | With the increasing availability of human-tracking data (e.g., Public transport IC card data, trajectory data, etc.), human mobility prediction is increasingly important. In this paper, we study a novel problem of using human-tracking data to predict human mobility and to detect over-crowded stations in public transport networks, and then finding unobstructed routes to go around these over-crowded stations. We believe that this study can bring significant benefits to users in many popular mobile applications such as route planning and recommendation, urban computing, and location based services in general. This problem is challenged by two difficulties: (1) how to detect crowded stations effectively, and (2) how to find unobstructed routes in public transport networks efficiently. To overcome these difficulties, we propose three human-mobility prediction methods based on uniform distribution, standard normal distribution, and priority ranking, respectively, to predict human mobility and to detect over-crowded stations. Then, we develop an efficient algorithm based on network expansion to find unobstructed routes in public transport networks. The performance of the developed algorithms has been verified by extensive experiments. |
| 英文摘要 | With the increasing availability of human-tracking data (e.g., Public transport IC card data, trajectory data, etc.), human mobility prediction is increasingly important. In this paper, we study a novel problem of using human-tracking data to predict human mobility and to detect over-crowded stations in public transport networks, and then finding unobstructed routes to go around these over-crowded stations. We believe that this study can bring significant benefits to users in many popular mobile applications such as route planning and recommendation, urban computing, and location based services in general. This problem is challenged by two difficulties: (1) how to detect crowded stations effectively, and (2) how to find unobstructed routes in public transport networks efficiently. To overcome these difficulties, we propose three human-mobility prediction methods based on uniform distribution, standard normal distribution, and priority ranking, respectively, to predict human mobility and to detect over-crowded stations. Then, we develop an efficient algorithm based on network expansion to find unobstructed routes in public transport networks. The performance of the developed algorithms has been verified by extensive experiments. |
| 收录类别 | EI |
| 会议录出版地 | Institute of Electrical and Electronics Engineers Inc. |
| 语种 | 英语 |
| ISSN号 | 15516245 |
| ISBN号 | 9781479957057 |
| 源URL | [http://ir.iscas.ac.cn/handle/311060/16616]  |
| 专题 | 软件研究所_软件所图书馆_会议论文 |
| 推荐引用方式 GB/T 7714 | Shang, Shuo ,Guo, Danhuai ,Liu, Jiajun ,et al. Human mobility prediction and unobstructed route planning in public transport networks[C]. 见:15th IEEE International Conference on Mobile Data Management, IEEE MDM 2014. Brisbane, QLD, Australia. July 15, 2014 - July 18, 2014. |
入库方式： OAI收割
来源：软件研究所
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