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| Human mobility prediction and unobstructed route planning in public transport networks | |
| Shang, Shuo (1); Guo, Danhuai (3); Liu, Jiajun (2); Liu, Kuien (4); Shang, Shuo | |
| 2014 | |
| Conference Name | 15th IEEE International Conference on Mobile Data Management, IEEE MDM 2014 |
| Pages | 43-48 |
| Conference Date | July 15, 2014 - July 18, 2014 |
| Conference Place | Brisbane, QLD, Australia |
| Indexed Type | EI |
| Publish Place | Institute of Electrical and Electronics Engineers Inc. |
| ISSN | 15516245 |
| ISBN | 9781479957057 |
| Department | (1) Department of Computer Science, China University of Petroleum, Beijing, China; (2) CSIRO, Pullenvale, Australia; (3) CNIC, Chinese Academy of Sciences, Beijing, China; (4) Institute of Software, Chinese Academy of Sciences, Beijing, China |
| English Abstract | 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.; 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. |
| Language | 英语 |
| Content Type | 会议论文 |
| URI | http://ir.iscas.ac.cn/handle/311060/16616 |
| Collection | 中国科学院软件研究所 |
| Corresponding Author | Shang, Shuo |
| Recommended Citation GB/T 7714 | Shang, Shuo ,Guo, Danhuai ,Liu, Jiajun ,et al. Human mobility prediction and unobstructed route planning in public transport networks[C]. Institute of Electrical and Electronics Engineers Inc.,2014:43-48. |
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