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Title:
Human mobility prediction and unobstructed route planning in public transport networks
Author: Shang, Shuo (1) ; Guo, Danhuai (3) ; Liu, Jiajun (2) ; Liu, Kuien (4)
Conference Name: 15th IEEE International Conference on Mobile Data Management, IEEE MDM 2014
Conference Date: July 15, 2014 - July 18, 2014
Issued Date: 2014
Conference Place: Brisbane, QLD, Australia
Corresponding Author: Shang, Shuo
Publish Place: Institute of Electrical and Electronics Engineers Inc.
Indexed Type: EI
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
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.
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.
Language: 英语
Content Type: 会议论文
URI: http://ir.iscas.ac.cn/handle/311060/16616
Appears in Collections:软件所图书馆_会议论文

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Recommended Citation:
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.
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