ISCAS OpenIR
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 Name15th IEEE International Conference on Mobile Data Management, IEEE MDM 2014
Pages43-48
Conference DateJuly 15, 2014 - July 18, 2014
Conference PlaceBrisbane, QLD, Australia
Indexed TypeEI
Publish PlaceInstitute of Electrical and Electronics Engineers Inc.
ISSN15516245
ISBN9781479957057
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 AbstractWith 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
URIhttp://ir.iscas.ac.cn/handle/311060/16616
Collection中国科学院软件研究所
Corresponding AuthorShang, 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.
Files in This Item:
There are no files associated with this item.
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Shang, Shuo (1)]'s Articles
[Guo, Danhuai (3)]'s Articles
[Liu, Jiajun (2)]'s Articles
Baidu academic
Similar articles in Baidu academic
[Shang, Shuo (1)]'s Articles
[Guo, Danhuai (3)]'s Articles
[Liu, Jiajun (2)]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Shang, Shuo (1)]'s Articles
[Guo, Danhuai (3)]'s Articles
[Liu, Jiajun (2)]'s Articles
Terms of Use
No data!
Social Bookmark/Share
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.