Human mobility prediction and unobstructed route planning in public transport networks

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
会议名称	15th IEEE International Conference on Mobile Data Management, IEEE MDM 2014
页码	43-48
会议日期	July 15, 2014 - July 18, 2014
会议地点	Brisbane, QLD, Australia
收录类别	EI
出版地	Institute of Electrical and Electronics Engineers Inc.
ISSN	15516245
ISBN	9781479957057
部门归属	(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
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.; 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.
语种	英语
内容类型	会议论文
URI标识	http://ir.iscas.ac.cn/handle/311060/16616
专题	中国科学院软件研究所
通讯作者	Shang, Shuo
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]. Institute of Electrical and Electronics Engineers Inc.,2014:43-48.