ScenicPlanner: planning scenic travel routes leveraging heterogeneous   user-generated digital footprints

doi:10.1007/s11704-016-5550-2

CORC > 北京大学 > 信息科学技术学院

	ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints
	Chen, Chao ; Chen, Xia ; Wang, Zhu ; Wang, Yasha ; Zhang, Daqing
刊名	FRONTIERS OF COMPUTER SCIENCE
	2017
关键词	scenic view travel route planning heterogeneous digital footprint INTELLIGENCE
DOI	10.1007/s11704-016-5550-2
英文摘要	To facilitate the travel preparation process to a city, a lot of work has been done to recommend a POI or a sequence of POIs automatically to satisfy users' needs. However, most of the existing work ignores the issue of planning the detailed travel routes between POIs, leaving the task to online map services or commercial GPS navigators. Such a service or navigator in terms of suggesting the shortest travel distance or time, which cannot meet the diverse requirements of users. For instance, in the case of traveling by driving for leisure purpose, the scenic view along the travel routes would be of great importance to users, and a good planning service should put the sceneries of the route in higher priority rather than the distance or time taken. To this end, in this paper, we propose a novel framework called ScenicPlanner for route recommendation, leveraging a combination of geotagged image and check-in digital footprints from locationbased social networks (LBSNs). First, we enrich the road network and assign a proper scenic view score to each road segment to model the scenic road network, by extracting relevant information from geo-tagged images and check-ins. Then, we apply heuristic algorithms to iteratively add road segment and determine the travelling order of added road segments with the objective of maximizing the total scenic view score while satisfying the user-specified constraints (i.e., origin, destination and the total travel distance). Finally, to validate the efficiency and effectiveness of the proposed framework, we conduct extensive experiments on three real-world data sets from the Bay Area in the city of San Francisco, which contain a road network crawled from OpenStreetMap, more than 31 000 geo-tagged images generated by 1 571 Flickr users in one year, and 110 214 check-ins left by 15 680 Foursquare users in six months.; National Natural Science Foundation of China [61602067, 61402369, 61572048]; Fundamental Research Funds for the Central Universities [106112015CDJXY180001]; Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University); Chongqing Basic and Frontier Research Program [cstc2015jcyjA00016]; SCI(E); 中国科学引文数据库(CSCD); ARTICLE; 1; 61-74; 11
语种	英语
内容类型	期刊论文
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/475490]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Chen, Chao,Chen, Xia,Wang, Zhu,et al. ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints[J]. FRONTIERS OF COMPUTER SCIENCE,2017.
APA	Chen, Chao,Chen, Xia,Wang, Zhu,Wang, Yasha,&Zhang, Daqing.(2017).ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints.FRONTIERS OF COMPUTER SCIENCE.
MLA	Chen, Chao,et al."ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints".FRONTIERS OF COMPUTER SCIENCE (2017).