ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints
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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.
Keywordsscenic view travel route planning heterogeneous digital footprint
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Chao Chen and Xia Chen contributed equally on this work. The work was partially supported by the National Natural Science Foundation of China (Grant Nos. 61602067, 61402369 and 61572048), the Fundamental Research Funds for the Central Universities (106112015CDJXY180001), Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University), and Chongqing Basic and Frontier Research Program (cstc2015jcyjA00016).
- 8.Cheng Z Y, Caverlee J, Lee K, Sui D Z. Exploring millions of footprints in location sharing services. ICWSM, 2011, 2011: 81–88Google Scholar
- 9.Scellato S, Noulas A, Mascolo C. Exploiting place features in link prediction on location-based social networks. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2011, 1046–1054Google Scholar
- 10.Zheng Y T, Zha Z J, Chua T S. Mining travel patterns from geotagged photos. ACM Transactions on Intelligent Systems and Technology (TIST), 2012, 3(3): 56Google Scholar
- 11.Kurashima T, Iwata T, Irie G, and Fujimura K. Travel route recommendation using geotags in photo sharing sites. In: Proceedings of ACM International Conference on Information and Knowledge Management. 2010, 579–588Google Scholar
- 13.Alivand M, Hochmair H. Extracting scenic routes from vgi data sources. In: Proceedings of ACM SIGSPATIAL International Workshop on Crowdsourced and Volunteered Geographic Information. 2013, 23–30Google Scholar
- 16.Zheng Y T, Yan S C, Zha Z J, Li Y Q, Zhou X D, Chua T S, Jain R. GPSView: a scenic driving route planner. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 2013, 9(1): 3Google Scholar
- 18.Simon I, Snavely N, Seitz S M. Scene summarization for online image collections. In: Proceedings of the 11th IEEE International Conference on Computer Vision. 2007, 1–8Google Scholar
- 20.Yang Y Y, Gong Z G. Identifying points of interest by self-tuning clustering. In: Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2011, 883–892Google Scholar
- 21.Jin X, Gallagher A, Cao L L, Luo J B, Han J W. The wisdom of social multimedia: using flickr for prediction and forecast. In: Proceedings of the 18th ACM International Conference on Multimedia. 2010, 1235–1244Google Scholar
- 22.Abbasi R, Chernov S, Nejdl W, Paiu R, Staab S. Exploiting flickr tags and groups for finding landmark photos. In: Proceedings of European Conference on Information Retrieval. 2009, 654–661Google Scholar
- 24.Lu X, Wang C H, Yang J M, Pang Y W, Zhang L. Photo2trip: generating travel routes from geo-tagged photos for trip planning. In: Proceedings of the 18th ACM International Conference on Multimedia. 2010, 143–152Google Scholar
- 25.Kurashima T, Iwata T, Hoshide T, Takaya N, Fujimura K. Geo topic model: joint modeling of user’s activity area and interests for location recommendation. In: Proceedings of the 6th ACM International Conference on Web Search and Data Mining. 2013, 375–384Google Scholar
- 26.Wang H, Terrovitis M, Mamoulis N. Location recommendation in location-based social networks using user check-in data. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 2013, 374–383Google Scholar
- 28.Dehne F, Omran M T, Sack J R. Shortest paths in time-dependent fifo networks using edge load forecasts. In: Proceedings of the 2nd International Workshop on Computational Transportation Science. 2009, 1–6Google Scholar
- 32.Quercia D, Schifanella R, Aiello L M. The shortest path to happiness: Recommending beautiful, quiet, and happy routes in the city. In: Proceedings of the 25th ACM conference on Hypertext and Social Media. 2014, 116–125Google Scholar
- 33.Kim J, Cha M, Sandholm T. Socroutes: safe routes based on tweet sentiments. In: Proceedings of the 23rd ACM International Conference on World Wide Web. 2014, 179–182Google Scholar