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


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.

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  1. 1.

    Chen C, Zhang D Q, Guo B, Ma X J, Pan G, Wu Z H. Tripplanner: personalized trip planning leveraging heterogeneous crowdsourced digital footprints. IEEE Transactions on Intelligent Transportation Systems, 2015, 16(3): 1259–1273

    Article  Google Scholar 

  2. 2.

    De Choudhury M, Feldman M, Amer-Yahia S, Golbandi N, Lempel R, Yu C. Automatic construction of travel itineraries using social breadcrumbs. In: Proceedings of the 21st ACM Conference on Hypertext and Hypermediain. 2010, 35–44

    Google Scholar 

  3. 3.

    Yu ZW, Xu H, Yang Z, Guo B. Personalized travel package with multipoint- of-interest recommendation based on crowdsourced user footprints. IEEE Transactions on Human-Machine Systems, 2016, 46(1): 151–158

    Article  Google Scholar 

  4. 4.

    Vansteenwegen P, Souffriau W, Berghe G V, Van Oudheusden D. The city trip planner: an expert system for tourists. Expert Systems with Applications, 2011, 38(6): 6540–6546

    Article  Google Scholar 

  5. 5.

    Guo B, Zhang D Q, Yu Z W, Liang Y J, Wang Z, Zhou X S. From the internet of things to embedded intelligence. World Wide Web, 2013, 16(4): 399–420

    Article  Google Scholar 

  6. 6.

    Li X L, Pan G, Wu Z H, Qi G D, Li S J, Zhang D Q, Zhang W S, Wang Z H. Prediction of urban human mobility usinglarge-scale taxi traces and its applications. Frontiers of Computer Science, 2012, 6(1): 111–121

    MathSciNet  Google Scholar 

  7. 7.

    Zhang D Q, Guo B, Yu Z W. The emergence of social and community intelligence. Computer, 2011, 44(7): 21–28

    Article  Google Scholar 

  8. 8.

    Cheng Z Y, Caverlee J, Lee K, Sui D Z. Exploring millions of footprints in location sharing services. ICWSM, 2011, 2011: 81–88

    Google Scholar 

  9. 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, 1046–1054

    Google Scholar 

  10. 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): 56

    Google Scholar 

  11. 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, 579–588

    Google Scholar 

  12. 12.

    Alivand M, Hochmair H, Srinivasan S. Analyzing how travelers choose scenic routes using route choice models. Computers, Environment and Urban Systems, 2015, 50: 41–52

    Article  Google Scholar 

  13. 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, 23–30

    Google Scholar 

  14. 14.

    Wolsey L A. Integer Programming. New York: Wiley-Interscience, 1998

    Google Scholar 

  15. 15.

    Jolliffe I. Principal Component Analysis. New York: John Wiley & Sons, Ltd, 2002

    Google Scholar 

  16. 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): 3

    Google Scholar 

  17. 17.

    Lew A, Mauch H. Dynamic Programming: A Computational Tool. Berlin: Springer, 2006

    Google Scholar 

  18. 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, 1–8

    Google Scholar 

  19. 19.

    Papadopoulos S, Zigkolis C, Kompatsiaris Y, Vakali A. Cluster-based landmark and event detection for tagged photo collections. IEEE MultiMedia, 2011, 18(1): 52–63

    Article  Google Scholar 

  20. 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, 883–892

    Google Scholar 

  21. 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, 1235–1244

    Google Scholar 

  22. 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, 654–661

    Google Scholar 

  23. 23.

    Lee I, Cai G C, Lee K. Exploration of geo-tagged photos through data mining approaches. Expert Systems with Applications, 2014, 41(2): 397–405

    Article  Google Scholar 

  24. 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, 143–152

    Google Scholar 

  25. 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, 375–384

    Google Scholar 

  26. 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, 374–383

    Google Scholar 

  27. 27.

    Hsieh H P, Li C T, Lin S D. Exploiting large-scale check-in data to recommend time-sensitive routes. In: Proceedings of ACM SIGKDD International Workshop on Urban Computing, 2012, 55–62

    Google Scholar 

  28. 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, 1–6

    Google Scholar 

  29. 29.

    Hochmair H. Towards a classification of route selection criteria for route planning tools. In: Fisher P F, ed. Developments in Spatial Data Handling. Springer, 2005, 481–492

    Google Scholar 

  30. 30.

    Fawcett J, Robinson P. Adaptive routing for road traffic. IEEE Computer Graphics and Applications, 2005, 20(3): 46–53

    Article  Google Scholar 

  31. 31.

    Sharker MH, Karimi H A, Zgibor J C. Health-optimal routing in pedestrian navigation services. In: Proceedings of the 1st ACM SIGSPATIAL International Workshop on Use of GIS in Public Health. 2012, 1–10

    Google Scholar 

  32. 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, 116–125

    Google Scholar 

  33. 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, 179–182

    Google Scholar 

  34. 34.

    Galbrun E, Pelechrinis K, Terzi E. Urban navigation beyond shortest route: the case of safe paths. Information Systems, 2016, 57: 160–171

    Article  Google Scholar 

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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).

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Correspondence to Chao Chen.

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Chao Chen is an associate professor at College of Computer Science, Chongqing University, China. He obtained his PhD degree from Pierre and Marie Curie University, France in 2014. His research interests include pervasive computing, social network analysis, data mining from large-scale taxi data, and big data analytics for smart cities.

Xia Chen is currently a master student at Chongqing Automotive Collaborative Innovation Center, Chongqing University (CQU), China. She obtained her bachelor degree from the College of Computer Science, CQU in 2015. Her research interests include travel route planning, crowdsourced data mining for smart services.

Zhu Wang is an associate professor of computer science at Northwestern Polytechnical University (NPU), China. He obtained his PhD degree in computer science from NPU in 2013. His research interests include pervasive computing, mobile social network analysis, and mobile healthcare.

Yasha Wang is a professor at School of Electronics Engineering and Computer Science, Peking University, China. He received his PhD degree in Northeastern University, China in 2003. His research interests include urban data analytics, ubiquitous computing and software reuse.

Daqing Zhang is a professor at School of Electronics Engineering and Computer Science, Peking University, China. He obtained his PhD from University of Rome “La Sapienza” and the University of L’Aquila, Italy in 1996. His research interests include large-scale data mining, urban computing, context-aware computing, and ambient assistive living.

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Chen, C., Chen, X., Wang, Z. et al. ScenicPlanner: planning scenic travel routes leveraging heterogeneous user-generated digital footprints. Front. Comput. Sci. 11, 61–74 (2017).

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  • scenic view
  • travel route planning
  • heterogeneous
  • digital footprint