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

Abstract

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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Acknowledgements

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). https://doi.org/10.1007/s11704-016-5550-2

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Keywords

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