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Discovery of probabilistic nearest neighbors in traffic-aware spatial networks

Abstract

Travel planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of discovering probabilistic nearest neighbors and planning the corresponding travel routes in traffic-aware spatial networks (TANN queries) to avoid potential time delay/traffic congestions. We propose and study four novel probabilistic TANN queries. Thereinto two queries target at minimizing the travel time, including a congestion-probability threshold query, and a time-delay threshold query, while another two travel-time threshold queries target at minimizing the potential time delay/traffic congestion. We believe that TANN queries are useful in many real applications, such as discovering nearby points of interest and planning convenient travel routes for users, and location based services in general. The TANN queries are challenged by two difficulties: (1) how to define probabilistic metrics for nearest neighbor queries in traffic-aware spatial networks, and (2) how to process these TANN queries efficiently under different query settings. To overcome these challenges, we define a series of new probabilistic metrics and develop four efficient algorithms to compute the TANN queries. The performances of TANN queries are verified by extensive experiments on real and synthetic spatial data.

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Notes

  1. http://maps.google.com/

  2. http://www.bing.com/maps/

  3. http://www.mapquest.com

  4. http://www.bikely.com/

  5. http://www.gps-waypoints.net/

  6. http://www.sharemyroutes.com/

  7. http://research.microsoft.com/en-us/projects/geolife/

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (NSFC. 61402532, NSFC. 41371386, and NSFC.61373147), Beijing Nova Program (xx2016078), the Science and Technology Planning Project of Fujian Province (No.2016Y0079), Guangdong Provincial Big Data Collaborative Innovation Center, Shenzhen University, and the Open Research Fund Program of Shenzhen Key Laboratory of Spatial Smart Sensing and Services (Shenzhen University).

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Correspondence to Minhua Lu.

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Shang, S., Zhu, S., Guo, D. et al. Discovery of probabilistic nearest neighbors in traffic-aware spatial networks. World Wide Web 20, 1135–1151 (2017). https://doi.org/10.1007/s11280-016-0425-x

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  • DOI: https://doi.org/10.1007/s11280-016-0425-x

Keywords

  • Traffic-aware spatial networks
  • Probabilistic nearest neighbor
  • Efficiency
  • Spatio-temporal databases