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Planning unobstructed paths in traffic-aware spatial networks

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Abstract

Route planning and recommendation have received significant attention in recent years. In this light, we study a novel problem of planning unobstructed paths in traffic-aware spatial networks (TAUP queries) to avoid potential traffic congestions. We propose two probabilistic TAUP queries: (1) a time-threshold query like “what is the path from the check-in desk to the flight SK 1217 with the minimum congestion probability to take at most 45 minutes?”, and (2) a probability-threshold query like “what is the fastest path from the check-in desk to the flight SK 1217 whose congestion probability is less than 20 %?”. These queries are mainly motivated by indoor space applications, but are also applicable in outdoor spaces. We believe that these queries are useful in some popular applications, such as planning unobstructed paths for VIP bags in airports and planning convenient routes for travelers. The TAUP queries are challenged by two difficulties: (1) how to model the traffic awareness in spatial networks practically, and (2) how to compute the TAUP queries efficiently under different query settings. To overcome these challenges, we construct a traffic-aware spatial network G t a (V, E) by analyzing uncertain trajectories of moving objects. Based on G t a (V, E), two efficient algorithms are developed to compute the TAUP queries. The performances of TAUP 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://daisy.aau.dk/bagtrack

  5. http://www.bikely.com/

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

  7. http://www.sharemyroutes.com/

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

  9. For a moving object o, when it arrives at vertex p, vertex p is occupied by other objects, and the number of objects to be processed exceeds the capability of vertex p. Then, object o has to be waiting at p, and this scenario is called congestion. The computation method of congestion time-delay is introduced in Section 3.1.

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Acknowledgments

This work is partly supported by the National Natural Science Foundation of China (NSFC. 61402532), the Science Foundation of China University of Petroleum-Beijing (No. 2462013 YJRC031), and the Excellent Talents of Beijing Program (No. 2013D009051000003).

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Authors and Affiliations

  1. State Key Laboratory of Petroleum Resources and Prospecting, China University of Petroleum, Beijing, China

    Shuo Shang

  2. Department of Computer Science, China University of Petroleum, Beijing, China

    Shuo Shang

  3. CSIRO, Canberra, Australia

    Jiajun Liu

  4. School of ITEE, The University of Queensland, Brisbane, Australia

    Kai Zheng

  5. Department of Computer Science, Aalborg University, Aalborg, Denmark

    Hua Lu & Torben Bach Pedersen

  6. MOE Key Laboratory of Data Engineering and Knowledge Engineering, Renmin University, Beijing, China

    Ji-Rong Wen

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  1. Shuo Shang
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  2. Jiajun Liu
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  3. Kai Zheng
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  4. Hua Lu
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  5. Torben Bach Pedersen
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  6. Ji-Rong Wen
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Correspondence to Shuo Shang.

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Shang, S., Liu, J., Zheng, K. et al. Planning unobstructed paths in traffic-aware spatial networks. Geoinformatica 19, 723–746 (2015). https://doi.org/10.1007/s10707-015-0227-9

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  • DOI: https://doi.org/10.1007/s10707-015-0227-9

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