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The TM-RTree: an index on generic moving objects for range queries

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Abstract

Existing works on moving objects mainly focus on a single environment such as free space and road network, and do not investigate the complete trip for humans who can pass several environments, e.g., road network, pavement areas, indoor. In this paper, we consider multiple environments and study moving objects with different transportation modes, also called generic moving objects. We aim to answer a new class of queries supporting three kinds of conditions: temporal, spatial, and transportation modes. To efficiently provide the result, we propose an index structure called TM-RTree, which takes into account the feature of moving objects in different environments and has the capability of managing objects on not only temporal and spatial data but also transportation modes. This property is not maintained by existing indices for moving objects. Different cases on transportation modes are supported. Correspondingly, several algorithms are developed. The TM-RTree and related algorithms are developed in a real DBMS to have a practical and solid result for applications. In the experiment, we conduct the performance evaluation using extensive datasets and compare the proposed technique with the other two competitors, demonstrating the efficiency and significant superiority of our solution in various settings.

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Notes

  1. we model the overall pedestrian area in a city as a large polygon with obstacles inside, denoting areas covered by buildings and roads for vehicles.

  2. more queries see Appendix A

  3. In the implementation, a movement tuple is developed to be a relational tuple containing three attributes (traj_id, box, m). In order to efficiently access the data in the future, we combine each movement tuple with its corresponding subtrip in one relational tuple. The sub trip is represented by a moving object.

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Acknowledgments

This work is supported in part by NSFC under grants 61300052, the Fundamental Research Funds for the Central Universities under grants NZ2013306 and Natural Science Foundation of Jiangsu Province of China under grants BK20130810.

Author information

Authors and Affiliations

  1. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Jianqiu Xu

  2. FernUniversität in Hagen, Hagen, Germany

    Ralf Hartmut Güting

  3. Microsoft Research Asia, Beijing, China

    Yu Zheng

Authors
  1. Jianqiu Xu
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  2. Ralf Hartmut Güting
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  3. Yu Zheng
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Corresponding author

Correspondence to Jianqiu Xu.

Additional information

Most of the work is done when the author is a Ph.D student in FernUniversität in Hagen, Germany.

Appendices

Appendix: – A Range Query Examples

  • Who passed the room No. 123 in the university on Tuesday afternoon?

    • Q.t: Tuesday afternoon

    • Q.s: room No. 123 in the university

    • Q.m: Indoor

  • Find all taxis passing through Alexender street on Saturday.

    • Q.t: Saturday

    • Q.s: Alexender street

    • Q.m: Taxi

  • Find out all people walking through zone A and moving around in a shopping mall on Saturday between 10am and 3pm.

    • Q.t: [10am, 3pm] on Saturday

    • Q.s: a region + a building

    • Q.m: {W a l k, I n d o o r}

  • Find all people who pass room No. 34 at the office building between 9:00am and 12:00am on Monday, and then take a bus to the train station.

    • Q.t: [9am, 12am] on Monday

    • Q.s: a region covering the building and the station

    • Q.m: < Indoor, Walk, Bus, Walk, Train >

  • Find all people who drive through the area A and then walk to the building X on Monday between [8am, 9am].

    • Q.t: [8am, 9am] on Monday

    • Q.s: a region including A and X

    • Q.m: < Car, Walk, Indoor >

Appendix: – B Experimental Statistics

Tables 6, 7, 8, 9, 10, 11, and 12

Table 6 Scaling experiments: Q s i n
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Table 7 Scaling experiments: Q m u l
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Table 8 Scaling experiments: Q s e q
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Table 9 Varying spatial and temporal parameters: Q s i n
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Table 10 Varying spatial and temporal parameters: Q m u l
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Table 11 Varying spatial and temporal parameters: Q s e q
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Table 12 Compare the performance between the TM-RTree and the TM-RTree_sin
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Xu, J., Güting, R.H. & Zheng, Y. The TM-RTree: an index on generic moving objects for range queries. Geoinformatica 19, 487–524 (2015). https://doi.org/10.1007/s10707-014-0218-2

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