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Coarse-Grained Path Planning Under Dynamic Situational Environment

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Spatial Data and Intelligence (SpatialDI 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13614))

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Abstract

With the enrichment of data acquisition means, large amounts of spatiotemporal data in the traffic network are accumulated in real-time by various sensors and multimedia devices. Through status identification, geographic mapping and statistical calculation, a dynamic situational road network is formed. Existing path planning methods based on the static network are inadequate in coping with the traffic situation, which seriously affects the performance of the path planning in accuracy and ETA. Considering the shortage of above, this paper proposes a coarse-grained path planning method under dynamic situational network. By introducing streaming graph partition and constructing a hierarchical index, the computational cost of path planning is effectively reduced. The pathfinding scope is decreased according to the coarse-grained path by progressively shrinking the minimum containing subarea, converting the pathfinding to a small-scale addressing in the hierarchical index. Meanwhile, the situation update and link reconstruction are performed within each tracking subarea, thus making dynamic perception of the traffic situation during travel. Finally, we conduct simulation experiments on the Beijing traffic dataset to verify the effectiveness of the proposed method.

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Notes

  1. 1.

    www.openstreetmap.org.

References

  1. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    Article  MathSciNet  MATH  Google Scholar 

  2. Hart, E., Nilsson, N., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Trans. Syst. Sci. Cybern. SSC 4(2), 100–107 (1968)

    Article  Google Scholar 

  3. Noreen, I., Khan, A., Habib, Z.: A comparison of RRT, RRT* and RRT*-smart path planning algorithms (2016)

    Google Scholar 

  4. Oubbati, O.S., Atiquzzaman, M., Lorenz, P., et al.: SEARCH: an SDN-enabled approach for vehicle path-planning. IEEE Trans. Veh. Technol. 69, 14523–14536 (2020)

    Article  Google Scholar 

  5. Guo, D., Wang, J., Zhao, J.B., et al.: A vehicle path planning method based on a dynamic traffic network that considers fuel consumption and emissions. Sci. Total Environ. 663(MAY 1), 935–943 (2019)

    Article  Google Scholar 

  6. Potamias, M., Bonchi, F., Castillo, C., et al.: Fast shortest path distance estimation in large networks. In: Conference on Information and Knowledge Management, pp. 867–876 (2009)

    Google Scholar 

  7. Yan, C.A., Adb, C., Yi, S.B.: Road-condition-aware dynamic path planning for intelligent vehicles. Proc. Comput. Sci. 174, 419–423 (2020)

    Article  Google Scholar 

  8. Ahmad, A., Din, S., Paul, A., et al.: Real-time route planning and data dissemination for urban scenarios using the internet of things. IEEE Wirel. Commun. 26(6), 50–55 (2019)

    Article  Google Scholar 

  9. Ma, A., Ouimet, M., Cortés, J.: Hierarchical reinforcement learning via dynamic subspace search for multi-agent planning. Auton. Robots. 44(3-4), 485–503 (2020)

    Google Scholar 

  10. Cohen, E., Halperin, E., Kaplan, H., et al.: Reachability and distance queries via 2-hop labels. SIAM J. Comput. 32(5), 542–556 (2002)

    MathSciNet  MATH  Google Scholar 

  11. Akiba, T., Iwata, Y., Yoshida, Y.: Fast exact shortest-path distance queries on large networks by pruned landmark labeling (2013)

    Google Scholar 

  12. Akiba, T., Iwata, Y., Yoshida, Y.: Dynamic and historical shortest-path distance queries on large evolving networks by pruned landmark labeling. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 237–248. ACM (2014)

    Google Scholar 

  13. Narvaez, P., Siu, K.Y., Tzeng, H.Y.: New dynamic algorithms for shortest path tree computation. IEEE/ACM Trans. Netw. 8(6), 734–746 (2000)

    Article  Google Scholar 

  14. Hayashi, T., Akiba, T., Kawarabayashi, K.I.: Fully dynamic shortest-path distance query acceleration on massive networks (2016)

    Google Scholar 

  15. Tretyakov, K., Armas-Cervantes, A., García-Bauelos, L., et al.: Fast fully dynamic landmark-based estimation of shortest path distances in very large graphs. In: ACM International Conference on Information and Knowledge Management. ACM (2011)

    Google Scholar 

  16. Yu, Z., Yu, X., Koudas, N., et al.: Distributed processing of k shortest path queries over dynamic road networks (2020)

    Google Scholar 

  17. Tsalouchidou, I., Baeza-Yates, R., Bonchi, F., et al.: Temporal betweenness centrality in dynamic graphs. Int. J. Data Sci. Anal. 9(3), 257–272 (2020)

    Article  Google Scholar 

  18. Akiba, T., Iwata, Y., Kawarabayashi, K., et al.: Fast shortest-path distance queries on road networks by pruned highway labeling. In: 2014 Proceedings the Sixteenth Workshop on Algorithm Engineering and Experiments (ALENEX), pp. 147–154. Society for Industrial and Applied Mathematics (2014)

    Google Scholar 

  19. Hayashi, T., Akiba, T., Yoshida, Y.: Fully dynamic betweenness centrality maintenance on massive networks. Proc. VLDB Endow. 9(2), 48–59 (2015)

    Article  Google Scholar 

  20. Holme, P.: Modern temporal network theory: a colloquium. Eur. Phys. J. B 88(9), 1–30 (2015). https://doi.org/10.1140/epjb/e2015-60657-4

    Article  Google Scholar 

  21. Taylor, D., Myers, S.A., Clauset, A., et al.: Eigenvector-based centrality measures for temporal networks. Multisc. Model. Simul. 15(1), 537–574 (2017)

    Article  MathSciNet  MATH  Google Scholar 

  22. Alsayed, A., Higham, D.J.: Betweenness in time dependent networks. Chaos Solitons Fractals 72, 35–48 (2015)

    Article  MathSciNet  MATH  Google Scholar 

  23. Takaguchi, T., Yano, Y., Yoshida, Y.: Coverage centralities for temporal networks. Eur. Phys. J. B 89(2), 1–11 (2016). https://doi.org/10.1140/epjb/e2016-60498-7

    Article  Google Scholar 

  24. Lin, C., Han, G., Du, J., et al.: Spatiotemporal congestion-aware path planning toward intelligent transportation systems in software-defined smart city IoT. IEEE Internet Things J. 7(9), 8012–8024 (2020)

    Article  Google Scholar 

  25. Kicki, P., Gawron, T., Skrzypczyński, P.: A Self-supervised learning approach to rapid path planning for car-like vehicles maneuvering in urban environment (2020)

    Google Scholar 

  26. Zakrzewska, A., Bader, D.A.: A dynamic algorithm for local community detection in graphs. In: Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015, pp. 559–564. ACM (2015)

    Google Scholar 

  27. Stanton, I., Kliot, G.: Streaming graph partitioning for large distributed graphs. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1222–1230. ACM (2012)

    Google Scholar 

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Acknowledgments

This work is supported by the National Key R & D Program of China (No. 2022YFF0503900), the Key R & D Program of Shandong Province (No. 2021CXGC010104), the National Natural Science of Foundation of China (No. 62072016).

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Authors and Affiliations

  1. Faculty of Information Technology, Beijing University of Technology, Beijing, China

    Mengmeng Chang, Lutong Li, Nannan Jia & Jing Tian

  2. The Institute of Software, Chinese Academy of Sciences, Beijing, China

    Zhiming Ding

Authors
  1. Mengmeng Chang
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  2. Zhiming Ding
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  3. Lutong Li
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  4. Nannan Jia
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  5. Jing Tian
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Corresponding author

Correspondence to Zhiming Ding .

Editor information

Editors and Affiliations

  1. Peking University, Wuhan, China

    Huayi Wu

  2. Peking University, Beijing, China

    Yu Liu

  3. Harbin Institute of Technology, Harbin, China

    Jianzhong Li

  4. Department of Computer Science and Information, Renmin University of China, Beijing, China

    Xiaofeng Meng

  5. China University of Geosciences (Wuhan), Wuhan, China

    Qingfeng Guan

  6. Southern University of Science and Technology, Shenzhen, China

    Xuan Song

  7. Jiangxi University of Finance and Economics, Nanchang, China

    Guoqiong Liao

  8. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Guoliang Li

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Chang, M., Ding, Z., Li, L., Jia, N., Tian, J. (2022). Coarse-Grained Path Planning Under Dynamic Situational Environment. In: Wu, H., et al. Spatial Data and Intelligence. SpatialDI 2022. Lecture Notes in Computer Science, vol 13614. Springer, Cham. https://doi.org/10.1007/978-3-031-24521-3_1

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  • DOI: https://doi.org/10.1007/978-3-031-24521-3_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-24520-6

  • Online ISBN: 978-3-031-24521-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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