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MCM: A Robust Map Matching Method by Tracking Multiple Road Candidates

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Algorithmic Aspects in Information and Management (AAIM 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13513))

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Abstract

Map matching is to track the positions of vehicles on the road network based on the positions provided by GPS (Global Positioning System). Balancing localization accuracy and computation efficiency is a key problem in online map matching, for which, existing methods HMM and MHT mainly use Markov assumption which drops early unused data. Although the roads to explore can be remarkably reduced by the Markov assumption, miss-of-match and matching breaks may occur if the GPS data is highly noisy. To address these problems, this paper presents Multiple Candidate Matching (MCM) to improve the robustness of map matching by using historical trajectory data. MCM tracks multiple road candidates in the map matching process while limiting the number of road candidates by excluding the routes whose likelihood are below a threshold. Numerical experiments in large-scale data sets show that MCM is very promising in terms of accuracy, computational efficiency, and robustness. Mismatching problems caused by Markov assumption can be resolved effectively when compared with state-of-the-art online map matching methods.

This work was supported in part by the National Natural Science Foundation of China Grant No. 61972404, 12071478, Public Computing Cloud, Renmin University of China.

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References

  1. Bernstein, D., Kornhauser, A., et al.: An introduction to map matching for personal navigation assistants (1996)

    Google Scholar 

  2. Blanco-Delgado, N., Nunes, F.D.: Multipath estimation in multicorrelator GNSS receivers using the maximum likelihood principle. IEEE Trans. Aerosp. Electron. Syst. 48(4), 3222–3233 (2012)

    Article  Google Scholar 

  3. Chaggara, R., Macabiau, C., Chatre, E.: Using GPS multicorrelator receivers for multipath parameters estimation. In: Proceedings of the 15th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS 2002), pp. 477–492 (2002)

    Google Scholar 

  4. Chao, P., Xu, Y., Hua, W., Zhou, X.: A survey on map-matching algorithms. In: Borovica-Gajic, R., Qi, J., Wang, W. (eds.) ADC 2020. LNCS, vol. 12008, pp. 121–133. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-39469-1_10

    Chapter  Google Scholar 

  5. Chen, B.Y., Yuan, H., Li, Q., Lam, W.H., Shaw, S.L., Yan, K.: Map-matching algorithm for large-scale low-frequency floating car data. Int. J. Geogr. Inf. Sci. 28(1), 22–38 (2014)

    Article  Google Scholar 

  6. Cui, Y., Ge, S.S.: Autonomous vehicle positioning with GPS in urban canyon environments. IEEE Trans. Robot. Autom. 19(1), 15–25 (2003)

    Article  Google Scholar 

  7. Dogramadzi, M., Khan, A.: Accelerated map matching for GPS trajectories. IEEE Trans. Intell. Transp. Syst. 23(5), 4593–4602 (2021)

    Article  Google Scholar 

  8. Goh, C.Y., Dauwels, J., Mitrovic, N., Asif, M.T., Oran, A., Jaillet, P.: Online map-matching based on Hidden Markov Model for real-time traffic sensing applications. In: 2012 15th International IEEE Conference on Intelligent Transportation Systems, pp. 776–781. IEEE (2012)

    Google Scholar 

  9. Jagadeesh, G.R., Srikanthan, T.: Online map-matching of noisy and sparse location data with hidden Markov and route choice models. IEEE Trans. Intell. Transp. Syst. 18(9), 2423–2434 (2017)

    Article  Google Scholar 

  10. Li, G., Lou, L., Zheng, P., et al.: Route restoration method for sparse taxi GPS trajectory based on Bayesian network. Tehnički vjesnik 28(2), 668–677 (2021)

    Google Scholar 

  11. Li, Y., Huang, Q., Kerber, M., Zhang, L., Guibas, L.: Large-scale joint map matching of GPS traces. In: Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 214–223 (2013)

    Google Scholar 

  12. Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 352–361 (2009)

    Google Scholar 

  13. Luo, L., Hou, X., Cai, W., Guo, B.: Incremental route inference from low-sampling GPS data: an opportunistic approach to online map matching. Inf. Sci. 512, 1407–1423 (2020)

    Article  Google Scholar 

  14. Mohamed, R., Aly, H., Youssef, M.: Accurate real-time map matching for challenging environments. IEEE Trans. Intell. Transp. Syst. 18(4), 847–857 (2016)

    Article  Google Scholar 

  15. Newson, P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 336–343 (2009)

    Google Scholar 

  16. Spangenberg, M., Giremus, A., Poire, P., Tourneret, J.Y.: Multipath estimation in the global positioning system for multicorrelator receivers. In: 2007 IEEE International Conference on Acoustics, Speech and Signal Processing-ICASSP’07, vol. 3, pp. III-1277. IEEE (2007)

    Google Scholar 

  17. Taguchi, S., Koide, S., Yoshimura, T.: Online map matching with route prediction. IEEE Trans. Intell. Transp. Syst. 20(1), 338–347 (2018)

    Article  Google Scholar 

  18. Wei, H., Wang, Y., Forman, G., Zhu, Y., Guan, H.: Fast Viterbi map matching with tunable weight functions. In: Proceedings of the 20th International Conference on Advances in Geographic Information Systems, pp. 613–616 (2012)

    Google Scholar 

  19. Yuan, J., Zheng, Y., Zhang, C., Xie, X., Sun, G.Z.: An interactive-voting based map matching algorithm. In: 2010 Eleventh International Conference on Mobile Data Management, pp. 43–52. IEEE (2010)

    Google Scholar 

  20. Zeng, Z., Zhang, T., Li, Q., Wu, Z., Zou, H., Gao, C.: Curvedness feature constrained map matching for low-frequency probe vehicle data. Int. J. Geogr. Inf. Sci. 30(4), 660–690 (2016)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Information, Renmin University of China, Beijing, 100872, People’s Republic of China

    Wanting Li, Yongcai Wang, Deying Li & Xiaojia Xu

Authors
  1. Wanting Li
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  2. Yongcai Wang
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  3. Deying Li
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  4. Xiaojia Xu
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Corresponding author

Correspondence to Yongcai Wang .

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Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Qiufen Ni

  2. University of Texas at Dallas, Richardson, TX, USA

    Weili Wu

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Li, W., Wang, Y., Li, D., Xu, X. (2022). MCM: A Robust Map Matching Method by Tracking Multiple Road Candidates. In: Ni, Q., Wu, W. (eds) Algorithmic Aspects in Information and Management. AAIM 2022. Lecture Notes in Computer Science, vol 13513. Springer, Cham. https://doi.org/10.1007/978-3-031-16081-3_20

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  • DOI: https://doi.org/10.1007/978-3-031-16081-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16080-6

  • Online ISBN: 978-3-031-16081-3

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