Abstract
Due to the inaccuracy of GPS devices, the location error of raw GPS points can be up to several hundred meters. Many applications using GPS-based vehicle location data require map-matching to pre-process GPS points by aligning them to a road network. However, existing map-matching algorithms can be limited in accuracy due to various factors including low sampling rates, abnormal GPS points, and dense road networks. In this paper, we propose the design and implementation of HIMM, an HMM-based Interactive Map-Matching system that produces accurate map-matching results through human interaction. The main idea is to involve human annotations in the matching process of some elaborately selected error-prone points and to let the system automatically adjust the matching of the remaining points. We use both real-world and synthetic datasets to evaluate the system. The results show that HIMM can significantly reduce human annotation costs comparing to the baseline methods.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bilgic, M., Mihalkova, L., Getoor, L.: Active learning for networked data. In: Proceedings of ICML, pp. 79–86 (2010)
Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: Proceedings of VLDB, pp. 853–864. VLDB Endowment (2005)
Ding, Y., Liu, S., Pu, J., Ni, L.M.: HUNTS: a trajectory recommendation system for effective and efficient hunting of taxi passengers. In: Proceedings of MDM, vol. 1, pp. 107–116. IEEE (2013)
Ding, Y., Zheng, J., Tan, H., Luo, W., Ni, L.M.: Inferring road type in crowdsourced map services. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds.) DASFAA 2014. LNCS, vol. 8422, pp. 392–406. Springer, Cham (2014). doi:10.1007/978-3-319-05813-9_26
Jagadeesh, G., Srikanthan, T., Zhang, X.: A map matching method for GPS based real-time vehicle location. J. Navig. 57(03), 429–440 (2004)
Karimi, H.A., Conahan, T., Roongpiboonsopit, D.: A methodology for predicting performances of map-matching algorithms. In: Carswell, J.D., Tezuka, T. (eds.) W2GIS 2006. LNCS, vol. 4295, pp. 202–213. Springer, Heidelberg (2006). doi:10.1007/11935148_19
Liao, L., Patterson, D.J., Fox, D., Kautz, H.: Learning and inferring transportation routines. Artif. Intell. 171(5), 311–331 (2007)
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of SIGSPATIAL, pp. 352–361. ACM (2009)
Newson, P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: Proceedings of SIGSPATIAL, pp. 336–343. ACM (2009)
Pink, O., Hummel, B.: A statistical approach to map matching using road network geometry, topology and vehicular motion constraints. In: Proceedings of ITSC, pp. 862–867. IEEE (2008)
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)
Settles, B.: Active learning literature survey, vol. 52, no. 55–66, p. 11. University of Wisconsin, Madison (2010)
Settles, B., Craven, M.: An analysis of active learning strategies for sequence labeling tasks. In: Proceedings of Empirical Methods in Natural Language Processing, pp. 1070–1079. Association for Computational Linguistics (2008)
Wang, G., Zimmermann, R.: Eddy: an error-bounded delay-bounded real-time map matching algorithm using HMM and online Viterbi decoder. In: Proceedings of SIGSPATIAL, pp. 33–42. ACM (2014)
Xue, A.Y., Qi, J., Xie, X., Zhang, R., Huang, J., Li, Y.: Solving the data sparsity problem in destination prediction. VLDB J. 24(2), 219–243 (2015)
Acknowledgments
This work is supported in part by NSFC Grant 61300030 and the National Key Basic Research and Development Program of China (973) Grant 2014CB340304.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Zhou, X., Ding, Y., Tan, H., Luo, Q., Ni, L.M. (2017). HIMM: An HMM-Based Interactive Map-Matching System. In: Candan, S., Chen, L., Pedersen, T., Chang, L., Hua, W. (eds) Database Systems for Advanced Applications. DASFAA 2017. Lecture Notes in Computer Science(), vol 10178. Springer, Cham. https://doi.org/10.1007/978-3-319-55699-4_1
Download citation
DOI: https://doi.org/10.1007/978-3-319-55699-4_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-55698-7
Online ISBN: 978-3-319-55699-4
eBook Packages: Computer ScienceComputer Science (R0)