Abstract
Map matching is a process of aligning a sequence of location estimates to a sequence of road segments in a road network to reduce the noisiness of the location estimates. Most existing map matching methods are designed based on GPS localization, which has many limitations (e.g. unstable urban operations and power hungry) and not suitable for mobile phone users. In this paper, we propose a map matching method based on Cell-ID localization for mobile phone users. Cell-ID localization is stable and energy efficient, but the location estimates are highly inaccurate, making the existing methods ineffective. For this problem, the proposed method firstly handles the inaccurate location estimates from Cell-ID localization through a series of preprocessing steps, and then uses a HMM (Hidden Markov Model) to align a sequence of location estimates to a sequence of road segments. The experimental results based on a real-world dataset collected in an urban environment have demonstrated the effectiveness of the proposed approach.
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Toledo-Moreo, R., Bétaille, D., Peyret, F.: Lane-level integrity provision for navigation and map matching with GNSS, dead reckoning, and enhanced maps. IEEE Trans. Intell. Transp. Syst. 11(1), 100–112 (2010)
Zolfpour-Arokhlo, M., Selamat, A., Hashim, S.Z.: Route planning model of multi-agent system for a supply chain management. Expert Syst. Appl. 40(5), 1505–1518 (2013)
Janecek, A., Hummel, K.A., Valerio, D., Ricciato, F., Hlavacs. H.: Cellular data meet vehicular traffic theory: location area updates and cell transitions for travel time estimation. In: Proceedings of the ACM Conference on Ubiquitous Computing (UbiComp), pp. 361-370. (2012)
Greenfeld, J.S.: Matching GPS observations to locations on a digital map. In: Proceedings of the Annual Meeting of the Transportation Research Board (2002)
Newson. P., Krumm, J.: Hidden Markov map matching through noise and sparseness. In: Proceedings of the SIGSPATIAL Conference on Advanced in Geographic Information Systems (GIS), pp. 336-343. (2009)
Liu, J., Tian, Y., Yu, X., Yang, Z., Jia, X., Ma, C., Xu, Z.: A multi-source approach for bug triage. Int. J. Softw. Eng. Knowl. Eng. 26(9–10), 1593–1604 (2016)
Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C: On map-matching vehicle tracking data. In: Proceedings of the International Conference on Very Large Data Bases (VLDB), pp. 853-864. (2005)
Liu, J., Yu, X., Xu, Z., Choo, K.R., Hong, L., Cui, X.: A cloud-based taxi trace mining framework for smart city. Softw. Pract. Exper. 47(8), 1081–1094 (2017)
Quddus, M.A., Ochieng, W.Y., Noland, R.B.: Current map-matching algorithms for transport applications: state-of-the-art and future research directions. Transp. Res. Part C 15(5), 312–328 (2007)
Ding, Y., Xu, Z., Zhang, Y., Sun, K.: Fast lane detection based on bird’s eye view and improved random sample consensus algorithm. Multimed. Tools Appl. 76(21), 22979–22998 (2017)
Quddus, M.A., Ochieng, W.Y., Zhao, L., Noland, R.B.: A general map matching algorithm for transport telematics applications. GPS Solut. 7(3), 157–167 (2003)
Rose, G.: Mobile phones as traffic probes: practices, prospects and issues. Transp. Rev. 26(3), 275–291 (2006)
Thiagarajan, A., Biagioni, J., Gerlich, T., Eriksson, J.: Cooperative transit tracking using smart-phones. In: Proceedings of the ACM Conference on Embedded Networked Sensor Systems (SenSys), pp. 85–98. (2010)
Paek, J., Kim, J., Govindan, R.: Energy-efficient rate-adaptive GPS-based localization for smartphones. In: Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys), pp. 299–314 (2010)
Yang, G.: Discovering significant places from mobile phones: a mass market solution. In: Proceedings of the International Conference on Mobile Entity Localization and Tracking in GPS-less Environments (MELT), pp. 34–49 (2009)
Aly H, Youssef M (2015) semMatch: Road semantics-based accurate map matching for challenging positioning data. Proceedings of the ACM SIGSPATIAL Conference on Advances in Geographic Information Systems
LaMarca, A., Chawathe, Y., Consolvo, S., Hightower, J., Smith, I., Scott, J.: Place lab: device localization using radio beacons in the wild. In: Proceedings of the International Conference on Pervasive Computing, pp. 116–133 (2005)
Laasonen, K., Raento, M., Toivonen, H.: Adaptive on-device location recognition. In: Proceedings of the International Conference on Pervasive Computing, pp. 287–304 (2004)
Bayir, M.A., Demirbas, M., Eagle, N.: Mobility profiler: a framework for discovering mobility profiles of cell phone users. Pervasive Mob. Comput. 6(4), 435–454 (2010)
Trevisani, E., Vitaletti, A.: Cell-ID location technique, limits and benefits: an experimental study. In: Proceedings of the IEEE Workshop on Mobile Computing Systems and Applications, pp. 51–60 (2004)
Thiagarajan, A., Ravindranath, L., Balakrishnan, H., Madden, S., Girod, L.: Accurate, low-energy trajectory mapping for mobile devices. In: Proceedings of the USENIX Conference on Networked Systems Design and Implementation (NSDI) (2011)
Yuan, Y., Guan, W., Qiu, W.: Map matching of mobile probes based on handover location technology. In: Proceedings of the International Conference on Networking, Sensing and Control (ICNSC), pp. 587–592 (2010)
Becker, R.A., Caceres, R., Hanson, K., Loh, J.M., Urbanek, S., Varshavsky, A., Volinsky, C.: Route classification using cellular handoff patterns. In: Proceedings of the International Conference on Ubiquitous Computing (UbiComp), pp. 123–132 (2011)
Sohn, T., Varshavsky, A., LaMarca, A., Chen, M.Y., Choudhury, T., Smith, I., Consolvo, S., Hightower, J., Griswold, W.G., de Lara, E.: Mobility detection using everyday GSM traces. In: Proceedings of the International Conference on Ubiquitous Computing (UbiComp), pp. 212–224 (2006)
Mun, M., Reddy, S., Shilton, K., Yau, N., Burke, J., Estrin, D., Hansen, M., Howard, E., West, R., Boda, P.: PEIR, the personal environmental impact report, as a platform for participatory sensing systems research. In: Proceedings of the International Conference on Mobile Systems, Applications, and Services (MobiSys), pp. 55–68 (2009)
Guha S, Plarre K, Lissner D, Mitra S, Krishna B (2010) Autowitness: Locating and tracking stolen property while tolerating GPS and radio outages. Proceedings of the ACM Conference on Embedded Sensor System (SenSys), pp. 29-42
Wang, H., Wang, Z., Shen, G., Li, F., Han, S., Zhao, F.: WheelLoc: enabling continuous location service on mobile phone for outdoor scenarios. In: Proceedings of the IEEE International Conference on Computer Communications (INFOCOM) (2013)
Tao, Y., Faloutsos, C., Papadias, D., Liu, B.: Prediction and indexing of moving objects with unknown motion patterns. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), pp. 611–622 (2004)
Viterbi, A.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theory 13, 260–269 (1967)
OpenCellID, http://opencellid.org/
OpenStreetMap, http://www.openstreetmap.org
Bergroth, L., Hakonen, H., Raita, T.: A survey of longest common subsequence algorithms. In: Proceedings of the International Symposium on String Processing Information Retrieval. (2000)
Acknowledgements
This work was supported by the Zhejiang Provincial Natural Science Foundation of China (Nos. LY18F020033, LY15F020025), the Natural Science Foundation of China (Nos. 61772026, 61202282), and the Joint Funds of the National Natural Science Foundation of China (No. U1509214).
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Lin, F., Lv, M., Wang, T. et al. Map matching based on Cell-ID localization for mobile phone users. Cluster Comput 22 (Suppl 3), 6231–6239 (2019). https://doi.org/10.1007/s10586-018-1950-4
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DOI: https://doi.org/10.1007/s10586-018-1950-4