Abstract
Efficient vehicle tracking and trajectory analyzing are important to emergency scheduling and commanding as they are essential for assessing and understanding the current situation. One of the fundamental techniques is map matching which aligns the trajectory points of moving objects to the underlying traffic network. In this paper, we propose an efficient map matching algorithm called EM3 to meet the requirement of high efficiency and accuracy posed by emergency management. Instead of matching every single GPS point, the algorithm concentrates on those close to intersections and infers the matching results of intermediated ones, which makes the algorithm quite efficient and robust to edge simplification. To provide accurate matching results in ambiguous situations, e.g., road intersections and parallel paths, we further propose EM3*, which is based on the multi-hypothesis technique with novel candidate generation and management methods. The results of experiments performed on real datasets demonstrate that EM3* is efficient while maintaining the high accuracy.
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References
Liu, K., Deng, K., Ding, Z., Li, M., Zhou, X.: Moir/mt: Monitoring large-scale road network traffic in real-time. PVLDB 2, 1538–1541 (2009)
Li, M., Zhang, Y., Wang, W.: Analysis of congestion points based on probe car data. In: 12th International IEEE Conference on Intelligent Transportation Systems, ITSC 2009, pp. 1–5 (2009)
Gonzalez, H., Han, J., Li, X., Myslinska, M., Sondag, J.P.: Adaptive fastest path computation on a road network: a traffic mining approach. In: VLDB, pp. 794–805 (2007)
Chen, Y., Xiao, D.: Real-time traffic management under emergency evacuation based on dynamic traffic assignment. In: IEEE International Conference on Automation and Logistics, ICAL 2008, pp. 1376–1380 (2008)
Ding, Z., Deng, K.: Collecting and managing network-matched trajectories of moving objects in databases. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part I. LNCS, vol. 6860, pp. 270–279. Springer, Heidelberg (2011)
White, C.E., Bernstein, D., Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Transportation Research Part C: Emerging Technologies 8(1-6), 91–108 (2000)
Greenfeld, J.S.: Matching gps observations to locations on a digital map. In: Transportation Research Board. Meeting, Washington, D.C (2002)
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., Huang, Y.: Map-matching for low-sampling-rate gps trajectories. In: GIS, Seattle, Washington, pp. 352–361 (2009)
Newson, P., Krumm, J.: Hidden markov map matching through noise and sparseness. In: GIS, Seattle, WA, USA, pp. 336–343 (2009)
Zheng, K., Zheng, Y., Xie, X., Zhou, X.: Reducing uncertainty of low-sampling-rate trajectories. In: ICDE, Washington, DC, USA, pp. 1144–1155 (2012)
Pink, O., Hummel, B.: A statistical approach to map matching using road network geometry, topology and vehicular motion constraints. In: IEEE ITSC, pp. 862–867 (2008)
Wenk, C., Salas, R., Pfoser, D.: Addressing the need for map-matching speed: Localizing globalb curve-matching algorithms. In: SSDBM, Washington, DC, USA, pp. 379–388 (2006)
Brakatsoulas, S., Pfoser, D., Salas, R., Wenk, C.: On map-matching vehicle tracking data. In: VLDB, Trondheim, Norway, pp. 853–864 (2005)
Quddus, M.A., Ochieng, W.Y., Noland, R.B.: Current map-matching algorithms for transport applications: State-of-the art and future research directions. Transportation Research Part C: Emerging Technologies 15(5), 312–328 (2007)
Syed, S., Cannon, M.E.: Fuzzy logic based-map matching algorithm for vehicle navigation system in urban canyons. In: National Technical Meeting of The Institute of Navigation, San Diego, CA, pp. 982–993 (2004)
Reid, D.: An algorithm for tracking multiple targets. IEEE Transactions on Automatic Control 24(6), 843–854 (1979)
Pyo, J.-S., Shin, D.-H., Sung, T.-K.: Development of a map matching method using the multiple hypothesis technique. In: Proceedings of 2001 IEEE Intelligent Transportation Systems, Oakland, CA, USA, pp. 23–27 (2001)
Liu, K., Li, Y., He, F., Xu, J., Ding, Z.: Effective map-matching on the most simplified road network. In: GIS, Redondo Beach, CA, USA, pp. 609–612 (2012)
Zhou, J., Golledge, R.: A three-step general map matching method in the gis environment: travel/transportation study perspective. International Journal of Geographical Information System 8(3), 243–260 (2006)
ACM SIGSPATIAL Cup 2012: Training data sets (2012), http://depts.washington.edu/giscup/home
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Li, Y., Liu, K., Xu, J., He, F. (2013). An Efficient Map-Matching Mechanism for Emergency Scheduling and Commanding. In: Gao, Y., et al. Web-Age Information Management. WAIM 2013. Lecture Notes in Computer Science, vol 7901. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39527-7_15
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DOI: https://doi.org/10.1007/978-3-642-39527-7_15
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