Abstract
Map matching, i.e. matching a moving entity’s position trajectory to an underlying transport network, is a crucial functionality of many location-based services. During the last decade, numerous map-matching algorithms have been proposed, tackling challenging aspects like sparse trajectory data or online matching. This work describes GraphiumMM, an open-source map-matching implementation combining and optimizing geometrical and topological matching concepts from previous works. The implementation aims at highly accurate and performant map matching in online and offline mode taking trajectories with average sampling intervals between 1 and 120 s as input. For evaluating its runtime performance and matching quality, results are compared to results from the open-source map-matcher Barefoot. Results indicate better matching quality and runtime performance especially for sampling intervals from 1 to 15 s in offline and online mode.
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References
Ali M et al (2012) ACM SIGSPATIAL GIS Cup 2012. In: Proceedings of the 20th international conference on advances in geographic information systems—SIGSPATIAL’12, p 597. http://dl.acm.org/citation.cfm?doid=2424321.2424426
Brakatsoulas S et al (2005) On map-matching vehicle tracking data. In: Proceedings of the 31st VLDB conference, Trondheim, Norway, pp 853–864
Goh CY et al (2012) Online map-matching based on hidden Markov model for real-time traffic sensing applications. In: The 15th international IEEE conference on intelligent transportation systems, 117543, pp 776–781. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6338627
Greenfeld JS (2002) Matching GPS observations to locations on a digital map. Trans Res Board 3:13
Hummel B (2006) Map matching for vehicle guidance. In: Dynamic and mobile GIS: investigating space and time
Kim JS et al (1996) Node based map-matching algorithm for car navigation system. In: Proceedings of the 29th ISATA symposium, Florence, pp 121–126
Liu K et al (2012) Effective map-matching on the most simplified road network. In: Proceedings of the 20th international conference on advances in geographic information systems—SIGSPATIAL’12, p 609. http://dl.acm.org/citation.cfm?doid=2424321.2424429
Liu Y, Li Z (2017) A novel algorithm of low sampling rate GPS trajectories on map-matching. EURASIP J Wirel Commun Netw 2017(1):30
Lou Y et al (2009) Map-matching for low-sampling-rate GPS trajectories. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems—GIS’09, p 352. http://portal.acm.org/citation.cfm?doid=1653771.1653820
Mattheis S et al (2014) Putting the car on the map: a scalable map matching system for the open source community. In: Lecture notes in informatics (LNI), Proceedings—Series of the Gesellschaft fur Informatik (GI), P-232, pp 2109–2119
Montenbruck O, Garcia-Fernandez M, Williams J (2006) Performance comparison of semicodeless GPS receivers for LEO satellites. GPS Solutions 10(4):249–261
Newson P, Krumm J (2009) Hidden Markov map matching through noise and sparseness. In: Proceedings of the 17th ACM SIGSPATIAL international conference on advances in geographic information systems—GIS’09, pp 336–343. http://portal.acm.org/citation.cfm?doid=1653771.1653818
Quddus M, Noland R, Ochieng WY (2006) A high accuracy fuzzy logic based map matching algorithm for road transport. J Intell Trans Syst Technol Plann Oper 10(3):103–115
Rigaux P, Scholl M, Voisard A (2002) Spatial databases: with application to GIS
Sauerwein T (2013) Optimization and evaluation of an online map-matching algorithm for mid-range sampling rates. Universität Marburg
Song R et al (2012) Quick map matching using multi-core CPUs. In: Proceedings of the 20th international conference on advances in geographic information systems—SIGSPATIAL’12, pp 605–608. http://dl.acm.org/citation.cfm?doid=2424321.2424428
Tang Y, Zhu AD, Xiao X (2012) An efficient algorithm for mapping vehicle trajectories onto road networks. In: Proceedings of the 20th international conference on advances in geographic information systems—SIGSPATIAL’12. New York, NY, USA, ACM, pp 601–604. http://doi.acm.org/10.1145/2424321.2424427
Velaga NR, Quddus MA, Bristow AL (2009) Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems. Trans Res Part C Emerg Technol 17(6):672–683
White CE, Bernstein D, Kornhauser AL (2000) Some map matching algorithms for personal navigation assistants. Trans Res Part C Emerg Technol 8(1–6):91–108
Yuan J et al (2010) An interactive-voting based map matching algorithm. In: Proceedings—IEEE international conference on mobile data management, pp 43–52
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This work has been partly funded by the Austrian Ministry for Transportation, Innovation and Technology (bmvit).
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Rehrl, K., Gröchenig, S., Wimmer, M. (2018). Optimization and Evaluation of a High-Performance Open-Source Map-Matching Implementation. In: Mansourian, A., Pilesjö, P., Harrie, L., van Lammeren, R. (eds) Geospatial Technologies for All. AGILE 2018. Lecture Notes in Geoinformation and Cartography. Springer, Cham. https://doi.org/10.1007/978-3-319-78208-9_13
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DOI: https://doi.org/10.1007/978-3-319-78208-9_13
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