Abstract
We describe a prototype implementation of a real time traffic monitoring service that uses GPS positioning information received from moving vehicles to calculate average speed and travel time and assign them to road segments. The primary factor for reliability of determined parameters is the correct calculation of a vehicle location on a road segment, which is realized by a map-matching algorithm. We present an a new incremental map-matching algorithm based on Hidden Markov Model (HMM). A HMM state corresponds to a road segment and a sensor reading to an observation. The HMM model is updated on arrival of new GPS data by alternating operations: expansion and contraction. In the later step a part of determined trajectory is output. We present also results of conducted experiments.
This work is supported by the European Regional Development Fund within INSIGMA project no. POIG.01.01.02-00-062/09.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
INSIGMA project, http://insigma.kt.agh.edu.pl (last accessed: December 2013)
CodeCodexWiki: Calculate distance between two points on a globe, http://www.codecodex.com/wiki/Calculate_Distance_Between_Two_Points_on_a_Globe (online: last accessed: December 2013)
Fu, M., Li, J., Wang, M.: A hybrid map matching algorithm based on fuzzy comprehensive judgment. In: Proceedings of the 7th International IEEE Conference on Intelligent Transportation Systems, pp. 613–617 (2004)
Google Official Blog: The bright side of sitting in traffic: Crowdsourcing road congestion data, http://googleblog.blogspot.com/2009/08/bright-side-of-sitting-in-traffic.html (online: last accessed: December 2013)
Greenfeld, J.S.: Matching GPS observations to locations on a digital map. In: National Research Council (US). Transportation Research Board. Meeting (81st: 2002: Washington, DC). Preprint CD-ROM (2002)
Gurtam: Commercial GPS solutions for vehicle tracking and fleet management, http://gurtam.com/en/ (online: last accessed: December 2013)
Gustafsson, F., Gunnarsson, F., Bergman, N., Forssell, U., Jansson, J., Karlsson, R., Nordlund, P.J.: Particle filters for positioning, navigation, and tracking. IEEE Transactions on Signal Processing 50(2), 425–437 (2002)
INRIX: Inrix home page, http://www.inrix.com/default.asp (online: last accessed: December 2013)
Marchal, F., Hackney, J., Axhausen, K.: Efficient map-matching of large GPS data sets-tests on a speed monitoring experiment in Zurich. Arbeitsbericht Verkehrs-und Raumplanung 244 (2004)
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. ACM (2009)
Ochieng, W.Y., Quddus, M., Noland, R.B.: Map-matching in complex urban road networks. Revista Brasileira de Cartografia 2(55) (2009)
OpenStreetMap: OpenStreetMap Wiki (2013), http://wiki.openstreetmap.org/wiki/Main_Page (Online; accessed December 2013)
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)
Quddus, M.A., Ochieng, W.Y., Zhao, L., Noland, R.B.: A general map matching algorithm for transport telematics applications. GPS Solutions 7(3), 157–167 (2003), http://dx.doi.org/10.1007/s10291-003-0069-z
Rabiner, L., Juang, B.: An introduction to hidden Markov models. IEEE ASSP Magazine 3(1), 4–16 (1986)
Szwed, P., Kadluczka, P., Chmiel, W., Glowacz, A., Sliwa, J.: Ontology based integration and decision support in the Insigma route planning subsystem. In: FedCSIS, pp. 141–148 (2012)
Thiagarajan, A., Ravindranath, L., LaCurts, K., Madden, S., Balakrishnan, H., Toledo, S., Eriksson, J.: Vtrack: accurate, energy-aware road traffic delay estimation using mobile phones. In: Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems, pp. 85–98. ACM (2009)
University of California, Berkeley: Mobile millenium project, http://traffic.berkeley.edu/ (online: last accessed: December 2013)
White, C.E., Bernstein, D., Kornhauser, A.L.: Some map matching algorithms for personal navigation assistants. Transportation Research Part C: Emerging Technologies 8(1), 91–108 (2000)
Wu, D., Zhu, T., Lv, W., Gao, X.: A heuristic map-matching algorithm by using vector-based recognition. In: International Multi-Conference on Computing in the Global Information Technology, ICCGI 2007, p. 18 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Szwed, P., Pekala, K. (2014). Map-Matching in a Real-Time Traffic Monitoring Service. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures, and Structures. BDAS 2014. Communications in Computer and Information Science, vol 424. Springer, Cham. https://doi.org/10.1007/978-3-319-06932-6_41
Download citation
DOI: https://doi.org/10.1007/978-3-319-06932-6_41
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-06931-9
Online ISBN: 978-3-319-06932-6
eBook Packages: Computer ScienceComputer Science (R0)