Abstract
Floating car data (FCD) are one of the most important sources of traffic data. However, their processing requires several steps that may seem trivial but have far-reaching consequences. One such step is map-matching, i.e. assignment of the FCD measurement to the correct road segment. While it can be done very simply by assigning the point of measurement to the closest road, this approach may produce a highly undesirable level of error. The second challenge connected with processing of FCD measurements is missing measurements. They are usually caused by the shortcomings of GPS technology (e.g. the satellites can be obscured by buildings or bridges) and may deny us many measurements during longer downtimes. The last problem we will solve is the assignment of measurements to very short segments. FCD measurements are taken in periodic steps for several seconds long. However, some road segments are very short and can be passed by a car in the shorter interval. Such segments are therefore very difficult to monitor. We plan to solve all these problems through a combination of geometric map-matching with the Dijkstra’s shortest path algorithm.
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Fujise, M., Kato, A., Sato, K., & Harada, H. (2002). Intelligent transport systems. Wireless communication technologies: New multimedia systems. In The International Series in Engineering and Computer Science (Vol. 564).
https://xtralis.com/p.cfm?s=22&p=381. 30.3.2018.
Pfoser, D. (2008). Floating car data, Encyclopedia of GIS. US: Springer.
Khan, R., Landfeldt, B., & Dhamdher, A. (2012). Predicting travel times in dense and highly varying road traffic networks using starima models. Technical report, School of Information Technologies, The University of Sydney and National ICT Australia.
Wu, Y., Chen, F., Lu, C., & Smith, B. (2011) Traffic flow prediction for urban network using spatiotemporal random effects model. In 91st Annual Meeting of the Transportation Research Board.
Ma, Y., van Zuylen, H. J., & van Dalen, J. (2012). Freight origin-destination matrix estimation based on multiple data sources: Methodological study. In TRB 2012 Annual Meeting.
de Fabritiis, C., Ragona, R., & Valenti, G. (2008). Traffic estimation and prediction based on real time floating car data. In Proceedings of 11th International IEEE Conference on Intelligent Transportation Systems. ITSC 2008.
Kuhns, G., Ebendt, R., Wagner, P., Sohr, A., & Brockfeld, E. (2011). Self-evaluation of floating car data based on travel times from actual vehicle trajectories. In IEEE Forum on Integrated and Sustainable Transportation Systems.
Li, M., Zhang, Y., & Wang, W. (2009). Analysis of congestion points based on probe car data. In Proceedings of International IEEE Conference on Intelligent Transportation Systems, ITSC ’09.
Graser, A., Dragaschnig, M., Ponweiser, W., Koller, H., Marcinek, M., & Widhalm, P. (2012) FCD in the real world—System capabilities and applications. In Proceedings of 19th ITS World Congress (p. 7), Vienna, Austria.
Hofmann-Wellenhof, B., Lichtenegger, H., & Wasle, E. (2008). GNSS—Global navigation satellite systems. Wien: Springer.
Knuth, D. E. (1977). A generalization of Dijkstra’s algorithm. Information Processing Letters, 6(1).
Hashemi, M., & Karimi, H. A. (2014). A critical review of real-time map-matching algorithms: Current issues and future directions. Computers, Environment and Urban Systems, 48.
White, C. E., & Bernstein, D., Kornhauser, A. L. (2000). Some map matching algorithms for personal navigation assistants. Transportation Research Part C: Emerging Technologies, 8(1).
Taylor, G., Blewitt, G., Steup, D., & Corbett, S. (2001). Car road reduction filtering for GPS-GIS Navigation. Transactions in GIS, 5(3).
Srinivasan, D., Cheu, R. L., & Tan, C. W. (2003). Development of an improved ERP system using GPS and AI techniques. In Proceedings of Intelligent Transportation Systems Conference (Vol. 1).
Quddus, M. A., Ochieng, W. Y., Zhao, L., & Noland, R. B. (2003). A general map matching algorithm for transport telematics applications. GPS Solutions, 7(3).
Velaga, N. R., Quddus, M. A., & Bristow, A. L. (2009). Developing an enhanced weight-based topological map-matching algorithm for intelligent transport systems. Transportation Research Part C: Emerging Technologies, 17(6).
Li, L., Quddus, M., & Zhao, L. (2013). High accuracy tightly-coupled integrity monitoring algorithm for map-matching. Transportation Research Part C, 36.
Kim, S., & Kim, J.-H. (2001). Adaptive fuzzy-network-based C-measure map-matching algorithm for car navigation system. IEEE Transactions on Industrial Electronics, 48(2).
Quddus, M. A., Noland, R. B., & Ochieng, W. Y. (2006). A high accuracy fuzzy logic based map matching algorithm for road transport. Journal of Intelligent Transportation Systems, 10(3).
Yuan, L., Li, D., & Hu, S. (2018). A map-matching algorithm with low-frequency floating car data based on matching path. EURASIP Journal on Wireless Communications and Networking, 146(1).
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.
Ren, M., & Karimi, H. A. (2009). A hidden Markov model-based map-matching algorithm for wheelchair navigation. Journal of Navigation, 62(3).
Che, M., Wang, Y., Zhang, C., & Cao, X. (2018). An enhanced hidden Markov map matching model for floating car data. Sensors, 18(6).
Haklay, M., & Weber, P. (2008). OpenStreetMap: User-generated street maps. In IEEE Pervasive Computing, 7(4).
Ptošek, V., Ševčík, J., Martinovič, J., Slaninová, K., Rapant, L., & Cmar, R. (2018). Real time traffic simulator for self-adaptive navigation system validation. In Proceedings of EMSS-HMS: Modeling & Simulation in Logistics, Traffic & Transportation.
El Abbous, A., & Samanta, N. (2017). A modeling of GPS error distributions. In Proceedings of 2017 European Navigation Conference (ENC).
Acknowledgements
This work was supported by The Ministry of Education, Youth and Sports from the National Programme of Sustainability (NPU II) project “IT4Innovations excellence in science - LQ1602”. This work has been partially funded by ANTAREX, a project supported by the EU H2020 FET-HPC programme under grant 671623.
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Ptošek, V., Rapant, L., Martinovič, J. (2020). Floating Car Data Map-Matching Utilizing the Dijkstra’s Algorithm. In: Sharma, N., Chakrabarti, A., Balas, V. (eds) Data Management, Analytics and Innovation. Advances in Intelligent Systems and Computing, vol 1016. Springer, Singapore. https://doi.org/10.1007/978-981-13-9364-8_9
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