Weitere Kapitel dieses Buchs durch Wischen aufrufen
The ability to infer routes taken by vehicles from sparse and noisy GPS data is of crucial importance in many traffic applications. The task, known as map-matching, can be accurately approached by a popular technique known as ST-Matching. The algorithm is computationally efficient and has been shown to outperform more traditional map-matching approaches, especially on low-frequency GPS data. The major drawback of the algorithm is a lack of confidence scores associated with its outputs, which are particularly useful when GPS data quality is low. In this paper, we propose a probabilistic adaptation of ST-Matching that equips it with the ability to express map-matching certainty using probabilities. The adaptation, called probabilistic ST-Matching (PST-Matching) is inspired by similarities between ST-Matching and probabilistic approaches to map-matching based on a Hidden Markov Model. We validate the proposed algorithm on GPS trajectories of varied quality and show that it is similar to ST-Matching in terms of accuracy and computational efficiency, yet with the added benefit of having a measure of confidence associated with its outputs.
Bitte loggen Sie sich ein, um Zugang zu diesem Inhalt zu erhalten
Sie möchten Zugang zu diesem Inhalt erhalten? Dann informieren Sie sich jetzt über unsere Produkte:
Barber, D. (2012). Bayesian reasoning and machine learning. Cambridge: Cambridge University Press.
Bishop, C. M. (2006). Pattern recognition and machine learning. Berlin: Springer.
Brundson, C. (2007). Path estimation from GPS tracks. In Proceedings of the 9th International Conference on GeoComputation, National Centre for Geocomputation, Maynooth University.
Demšar, U., Buchin, K., Cagnacci, F., Safi, K., Speckmann, B., Van de Weghe, N., et al. (2015). Analysis and visualisation of movement: an interdisciplinary review. Movement Ecology, 3(1), 5.
Goh, C. Y., Dauwels, J., Mitrovic, N., Asif, M. T., Oran, A., & Jaillet, P. (2012). Online map-matching based on Hidden Markov model for real-time traffic sensing applications. In 2012 15th International IEEE Conference on Intelligent Transportation Systems (pp. 776–781). IEEE.
Gonzalez, H., Han, J., Li, X., Myslinska, M., & Sondag, J. P. (2007). Adaptive fastest path computation on a road network: a traffic mining approach. In Proceedings of the 33rd International Conference on Very Large Data Bases (pp. 794–805).
Jagadeesh, G. R., & Srikanthan, T. (2014). Robust real-time route inference from sparse vehicle position data. In 17th International IEEE Conference on Intelligent Transportation Systems (ITSC) (pp. 296–301). IEEE.
Kowalska, K., Shawe-Taylor, J., & Longley, P. (2015). Data-driven modelling of police route choice. In Proceedings of the 23rd GIS Research UK conference.
Kühne, R., Schäfer, R.-P., Mikat, J., & Lorkowski, S. (2003). New approaches for traffic management in metropolitan areas. In Proceedings of the 10th Symposium on Control in Transportation Systems, Tokyo.
Li, Q., Zeng, Z., Zhang, T., Li, J., & Wu, Z. (2011). Path-finding through flexible hierarchical road networks: An experiential approach using taxi trajectory data. International Journal of Applied Earth Observation and Geoinformation,13(1), 110–119. CrossRef
Liao, L., Patterson, D. J., Fox, D., & Kautz, H. (2006). Building personal maps from GPS data. Annals of the New York Academy of Sciences,1093, 249–265. CrossRef
Lou, Y., Zhang, C., Zheng, Y., Xie, X., Wang, W., & Huang, Y. (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). New York, USA: ACM Press.
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 (p. 336). New York, USA: ACM Press.
- Probabilistic Map-Matching for Low-Frequency GPS Trajectories
Neuer Inhalt/© ITandMEDIA, Product Lifecycle Management/© Eisenhans | vege | Fotolia