Dalia Tiesyte
Christian S. Jensen
ABSTRACT
The use of centralized, real-time position tracking is proliferating in the areas of logistics and public transportation. Real-time positions can be used to provide up-to-date information to a variety of users, and they can also be accumulated for uses in subsequent data analyses. In particular, historical data in combination with real-time data may be used to predict the future travel times of vehicles more accurately, thus improving the experience of the users who rely on such information. We propose a Nearest-Neighbor Trajectory (NNT) technique that identifies the historical trajectory that is the most similar to the current, partial trajectory of a vehicle. The historical trajectory is then used for predicting the future movement of the vehicle. The paper's specific contributions are two-fold. First, we define distance measures and a notion of nearest neighbor that are specific to trajectories of vehicles that travel along known routes. In empirical studies with real data from buses, we evaluate how well the proposed distance functions are capable of predicting future vehicle movements. Second, we propose a main-memory index structure that enables incremental similarity search and that is capable of supporting varying-length nearest neighbor queries.
- P. Bakalov, E. Keogh, and V. Tsotras. TS2-tree---an efficient similarity based organization for trajectory data. Proc. of the Annual ACM International Symposium on Advances in Geographic Information Systems, pp. 1--4, 2007. Google Scholar
Digital Library
- Y. Bin, Y. Zhongzhen, and Y. Baozhen. Bus arrival time prediction using support vector machines. Journal of Int. Transp. Systems: Technology, Planning, and Operations, 10(4): 151--158, 2006.Google Scholar
- B. Bollobas, G. Das, D. Gunopulos, and H. Mannila. Time-series similarity problems and well-separated geometric sets. In Proc. of the Annual Symposium on Computational Geometry, pp. 454--456, 1997. Google ScholarDigital Library
- F. Cathey and D. Dailey. A prescription for transit arrival/departure prediction using automatic vehicle location data. Transp. Res. Part C, 11(3--4): 241--264, 2003.Google Scholar
- L. Chen and R. Ng. On the marriage of Lp-norms and edit distance. In Proc. of the International Conference on Very Large Data Bases, pp. 792--803, 2004. Google ScholarDigital Library
- L. Chen, M. T. Özsu, and V. Oria. Robust and fast similarity search for moving object trajectories. In Proc. of the ACM SIGMOD International Conference on Management of Data, pp. 491--502, 2005. Google ScholarDigital Library
- S. I.-J. Chien, Y. Ding, and C. Wei. Dynamic bus arrival time prediction with artificial neural networks. Trans. Engrg., 128: 429--438, 2002.Google ScholarCross Ref
- E.-H. Chung and A. Shalaby. Expected time of arrival model for school bus transit using real-time global positioning system-based automatic vehicle location data. Journal of Int. Transp. Systems, 11: 157--167, 2007.Google ScholarCross Ref
- D. Dailey, S. Maclean, F. Cathey, and Z. Wall. Transit vehicle arrival prediction: An algorithm and a large scale implementation. Transp. Res. Rec., Transportation Network Modeling, pp. 46--51, 2001.Google Scholar
- R. Fagin, A. Lotem, and M. Naor. Optimal aggregation algorithms for middleware. Journal of Comp. and Syst. Sciences, 66(4): 614--656, 2003. Google ScholarDigital Library
- J. R. Hee and L. R. Rilett. Bus arrival time prediction using artificial neural network model. In Proc. of the International IEEE Conference on Int. Transp. Systems, pp. 988--993, 2004.Google Scholar
- J. Hwang, H. Kang, and K. Li. Spatio-temporal similarity analysis between trajectories on road networks. In Proc. of the International Workshop on Conceptual Modeling for Geographic Information Systems, pp. 280--289, 2005. Google ScholarDigital Library
- J. Hwang, H. Kang, and K. Li. Searching for similar trajectories on road networks using spatio-temporal similarity. In Proc. of the East-European Conference on Advances in Databases and Information Systems, pp. 282--295, 2006. Google ScholarDigital Library
- R. H. Jeong. The Prediction of Bus Arrival Time Using Automatic Vehicle Location Systems Data. Doctoral dissertation, Texas A&M University, 2004.Google Scholar
- R. Kalaputapu and M. J. Demetsky. Modeling schedule deviations of buses using automatic vehicle location data and artificial neural networks. Transp. Res. Rec., 1497: 44--52, 1995.Google Scholar
- M. G. Kendall. Rank Correlation Methods. Charles Griffin & Co. Ltd., 166 p, 1962.Google Scholar
- W.-H. Lin and J. Zeng. An experimental study on real time bus arrival time prediction with GPS data. Journal of Transportation Research Board, pages 101--109, 1999.Google ScholarCross Ref
- T. Park, S. Lee, and Y.-J. Moon. Real time estimation of bus arrival time under mobile environment. In Proc. of International Conference on Computational Science and its Applications, pp. 1088--1096, 2004.Google ScholarCross Ref
- N. Pelekis, I. Kopanakis, G. Marketos, I. Ntoutsi, G. Andrienko, and Y. Theodoridis. Similarity Search in Trajectory Databases. In Proc. of the International Symposium on Temporal Representation and Reasoning, pp. 129--140, 2007. Google ScholarDigital Library
- B. Predic, D. Stojanovic, S. Djordjevic-Kajan, A. Milosavljevic, and D. Rancic. Prediction of bus motion and continuous query processing for traveler information services. In Proc of the East European Conference on Advances in Databases and Information Systems, pp. 234--249, 2007. Google ScholarDigital Library
- H. Sakoe and S. Chiba. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoustics, Speech and Signal Processing, 26(1):43--49, 1978.Google ScholarCross Ref
- A. Shalaby and A. Farhan. Prediction model of bus arrival and departure times using AVL and APC data. Journal of Pub. Transp., 7: 41--61, 2004.Google ScholarCross Ref
- D. Tiesyte and C. S. Jensen. Recovery of vehicle trajectories from tracking data for analysis purposes. In Proc. of the European Congress and Exhibition on Intelligent Transport Systems and Services, pp. 1--12, 2007.Google Scholar
- M. Vlachos, G. Kollios, and D. Gunopulos. Discovering similar multidimensional trajectories. In Proc. of the International Conference on Data Engineering, pp. 673--684, 2002. Google ScholarDigital Library
Index Terms
- Similarity-based prediction of travel times for vehicles traveling on known routes
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