Extensions to the k-Means Algorithm for Clustering Large Data Sets with Categorical Values
Data Mining and Knowledge Discovery
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Indexing Multidimensional Time-Series
The VLDB Journal — The International Journal on Very Large Data Bases
Similarity-based prediction of travel times for vehicles traveling on known routes
Proceedings of the 16th ACM SIGSPATIAL international conference on Advances in geographic information systems
TransDB: GPS data management with applications in collective transport
Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Assessing the predictability of scheduled-vehicle travel times
Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Anticipatory DTW for efficient similarity search in time series databases
Proceedings of the VLDB Endowment
T-drive: driving directions based on taxi trajectories
Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Travel-time prediction with support vector regression
IEEE Transactions on Intelligent Transportation Systems
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In this paper, we develop a new bus travel time prediction framework, called Historical Trajectory based Travel/Arrival Time Prediction (HTTP) for real-time prediction of travel time over future segments (and thus the arrival time at stops) of an on-going bus journey. The basic idea behind HTTP is to use a collection of historical trajectories "similar" to the current bus trajectory to predict the future segments. Specifically, the HTTP framework (1) samples a set of similar trajectories as the basis for travel time estimation instead of relying on only one historical trajectory best matching the on-going bus journey; and (2) explores different prediction schemes, namely, passed segments, temporal features, and hybrid methods, to identify the sample set of similar trajectories. We conduct a comprehensive empirical experimentation using real bus trajectory data collected from Taipei City, Taiwan to validate our ideas and to evaluate the proposed schemes. Experimental result shows that the proposed prediction schemes significantly outperforms the state-of-the-art and baseline techniques.