Machine Learning
A Survey of Longest Common Subsequence Algorithms
SPIRE '00 Proceedings of the Seventh International Symposium on String Processing Information Retrieval (SPIRE'00)
Discovering Similar Multidimensional Trajectories
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Context in problem solving: a survey
The Knowledge Engineering Review
Learning and inferring transportation routines
Artificial Intelligence
Navigate like a cabbie: probabilistic reasoning from observed context-aware behavior
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Context-aware recommender systems
Proceedings of the 2008 ACM conference on Recommender systems
A method for predicting future location of mobile user for location-based services system
Computers and Industrial Engineering
Experimental comparison of pre- vs. post-filtering approaches in context-aware recommender systems
Proceedings of the third ACM conference on Recommender systems
Beyond accuracy, f-score and ROC: a family of discriminant measures for performance evaluation
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
Discovering temporal hidden contexts in web sessions for user trail prediction
Proceedings of the 22nd international conference on World Wide Web companion
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Personal route recognition is an important element of intelligent transportation systems. The results may be used for providing personal information about location-specific events, services, emergency or disaster situations, for location-specific advertising and more. Existing real-time route recognition systems often compare the current driving trajectory against the trajectories observed in past and select the most similar route as the most likely. The problem is that such systems are inaccurate in the beginning of a trip, as typically several different routes start at the same departure point (e.g. home). In such situations the beginnings of trajectories overlap and the trajectory alone is insufficient to recognize the route. This drawback limits the utilization of route prediction systems, since accurate predictions are needed as early as possible, not at the end of the trip. To solve this problem we incorporate external contextual information (e.g. time of the day) into route recognition from trajectory. We develop a technique to determine from the historical data how the probability of a route depends on contextual features and adjust (post-correct) the route recognition output accordingly. We evaluate the proposed context-aware route recognition approach using the data on driving behavior of twenty persons residing in Aalborg, Denmark, monitored over two months. The results confirm that utilizing contextual information in the proposed way improves the accuracy of route recognition, especially in cases when the historical routes highly overlap.