Mining traffic data from probe-car system for travel time prediction
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Trajectory clustering: a partition-and-group framework
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
A New Travel Time Prediction Method for Intelligent Transportation Systems
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part I
Continuous Clustering of Moving Objects in Spatial Networks
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part II
A knowledge based real-time travel time prediction system for urban network
Expert Systems with Applications: An International Journal
Probabilistic Estimation of Travel Behaviors Using Zone Characteristics
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part II
Development of an Effective Travel Time Prediction Method Using Modified Moving Average Approach
KES '09 Proceedings of the 13th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems: Part I
New travel time prediction algorithms for intelligent transportation systems
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
uCFS2: an enhanced system that mines uncertain data for constrained frequent sets
Proceedings of the Fourteenth International Database Engineering & Applications Symposium
Modified K-means clustering for travel time prediction based on historical traffic data
KES'10 Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part I
Travel-time prediction with support vector regression
IEEE Transactions on Intelligent Transportation Systems
Travel Time Prediction Using Floating Car Data Applied to Logistics Planning
IEEE Transactions on Intelligent Transportation Systems
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Travel time prediction provides commuters with useful information that enables them to decide whether or not to make necessary changes to their routes or departure times. This explains why travel time prediction has become important to intelligent systems, especially intelligent transportation systems (ITS). Over the past few years, several algorithms have been developed to predict travel time, but some of them suffer from a few problems. In this paper, we propose algorithms that solve these problems and improve the performance and/or accuracy of travel time prediction for ITS.