Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
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
A simple and effective method for predicting travel times on freeways
IEEE Transactions on Intelligent Transportation Systems
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
Improved travel time prediction algorithms for intelligent transportation systems
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part II
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Prediction of travel time on road network has emerged as a crucial research issue in intelligent transportation system (ITS). Travel time prediction provides information that may allow travelers to change their routes as well as departure time. To provide accurate travel time for travelers is the key challenge in this research area. In this paper, we formulate two new methods which are based on moving average can deal with this kind of challenge. In conventional moving average approach, data may lose at the beginning and end of a series. It may sometimes generate cycles or other movements that are not present in the original data. Our proposed modified method can strongly tackle those kinds of uneven presence of extreme values. We compare the proposed methods with the existing prediction methods like Switching method [10] and NBC method [11]. It is also revealed that proposed methods can reduce error significantly in compared with other existing methods.