Efficient Data Mining for Path Traversal Patterns
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Real-Time Mobility Tracking Algorithms for Cellular Networks Based on Kalman Filtering
IEEE Transactions on Mobile Computing
Handbook of Algorithms for Wireless Networking and Mobile Computing (Chapman & Hall/Crc Computer & Information Science)
Algorithms and Protocols for Wireless Sensor Networks
Algorithms and Protocols for Wireless Sensor Networks
Traffic-Known Urban Vehicular Route Prediction Based on Partial Mobility Patterns
ICPADS '09 Proceedings of the 2009 15th International Conference on Parallel and Distributed Systems
Detecting faulty and malicious vehicles using rule-based communications data mining
LCN '11 Proceedings of the 2011 IEEE 36th Conference on Local Computer Networks
A tutorial survey on vehicular ad hoc networks
IEEE Communications Magazine
Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks
IEEE Journal on Selected Areas in Communications
Advances on Network Protocols and Algorithms for Vehicular Ad Hoc Networks
Mobile Networks and Applications
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Behavioral patterns prediction in the context of Vehicular Ad hoc Networks (VANETs) has been receiving increasing attention due to the enabling of on-demand, intelligent traffic analysis and real-time responses to traffic issues. One of these patterns, sequential patterns, is a type of behavioral pattern that describes the occurrence of events in a timely and ordered fashion. In the context of VANETs, these events are defined as an ordered list of road segments traversed by vehicles during their trips from a starting point to their final intended destination. In this paper, a new set of formal definitions depicting vehicular paths as sequential patterns is described. Also, five novel communication schemes have been designed and implemented under a simulated environment to collect vehicular paths; such schemes are classified under two categories: RSU (Road Side Unit)-based and Vehicle-based. After collection, extracted frequent paths are obtained through data mining, and the probability of these frequent paths is measured. In order to evaluate the effectiveness and efficiency of the proposed schemes, extensive experimental analysis has been realized.