Horizontally scalable probabilistic generalized suffix tree (PGST) based route prediction using map data and GPS traces

Route prediction play a vital role in many important location-based applications like resource prediction in grid computing, traffic congestion estimation, vehicular ad-hoc networks, travel recommendation etc. The goal of this work is to design scalable route prediction application based on Context Tree Weighting (CTW) modeling of user travel data. CTW is one of the widely used technique for text compression as well string sequence indexing and for prediction. CTW tree construction from the huge volume of data by sequential processing is time-consuming in practical implementation. Existing techniques are designed for single machine and their implementation on the distributed environment is still a challenge. This work focuses on achieving horizontal scalability of CTW and addresses various challenges in distributed construction like reducing I/O, parallel computation of sequences and coming up with final CTW tree in a distributed environment efficiently. Map Reduce framework running over Hadoop file system is used for processing in distributed mode. Large GPS data set is map-matched to digitized road network obtained from Open Street Map and CTW model is built. A two-step construction of CTW tree is proposed which is implemented in the map-reduce framework. Horizontally scalable CTW model is built and evaluated for route prediction from a huge corpus of historical GPS traces.

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Horizontally scalable probabilistic generalized suffix tree (PGST) based route prediction using map data and GPS traces

Cited by 3 publications

References 29 publications

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Manufacturing process optimization for real-time quality control in multi-regime conditions: Tire tread production use case

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