Abstract
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.
Highlights
Route prediction is a key requirement in many location-based important applications like vehicular ad-hoc networks, traffic congestion estimation, resource prediction in grid computing, vehicular turn prediction, travel pattern similarity, pattern mining etc
Map data and GPS location traces are two data sets required for implementation of proposed Context Tree Weighting (CTW) based route prediction
Digitized road network data is used for converting user location GPS traces into an ordered set of edges
Summary
Route prediction is a key requirement in many location-based important applications like vehicular ad-hoc networks, traffic congestion estimation, resource prediction in grid computing, vehicular turn prediction, travel pattern similarity, pattern mining etc. Context tree weighting (CTW) is widely used in various applications in the area of data compression and machine learning [1]. Context Tree Weighting (CTW) is a context modeling based adaptive statistical data compression technique. It has evolved as a better alternative for solving many problems in the field of biomedical engineering, natural language processing and artificial intelligence. Tjalkens et al [10] proposed an encoding for CTW-method which was binary It proposed to store the probabilities in the node of the CTW tree and lead to a reduction in storage space requirement. Volf [13] presented a variant of CTW which used a hierarchical tree based decomposition and applied for prediction over binary symbols. The idea of this work is to come up with a technique for distributed computation of CTW and its application in route prediction
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