Abstract
Route prediction plays a vital role in many important location-based applications such as resource prediction in grid computing, traffic congestion estimation, vehicular ad hoc networks, and travel recommendation. The goal of this work is to design a scalable route prediction application based on prediction by partial match (PPM) modeling of user travel data. PPM is one of the widely used techniques for text compression as well as string sequence indexing and for prediction. PPM 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 a horizontal scalability of PPM and addresses various challenges in distributed construction, such as reducing I/O and parallel computation of sequences, and comes up with a final PPM tree in distributed environment without sacrificing accuracy. A huge corpus of GPS data set is map matched to the road network extracted from the OpenStreetMap and the PPM tree is built on the edges of the road network. A two-step construction of the PPM tree is proposed, which is extended to execute over the MapReduce framework. The MapReduce framework running over the Hadoop distributed file system is used for distributed processing. A horizontally scalable PPM model is built and evaluated for route prediction from a huge corpus of historical GPS traces. Data sets used are GPS traces and road networks. Both of these used in this work are taken from an openly available corpus. Distributed construction of PPM was proposed and evaluated on Hadoop cluster using MapReduce and the detailed results are presented.
Highlights
Route prediction is a key requirement in many location-based important applications such as vehicular ad hoc networks, traffic congestion estimation, resource prediction in grid computing, vehicular turn prediction, travel pattern similarity, and pattern mining
In this work, the focus was on the construction of the prediction by partial match (PPM) model in a distributed way from a huge corpus of GPS location traces
This model was used for building a route prediction application
Summary
Route prediction is a key requirement in many location-based important applications such as vehicular ad hoc networks, traffic congestion estimation, resource prediction in grid computing, vehicular turn prediction, travel pattern similarity, and pattern mining. PPM tree-based model is constructed from trips composed of an ordered sequence of road network edges. Scalability is achieved by decomposing GPS traces into trips and processing them in parallel and consolidating them to form the PPM model. PPM tree‐related work and literature Prediction by partial match (PPM) 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. PPM models use a set of historical occurrences of sequences to predict the probability of a specific symbol appearing at a given position in an input stream (Begleiter et al 2004). The resultant PPM tree is constructed by Algorithm 2 from the map of context strings in Table 3 including the frequency count. For remaining alphabets of fork a branch starting last node till where overlap was found in step I
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