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

Abstract Route prediction is an essential requirement for many intelligent transport systems (ITS) services like VANETS, traffic congestion estimation, resource prediction in grid computing etc. This work focuses on building an end-to-end horizontally scalable route prediction application based on statistical modeling of user travel data. Probabilistic suffix tree (PST) is one of widely used sequence indexing technique which serves a model for prediction. The probabilistic generalized suffix tree (PGST) is a variant of PST and is essentially a suffix tree built from a huge number of smaller sequences. We construct generalized suffix tree model from a large number of trips completed by the users. User trip raw GPS traces is mapped to the digitized road network by parallelizing map matching technique leveraging map reduce framework. PGST construction from the huge volume of data by processing sequentially is a bottleneck in the practical realization. Most of the existing works focused on time-space tradeoffs on a single machine. Proposed technique solves this problem by a two-step process which is intuitive to execute in the map-reduce framework. In the first step, computes all the suffixes along with their frequency of occurrences and in the second step, builds probabilistic generalized suffix tree. The probabilistic aspect of the tree is also taken care so that it can be used as a model for prediction application. Dataset used are road network spatial data and GPS traces of users. Experiments carried out on real datasets available in public domain