Parallel Map Matching on Massive Vehicle GPS Data Using MapReduce

Abstract

The procedure of matching vehicle location data onto road map is very essential for many ITS (Intelligent Transportation System) applications. However, with the boosting deployment of GPS devices in vehicles, the accumulation of huge amount of GPS data caused great challenge on the efficiency and scalability of traditional serial map matching algorithm. In this paper we address the challenge by presenting a novel parallel map matching algorithm to realize high-performance processing of GPS data. The main idea is to adapt the serial map matching algorithm for cloud computing environment by reforming its' data-intensive or I/O-intensive computing stages using MapReduce paradigm. We implemented the algorithm in Hadoop platform and tested its performance by a large GPS dataset exceeds 120 billion GPS records. Experimental results show that our approach is highly efficient and scalable for massive historical GPS data processing.

DOI: 10.1109/HPCC.and.EUC.2013.211

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Cite this paper

@article{Huang2013ParallelMM, title={Parallel Map Matching on Massive Vehicle GPS Data Using MapReduce}, author={Jian Huang and Shaoqing Qiao and Haitao Yu and Jinhui Qie and Chunwei Liu}, journal={2013 IEEE 10th International Conference on High Performance Computing and Communications & 2013 IEEE International Conference on Embedded and Ubiquitous Computing}, year={2013}, pages={1498-1503} }