Developing Map Matching Algorithm for Transportation Data Center

Map matching (MM), pins the drifting position data to the correct road link on which a vehicle is travelling, is a crucial step needed by many industrial or research ITS projects which rely on post-hoc analysis of trajectories. To address the unprecedented challenge of massive GPS data processing in...

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Bibliographic Details
Published in2014 Ninth International Conference on P2P, Parallel, Grid, Cloud and Internet Computing pp. 167 - 170
Main Authors Jian Huang, Chunwei Liu, Jinhui Qie
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.11.2014
Subjects
Accuracy
Algorithm design and analysis
Global Positioning System
GPS data
Hadoop
high-performance processing
map matching
MapReduce
Roads
Trajectory
Vehicles
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Summary:Map matching (MM), pins the drifting position data to the correct road link on which a vehicle is travelling, is a crucial step needed by many industrial or research ITS projects which rely on post-hoc analysis of trajectories. To address the unprecedented challenge of massive GPS data processing in urban transportation data center nowadays, this paper proposed an improved parallel topological map-matching algorithm that aims to achieve highest efficiency as well as guaranteed accuracy. The main contributions of this work include: I) a weighting scheme based on cost-effectiveness ratio to reduce candidate path set in low time cost, II) a novel leapfrog method to omit the redundant GPS points that are not needed in path determination, III) parallelized processing using Map Reduce paradigm. Experiment show that these improvements greatly reduced algorithm's running time when compare to the state of the art.
DOI:10.1109/3PGCIC.2014.52