An Approach for Map-Matching Strategy of GPS-Trajectories Based on the Locality of Road Networks

  • Aftab Ahmed Chandio
  • Nikos Tziritas
  • Fan Zhang
  • Cheng-Zhong Xu
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9502)

Abstract

A map-matching process plays a pivotal role in ascertaining the quality of many location based services (LBS). Map-matching process is to determine the accurate path of a vehicle onto road network in a form of digital map. Most of the current map-matching strategies are based on the shortest path queries (SPQs) providing best performance in terms of accuracy. Unfortunately, the execution of the SPQs is the most expensive part of the map-matching process in terms of computational cost, which may be unaffordable for real-time processing. This paper introduces LB-MM (i.e., Locality Based Map-Matching), a novel approach for map-matching strategy that is based on locality of road network. LB-MM approach addresses a key challenge of SPQs in map-matching strategies by adaptively tuning the interior parameters of the map-matching process. The interior parameters, i.e., a number of candidate points (CP) and error circle radius (ECR) are fine-tuned based on different classes of locality of road network for each GPS sampling point. We characterize the locality of road network in different classes which result by splitting road network into small grids. In that way, a set of interior parameters is chosen based on locality that drastically reduces a number of SPQs and the overall computation time of map-matching process. The evaluation of proposed strategy against the SPQ-based ST-MM (i.e., Spatio-Tempo Map-Matching) strategy found in the literature is performed through simulation results based on both synthetic and real-world datasets. In LB-MM strategy, the total number of SPQs is counted as less than 27 % against those of ST-MM.

Keywords

Location based services Map-matching GPS data Spatiotemporal data mining GIS Locality of road network 
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Notes

Acknowledgments

The work is funded in part by the grant of the National Natural Science Foundation of China (No. 61100220 and U1401258), U.S. NSF (CCF-1016966) and National Basic Research Program (973 Program, No. 2015CB352400). AA. Chandioś work was partly supported for his PhD studies in Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Aftab Ahmed Chandio
    • 1
    • 2
    • 3
  • Nikos Tziritas
    • 1
  • Fan Zhang
    • 1
  • Cheng-Zhong Xu
    • 1
    • 4
  1. 1.Shenzhen Institutes of Advanced TechnologyChinese Academy of SciencesShenzhenChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Institute of Mathematics and Computer ScienceUniversity of SindhJamshoroPakistan
  4. 4.Department of Electrical and Computer EngineeringWayne State UniversityDetroitUSA

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