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Hidden Markov Model for Floating Car Trajectory Map Matching

  • Chengbo Song
  • Xuefeng Yan
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 474)

Abstract

Map matching is the key technology in the data processing of floating car trajectory data. In order to improve the matching accuracy, this paper adopted widely followed Hidden Markov Model (HMM) approach and proposed new probabilistic models for the transition probability. The new model considers distance difference feature and average speed difference feature, which was proved to be more reasonable and accurate to describe the context relationship between adjacent candidate points by experiments. The experiments showed that our proposed algorithm can achieve a better matching accuracy compared with a comparable HMM-based method from the literature.

Keywords

Floating car Map matching Hidden Markov Model 

Notes

Acknowledgments

This work is supported by the 13th Five-Year equipment pre-research project (41401010201) and the 13th Five-Year key basic research project (JCKY2016206B001).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina
  2. 2.Collaborative Innovation Center of Novel Software Technology and Industrialization, College of Computer Science and TechnologyNanjing University of Aeronautics and AstronauticsNanjingChina

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