An Instance-Specific Parameter Tuning Approach Using Fuzzy Logic for a Post-Processing Topological Map-Matching Algorithm

Carola Blazquez, Jana Ries, Pablo Andres Miranda, Roberto Leon

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Map Matching Algorithms (MMAs) are developed to solve spatial ambiguities that arise in the process of assigning GPS measurements onto a digital roadway network. Scarce systematic parameter tuning approaches exist in the literature for optimizing MMA performance. Thus, a novel framework is proposed for a systematic calibration of the parameters of a post-processing MMA. The calibration approach consists of an Instance-specific Parameter Tuning Strategy (IPTS) that employs Fuzzy Logic principles. The proposed fuzzy IPTS tool determines algorithm-specific parameter values based on instance-specific information a priori to the execution of the MMA. Finally, the proposed IPTS tool is able to adjust to two particular decision maker preferences on algorithm performance, namely solution quality and computational time.

Original languageEnglish
Article number8463456
Pages (from-to)87-97
Number of pages11
JournalIEEE Intelligent Transportation Systems Magazine
Volume10
Issue number4
DOIs
Publication statusPublished - 24 Sep 2018

Keywords

  • Calibration
  • Electronic mail
  • Fuzzy logic
  • Global Positioning System
  • Roads
  • Tools
  • Tuning

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

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