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

Resultado de la investigación: Article

Resumen

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.

Idioma originalEnglish
Número de artículo8463456
Páginas (desde-hasta)87-97
Número de páginas11
PublicaciónIEEE Intelligent Transportation Systems Magazine
Volumen10
N.º4
DOI
EstadoPublished - 24 sep 2018

Huella dactilar

Fuzzy logic
Tuning
Processing
Calibration
Global positioning system

ASJC Scopus subject areas

  • Automotive Engineering
  • Mechanical Engineering
  • Computer Science Applications

Citar esto

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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.",
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