Evolving map matching with markov decision processes

  • Map matching is about finding the best route of a given track in a road network. This can be useful for many statistical analyses on mobility. With increasing spread of modern cars and mobile devices many tracks are available to match. The difficulty in map matching lies in the geospatial differences between tracks and road networks. Current technologies resolve such differences with Hidden Markov Models and Viterbi algorithm. They majorly vary concerning the used metrics for the probabilities in the models. In this research we improve map matching technology by refining the underlying algorithms, models and metrics. We will introduce Markov Decision Processes with Value Iteration and Q-Learning to the map matching domain and we will compare them to Hidden Markov Models with Viterbi algorithm. Markov Decision Processes allow to use active decisions and rewards, which are not available in previous methods. Combined with improvements concerning the preparation of tracks and the road network, and various technologies for improvedMap matching is about finding the best route of a given track in a road network. This can be useful for many statistical analyses on mobility. With increasing spread of modern cars and mobile devices many tracks are available to match. The difficulty in map matching lies in the geospatial differences between tracks and road networks. Current technologies resolve such differences with Hidden Markov Models and Viterbi algorithm. They majorly vary concerning the used metrics for the probabilities in the models. In this research we improve map matching technology by refining the underlying algorithms, models and metrics. We will introduce Markov Decision Processes with Value Iteration and Q-Learning to the map matching domain and we will compare them to Hidden Markov Models with Viterbi algorithm. Markov Decision Processes allow to use active decisions and rewards, which are not available in previous methods. Combined with improvements concerning the preparation of tracks and the road network, and various technologies for improved processing speed, we will show on a publicly available map matching data set that our approach has a higher overall performance compared to previous map matching technology. We will eventually discuss more possibilities we enable with our approach.show moreshow less

Download full text files

  • paper.pdfeng
    (311KB)

    Evolving map matching with markov decision processes

Export metadata

Metadaten
Author:Adrian Wöltche
URN:urn:nbn:de:bvb:1051-opus4-1193
Document Type:Article
Language:English
Date of Publication (online):2021/10/12
Date of first Publication:2021/10/12
Publishing Institution:Hochschule für Angewandte Wissenschaften Hof
Release Date:2021/10/13
Tag:geospatial information science; hidden markov model; map matching; markov decision process
Page Number:21
Institutes:Institut für Informationssysteme (iisys)
Licence (German):License LogoCreative Commons - CC BY-SA - Namensnennung - Weitergabe unter gleichen Bedingungen 4.0 International
Verstanden ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.