Map Matching Algorithm for Large-scale Datasets
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F22%3A00364465" target="_blank" >RIV/68407700:21230/22:00364465 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0010849100003116" target="_blank" >https://doi.org/10.5220/0010849100003116</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0010849100003116" target="_blank" >10.5220/0010849100003116</a>
Alternative languages
Result language
angličtina
Original language name
Map Matching Algorithm for Large-scale Datasets
Original language description
GPS receivers embedded in cell phones and connected vehicles generate series of location measurements that can be used for various analytical purposes. A common preprocessing step of this data is the so-called map matching. The goal of map matching is to infer the trajectory that the device followed in a road network from a potentially sparse series of noisy location measurements. Although accurate and robust map matching algorithms based on probabilistic models exist, they are computationally heavy and thus impractical for processing large datasets. In this paper, we present a scalable map matching algorithm based on Dijkstra's shortest path method, that is both accurate and applicable to large datasets. Our experiments on a publicly available dataset showed that the proposed method achieves accuracy that is comparable to that of the existing map matching methods using only a fraction of computational resources. As a result, our algorithm can be used to efficiently process large datasets of noisy and potentially sparse location data that would be unexploitable using existing techniques due to their high computational requirements.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICAART: PROCEEDINGS OF THE 14TH INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE - VOL 3
ISBN
978-989-758-547-0
ISSN
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e-ISSN
2184-433X
Number of pages
9
Pages from-to
500-508
Publisher name
SciTePress - Science and Technology Publications
Place of publication
Porto
Event location
Online Streaming
Event date
Mar 3, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
000774776400060