Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches

Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words’ feature vectors using their sequential order in the text via word embe...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 22; no. 19; p. 7475
Main Authors:	Cruz, Lívia Almada, Coelho da Silva, Ticiana Linhares, Magalhães, Régis Pires, Melo, Wilken Charles Dantas, Cordeiro, Matheus, de Macedo, José Antonio Fernandes, Zeitouni, Karine
Format:	Journal Article
Language:	English
Published:	Basel MDPI AG 01-10-2022 MDPI
Subjects:	Analysis Cameras Computational linguistics Criminal investigations Engineering Sciences Language Language processing License plates location prediction Machine learning Natural language Natural language interfaces Natural language processing representation learning Representations Semantics Sensors sensors trajectory Signal and Image processing Surveillance Traffic congestion trajectory embedding trajectory modeling trajectory prediction Vehicles Brazil trajectory prediction sensors trajectory trajectory embedding location prediction representation learning trajectory modeling
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Summary:	Representation learning seeks to extract useful and low-dimensional attributes from complex and high-dimensional data. Natural language processing (NLP) was used to investigate the representation learning models to extract words’ feature vectors using their sequential order in the text via word embeddings and language models that maintain their semantic meaning. Inspired by NLP, in this paper, we tackle the representation learning problem for trajectories, using NLP methods to encode external sensors positioned in the road network and generate the features’ space to predict the next vehicle movement. We evaluate the vector representations of on-road sensors and trajectories using extrinsic and intrinsic strategies. Our results have shown the potential of natural language models to describe the space of features on trajectory applications as the next location prediction.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s22197475