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

Lívia Almada Cruz (1) , Ticiana Linhares Coelho Coelho da Silva (1) , Régis Pires Magalhães (1) , Wilken Charles Dantas Melo (1) , Matheus Gomes Cordeiro (1) , José António Fernandes D. de Macedo (1) , Karine Zeitouni (2)

1 UFC - Universidade Federal do Ceará = Federal University of Ceará
2 DAVID - Données et algorithmes pour une ville intelligente et durable - DAVID

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.

Mots clés: location prediction; representation learning; sensors trajectory; trajectory embedding; trajectory modeling; trajectory prediction

Domaines: Traitement du signal et de l'image [eess.SP]

Lívia Almada Cruz, Ticiana Linhares Coelho Coelho da Silva, Régis Pires Magalhães, Wilken Charles Dantas Melo, Matheus Gomes Cordeiro, et al.. Modeling Trajectories Obtained from External Sensors for Location Prediction via NLP Approaches. Sensors, 2022, 22 (19), pp.7475. ⟨10.3390/s22197475⟩. ⟨hal-03837174⟩