BTS-Z: A Bootstrap Zero-Shot Learning Approach for City Traffic Forecasting

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2024

Autores

Kumar, R (Autor) Outra; Bhanu, M (Autor) Outra; Roy, S (Autor) Outra; João Mendes-Moreira (Autor) FEUP; Chandra, J (Autor) Outra

Ata de Conferência Internacional

Título: International Symposium on Advanced Networks and Telecommunication Systems, ANTS

Páginas: 1-6

18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

Guwahati, 15 December 2024 through 18 December 2024

Indexação

Scopus - 0 Citações

Outras Informações

ID Authenticus: P-018-E57

DOI: 10.1109/ants63515.2024.10898578

Abstract (EN): Taxi demand prediction with scarce historic information is among the most encountered challenges of the present decade for the traffic network of a smart city. Lack of sufficient information results in the failure of conventional approaches in prediction for a new city. Additionally, the prevalent Deep Neural Network (DNN) Models resort to ineffectual approaches which fail to meet the required prediction performance for the network. Moreover, existing domain adaptation (DA) models could not sufficiently reap the domain-shared features well from multiple source, questioning the models' applicability. Complex structure of these DA models tends to a nominal performance gain due to inefficient resource utilization of the sources. The present paper introduces a domain adaptation deep neural network model, Bootstrap Zero-Shot (BTS-Z) learning model which focuses on capturing the latent spatio-temporal features of the whole city traffic network shared among every source city and maneuver them to predict for the target city traffic network with no prior information. The presented model proves the efficacy of the bootstrap algorithm in the prediction of demands for the unseen target over the computationally expensive MAML models. The experimental results on three real-world city taxi data on the standard benchmark metrics report a minimum of 23.41% improvement over the best performing competitive system. © 2024 IEEE.

Idioma: Inglês

Tipo (Avaliação Docente): Científica