| Revista: | Computación y sistemas |
| Base de datos: | |
| Número de sistema: | 000607884 |
| ISSN: | 1405-5546 |
| Autores: | Molefe, Mohale E.1 Tapamo, Jules R.1 |
| Instituciones: | 1Inyuvesi YaKwaZulu-Natali, School of Engineering, Sudáfrica |
| Año: | 2024 |
| Periodo: | Ene-Mar |
| Volumen: | 28 |
| Número: | 1 |
| Paginación: | 257-270 |
| País: | México |
| Idioma: | Inglés |
| Resumen en inglés | Machine learning-based road-type classification is pivotal in intelligent road network systems, where accurate network modelling is crucial. Graph embedding methods have emerged as the leading paradigm for capturing the intricate relationships within road networks. However, their effectiveness hinges on the quality of input features. This paper introduces a novel two-stage graph embedding approach used to classify road-type. The first stage employs Deep Autoencoders to produce compact representation of road segments. This compactified representation is then used, in the second stage, by graph embedding methods to generate an embedded vectors, leveraging the features of neighbouring segments. Results achieved, with experiments on realistic city road network datasets, show that the proposed method outperforms existing approaches with respect to classification accuracy. |
| Keyword: | Road type classification, Road networks intelligent systems, Graph embedding methods, Deep autoencoder |
| Texto completo: | Texto completo (Ver PDF) Texto completo (Ver HTML) |
