Joint Feature Selection and Parameter Tuning for Short-term Traffic Flow Forecasting based on Heuristically Optimized Multi-layer Neural Networks
Short-term traffic flow forecasting is a vibrant research topic that has been growing in interest since the late 70’s. In the last decade this vibrant field has shifted its focus towards machine learning methods. These techniques often require fine-grained parameter tuning to obtain satisfactory per...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión aceptada para publicación |
| Fecha de publicación: | 2017 |
| País: | España |
| Institución: | Basque Center for Applied Mathematics (BCAM) |
| Repositorio: | BIRD. BCAM's Institutional Repository Data |
| OAI Identifier: | oai:bird.bcamath.org:20.500.11824/1217 |
| Acceso en línea: | http://hdl.handle.net/20.500.11824/1217 https://doi.org/10.1007/978-981-10-3728-3_10 |
| Access Level: | acceso abierto |
| Palabra clave: | Traffic forecasting Neural networks Bioinspired heuristics |
| Sumario: | Short-term traffic flow forecasting is a vibrant research topic that has been growing in interest since the late 70’s. In the last decade this vibrant field has shifted its focus towards machine learning methods. These techniques often require fine-grained parameter tuning to obtain satisfactory performance scores, a process that usually relies on man- ual trial-and-error adjustment. This paper explores the use of Harmony Search optimization for tuning the parameters of neural network jointly with the selection of the input features from the dataset at hand. Re- sults are discussed and compared to other tuning methods, from which it is concluded that neural predictors optimized via the proposed heuris- tic wrapper outperform those tuned by means of na ̈ıve parametrized algorithms, thus allowing for longer-term predictions. These promising results unfold potential applications of this technique in multi-location neighbor-aware traffic prediction. |
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