Vehicle traffic flow forecasting Costa Rica highway 27
Guardado en:
| Autores: | , |
|---|---|
| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2022 |
| Descripción: | Forecasting vehicle traffic flow is considered an important input for traffic planning and management for the countries’ intelligent transport systems (ITS). This article analyzes the hourly flow of light vehicle traffic that drives in highway 27 of Costa Rica in one direction (San Jose-Caldera). The data collected by the ITS of the route is used to forecast the behavior of hourly vehicular traffic. For this, three forecasting methods are proposed, which are compared to select the model with best performance: Seasonal Arima (SARIMA), Seasonal Naïve (SNAIVE), and Autoregression with Neural Network (NNAR). All three models are evaluated and are considered useful for prediction, however the NNAR model results in better performance when forecasting the hourly time series with the lowest MAPE of 9.4 and is consider a candidate for use in ITS. By applying the cross-validation process in the models, the conclusion is supported that as the NNAR is tested for more days, the prediction results are more stable and accurate. |
| País: | RepositorioTEC |
| Institución: | Instituto Tecnológico de Costa Rica |
| Repositorio: | RepositorioTEC |
| Lenguaje: | Español |
| OAI Identifier: | oai:repositoriotec.tec.ac.cr:2238/14118 |
| Acceso en línea: | https://revistas.tec.ac.cr/index.php/tec_marcha/article/view/5892 https://hdl.handle.net/2238/14118 |
| Access Level: | acceso abierto |
| Palabra clave: | Traffic flow forecasting Seasonal ARIMA(SARIAM) Seasonal Naïve (SNAIVE) Autogression with Neural Networks (NNAR) Predicción de flujo de tráfico ARIMA Estacional (SARIMA) Ingenuo Estacional (SNAIVE) Autoregresión con Redes Neuronales (NNAR) |