Guardado en:
| Autor principal: | |
|---|---|
| Formato: | Recurso digital |
| Lenguaje: | inglés |
| Publicado: |
Zenodo
2023
|
| Acceso en línea: | https://doi.org/10.5281/zenodo.8156132 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866902146080833536 |
|---|---|
| author | Waleed Ahmed |
| author_facet | Waleed Ahmed |
| contents | <p>As urban traffic congestion continues to pose significant challenges worldwide, accurate traffic prediction models are crucial for effective traffic management and efficient transportation systems. This thesis presents a comprehensive study on traffic prediction using SKTIME models, focusing on two prominent datasets: METR-LA and PEMS-BAY.</p> <p><br> The primary objective of this research is to investigate the effectiveness of SKTIME models in predicting traffic patterns and compare their performance across the METR-LA and PEMS-BAY datasets. To achieve this, an in-depth exploration of the datasets was performed, encompassing data preprocessing, feature engineering, and model development. The METR-LA dataset contains real-time traffic data from loop detectors in the Los Angeles area, while the PEMS-BAY dataset encompasses traffic information from various sensors in the San Francisco Bay Area.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_8156132 |
| institution | Zenodo |
| language | eng |
| publishDate | 2023 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Traffic Prediction Using Time Series Models Waleed Ahmed <p>As urban traffic congestion continues to pose significant challenges worldwide, accurate traffic prediction models are crucial for effective traffic management and efficient transportation systems. This thesis presents a comprehensive study on traffic prediction using SKTIME models, focusing on two prominent datasets: METR-LA and PEMS-BAY.</p> <p><br> The primary objective of this research is to investigate the effectiveness of SKTIME models in predicting traffic patterns and compare their performance across the METR-LA and PEMS-BAY datasets. To achieve this, an in-depth exploration of the datasets was performed, encompassing data preprocessing, feature engineering, and model development. The METR-LA dataset contains real-time traffic data from loop detectors in the Los Angeles area, while the PEMS-BAY dataset encompasses traffic information from various sensors in the San Francisco Bay Area.</p> |
| title | Traffic Prediction Using Time Series Models |
| url | https://doi.org/10.5281/zenodo.8156132 |