Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
The current paper concentrates on the examination of extensive datasets derived from public transportation networks, specifically addressing the prediction of urban bus passenger demand. The approach involves a series of steps designed to enhance the comprehension of passenger demand. Initially, due...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Rey Juan Carlos |
| Repositorio: | BURJC-Digital. Repositorio Institucional de la Universidad Rey Juan Carlos |
| OAI Identifier: | oai:burjcdigital.urjc.es:10115/28340 |
| Acceso en línea: | https://hdl.handle.net/10115/28340 |
| Access Level: | acceso abierto |
| Palabra clave: | forecasting time series models Big Data Clustering Cointegration Combination |
| Sumario: | The current paper concentrates on the examination of extensive datasets derived from public transportation networks, specifically addressing the prediction of urban bus passenger demand. The approach involves a series of steps designed to enhance the comprehension of passenger demand. Initially, due to the substantial number of bus stops in the network, they are categorized into clusters, and distinct models are subsequently developed for a representative from each cluster. The objective is to compare and integrate predictions generated by conventional methods like exponential smoothing or ARIMA with those from machine learning techniques, such as support vector machines or artificial neural networks. Furthermore, the accuracy of support vector machine predictions is refined by incorporating explanatory variables with temporal structures and moving averages. Ultimately, through cointegration techniques, the outcomes obtained for the representative of each group are extrapolated to the remaining series within the same cluster. The paper illustrates the application of these methods through a case study conducted in the city of Salamanca, Spain. |
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