Examinando por Autor "Frutos Bernal, Elisa"

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Clustering and Forecasting Urban Bus Passenger Demand with a Combination of Time Series Models
(MDPI, 2022) Mariñas Collado, Irene; Sipols, Ana E.; Santos Martín, M. Teresa; Frutos Bernal, Elisa
The present paper focuses on the analysis of large data sets from public transport networks, more specifically, on how to predict urban bus passenger demand. A series of steps are proposed to ease the understanding of passenger demand. First, given the large number of stops in the bus network, these are divided into clusters and then different models are fitted for a representative of each of the clusters. The aim is to compare and combine the predictions associated with traditional methods, such as exponential smoothing or ARIMA, with machine learning methods, such as support vector machines or artificial neural networks. Moreover, support vector machine predictions are improved by incorporating explanatory variables with temporal structure and moving averages. Finally, through cointegration techniques, the results obtained for the representative of each group are extrapolated to the rest of the series within the same cluster. A case study in the city of Salamanca (Spain) is presented to illustrate the problem.
Forecasting using dynamic factor models with cluster structure at Barcelona subway stations
(Taylor & Francis Online, 2022) Mariñas Collado, Irene; Sipols, Ana Elizabeth; Santos Martín, M. Teresa; Frutos Bernal, Elisa
Dynamic factor models are a powerful technique for analysing vast volumes of data, more precisely, time series. However, the large volumes of data that come from public transport networks tend to have heterogeneity and a cluster structure. In this paper, Dynamic Factor Models with Cluster Structure (DFMCS) are used to forecast hourly entrances in the different stations of the Barcelona subway network. The main and most novel contribution lies in the use of clustering techniques to make an initial grouping of the behaviour of the elements belonging to the time series, in order to subsequently be able to predict future patterns.