Examinando por Autor "Mariñas Collado, Irene"

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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.
Time series forecasting methods in emergency contexts
(2023) Villoria Hernandez, Pablo; Mariñas Collado, Irene; García Sipols, Ana Elizabeth; Simón de Blas, Clara; Rodríguez Sánchez, María Cristina
The key issues in any fire emergency are recognising fire hotspots, locating the emergency intervention team (EI), following the evolution of the fire, and selecting the evacuation path. This leads to the study and development of HelpResponder, a solution capable of detecting the focus of interest in hostile spaces derived from fire due to high temperatures without visibility. A study is conducted to determine which model best predicts measured CO2 levels. The variables used are temperature, humidity, and air quality, obtained from sensors installed in a fire tower. The statistical methods applied, namely ARIMAX, KNN, SVM, and TBATS, allow the adjustment and modelling of the variables. Explanatory variables with temporal structure are incorporated into SVM, a new improvement proposal. Moreover, combining different models showed the best efficiency in forecasting. In fact, another contribution of our work lies in offering a small-scale prediction system that is specifically designed to save batteries. The system has been tested and validated in a hostile environment (building), simulating real emergency situations. The system has been tested and validated in several hostile environments, simulating real emergency situations. It can help firefighters respond faster in an emergency. This reduces the risks associated with the lack of information and improves the time for tactical operations, which could save lives.