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- W4367058016 abstract "Predicting human mobility is a key element in the development of intelligent transport systems. Current digital technologies enable capturing a wealth of data on mobility flows between geographic areas, which are then used to train machine learning models to predict these flows. However, most works have only considered a single data source for building these models or different sources but covering the same spatial area. In this paper we propose to augment a macro open-data mobility study based on cellular phones with data from a road traffic sensor located within a specific motorway of one of the mobility areas in the study. The results show that models trained with the fusion of both types of data, especially long short-term memory (LSTM) and Gated Recurrent Unit (GRU) neural networks, provide a more reliable prediction than models based only on the open data source. These results show that it is possible to predict the traffic entering a particular city in the next 30 minutes with an absolute error less than 10%. Thus, this work is a further step towards improving the prediction of human mobility in interurban areas by fusing open data with data from IoT systems." @default.
- W4367058016 created "2023-04-27" @default.
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- W4367058016 date "2023-04-28" @default.
- W4367058016 modified "2023-10-14" @default.
- W4367058016 title "Human Mobility Prediction with Region-based Flows and Road Traffic Data" @default.
- W4367058016 doi "https://doi.org/10.3897/jucs.94514" @default.
- W4367058016 hasPublicationYear "2023" @default.
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