@misc{10498/32566,
year = {2023},
url = {http://hdl.handle.net/10498/32566},
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.},
publisher = {IICM},
keywords = {Human mobility},
keywords = {Machine Learning},
keywords = {open data},
keywords = {road traffic},
keywords = {inductive loop sensor},
title = {Human Mobility Prediction with Region-based Flows and Road Traffic Data},
doi = {10.3897/JUCS.94514},
author = {Terroso-Saenz, Fernando and Muñoz Ortega, Andrés},
}