Pregled bibliografske jedinice broj: 1238779
Spatial Machine Learning Personal Mobility Predictive Model Trained with Smartphone-Collected Trajectory Data
Spatial Machine Learning Personal Mobility Predictive Model Trained with Smartphone-Collected Trajectory Data // The Journal of CIEES, 2 (2022), 2; 7-12 doi:10.48149/jciees.2022.2.2.1 (međunarodna recenzija, članak, znanstveni)
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Naslov
Spatial Machine Learning Personal Mobility
Predictive Model Trained with Smartphone-Collected
Trajectory Data
Autori
Stoyanovski, Boyan ; Iliev, Teodor B ; Cesarec, Radovan ; Filjar, Renato
Izvornik
The Journal of CIEES (2738-7283) 2
(2022), 2;
7-12
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
GNSS, trajectory, kinematics, machine learning, predictive model, mobility
Sažetak
Individual and group mobility is an essential information for numerous segments of technology (including transport and logistics), society, and economy. The ability of telecommunications devices, such as smartphones, to collect accurate and reliable data on personal mobility with the embedded sensors, inspires research in personal mobility. We confirm the ability of suitably defined indicators to compare sets of trajectories, and identify outliers/differences among the individual ones. Furthermore, we demonstrate development of a machine learning (ML) regression predictive model based on experimental data collected on the real urban environment of the city of Krapina, Croatia, suitable for utilisation in personal mobility analysis, and traffic and transport planning and optimisation.
Izvorni jezik
Engleski
Znanstvena područja
Matematika, Elektrotehnika, Računarstvo, Zrakoplovstvo, raketna i svemirska tehnika
Napomena
The manuscript summarises the Erasmus+ project
results, accomplished by student Mr Boyan
Stoyanovski, while on his Erasmus+ traineeship at
Krapina University of Applied Sciences, Krapina,
Croatia, in Summer 2022.
