Spatial Machine Learning Personal Mobility Predictive Model Trained with Smartphone-Collected Trajectory Data


  • Boyan Stpyanovski University of Ruse, Bulgaria
  • Teodor Iliev University of Ruse, Bulgaria
  • Radovan Cesarec Krapina University of Applied Sciences, Croatia
  • Renato Filjar Krapina University of Applied Sciences, Croatia



GNSS, trajectory, kinematics, machine learning, predictive model, mobility


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.


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How to Cite

Stpyanovski, B., Iliev, T., Cesarec, R. ., & Filjar, R. (2022). Spatial Machine Learning Personal Mobility Predictive Model Trained with Smartphone-Collected Trajectory Data. The Journal of CIEES, 2(2), 7–12.