Mobility data typically covers both the spatial and temporal domain. The literature lacks methods which fully exploit the richness and at the same time manage the complexity of such data sets. Moreover, mobility data is often characterised by poor quality. In this paper two novel techniques are proposed that facilitate the advanced analysis of (spatio-temporal) mobility data. First, an incremental imputation technique is realised, which reduces substantially the amount of missing data by cleverly exploiting the spatio-temporal properties of historical data. Second, a multi-step non-negative matrix factorisation workflow is conceived allowing to extract spatio-temporal fingerprints of traffic trajectories of interest. The validation of both methods on a data set of vehicle counts from multiple adjacent locations in the Brussels-Capital Region (Belgium) produced already very promising results.
Dhont, M , Tsiporkova, E & González-Deleito, N 2021, Deriving Spatio-temporal Trajectory Fingerprints from Mobility Data using Non-Negative Matrix Factorisation . in B Xue, M Pechenizkiy & YS Koh (eds), 2021 International Conference on Data Mining Workshops (ICDMW). IEEE International Conference on Data Mining Workshops, ICDMW, vol. 2021-December, IEEE, Auckland, New Zealand, pp. 750-759, 21st IEEE International Conference on Data Mining, Auckland, New Zealand, 7/12/21 .
Dhont, M. , Tsiporkova, E., & González-Deleito, N. (2021). Deriving Spatio-temporal Trajectory Fingerprints from Mobility Data using Non-Negative Matrix Factorisation . In B. Xue, M. Pechenizkiy, & Y. S. Koh (Eds.), 2021 International Conference on Data Mining Workshops (ICDMW) (pp. 750-759). (IEEE International Conference on Data Mining Workshops, ICDMW Vol. 2021-December). IEEE.
@inproceedings{64b5ac8fcd5c4897a564712728345743,
title = " Deriving Spatio-temporal Trajectory Fingerprints from Mobility Data using Non-Negative Matrix Factorisation " ,
abstract = " Mobility data typically covers both the spatial and temporal domain. The literature lacks methods which fully exploit the richness and at the same time manage the complexity of such data sets. Moreover, mobility data is often characterised by poor quality. In this paper two novel techniques are proposed that facilitate the advanced analysis of (spatio-temporal) mobility data. First, an incremental imputation technique is realised, which reduces substantially the amount of missing data by cleverly exploiting the spatio-temporal properties of historical data. Second, a multi-step non-negative matrix factorisation workflow is conceived allowing to extract spatio-temporal fingerprints of traffic trajectories of interest. The validation of both methods on a data set of vehicle counts from multiple adjacent locations in the Brussels-Capital Region (Belgium) produced already very promising results. " ,
keywords = " Mobility Data, Non-negative Matrix Factorisation, Data Imputation, Spatio-temporal fingerprinting, Trajectory " ,
author = " Michiel Dhont and Elena Tsiporkova and Nicol{'a}s Gonz{'a}lez-Deleito " ,
year = " 2021 " ,
month = dec,
day = " 7 " ,
doi = " 10.1109/icdmw53433.2021.00098 " ,
language = " English " ,
isbn = " 978-1-6654-2428-8 " ,
series = " IEEE International Conference on Data Mining Workshops, ICDMW " ,
publisher = " IEEE " ,
pages = " 750759 " ,
editor = " Bing Xue and Mykola Pechenizkiy and Koh, {Yun Sing} " ,
booktitle = " 2021 International Conference on Data Mining Workshops (ICDMW) " ,
note = " 21st IEEE International Conference on Data Mining, IEEE ICDM Conference date: 07-12-2021 Through 10-12-2021 " ,
url = " https://icdm2021.auckland.ac.nz " ,
}