TY - JOUR
T1 - Machine learning for activity pattern detection
AU - Hadjidimitriou, Natalia Selini
AU - Cantelmo, Guido
AU - Antoniou, Constantinos
N1 - Publisher Copyright:
© 2022 Taylor & Francis Group, LLC.
PY - 2023
Y1 - 2023
N2 - This paper proposes a data fusion approach to automatically detect activity patterns in a GPS dataset based on travel diaries and correct misclassification errors. The Activity Patterns Detection consists of a Supervised Learning framework, thanks to which the activity purposes in the travel diaries are learned and then predicted in the GPS dataset. Furthermore, we deploy Unsupervised Learning to identify similar spatial and temporal activities in the GPS dataset and, based on travel diaries, to correct the misclassification errors. This work shows that, based on a few observations in the travel diaries and a set of features such as the resting time before the activity takes place, the number of occurrences of the same trip and the percentage of the trip made during daytime and the speed, it is possible to detect activities with an overall accuracy of 90%. Since the GPS dataset does not have information on the activity performed by the user, in reality, the aggregated results are validated based on the Kolmogorov-Smirnov test. The experiment shows that, with a confidence level of 99%, the majority of spatial and temporal feature distributions of activities in the travel diaries dataset are similar to those in the GPS dataset. Thanks to this approach, planners and transport operators can automatically obtain spatial and temporal patterns of frequent activities in urban areas.
AB - This paper proposes a data fusion approach to automatically detect activity patterns in a GPS dataset based on travel diaries and correct misclassification errors. The Activity Patterns Detection consists of a Supervised Learning framework, thanks to which the activity purposes in the travel diaries are learned and then predicted in the GPS dataset. Furthermore, we deploy Unsupervised Learning to identify similar spatial and temporal activities in the GPS dataset and, based on travel diaries, to correct the misclassification errors. This work shows that, based on a few observations in the travel diaries and a set of features such as the resting time before the activity takes place, the number of occurrences of the same trip and the percentage of the trip made during daytime and the speed, it is possible to detect activities with an overall accuracy of 90%. Since the GPS dataset does not have information on the activity performed by the user, in reality, the aggregated results are validated based on the Kolmogorov-Smirnov test. The experiment shows that, with a confidence level of 99%, the majority of spatial and temporal feature distributions of activities in the travel diaries dataset are similar to those in the GPS dataset. Thanks to this approach, planners and transport operators can automatically obtain spatial and temporal patterns of frequent activities in urban areas.
KW - Activity recognition
KW - GPS
KW - Urban informatics
KW - big data applications
KW - travel survey
UR - http://www.scopus.com/inward/record.url?scp=85131719511&partnerID=8YFLogxK
U2 - 10.1080/15472450.2022.2084336
DO - 10.1080/15472450.2022.2084336
M3 - Review article
AN - SCOPUS:85131719511
SN - 1547-2450
VL - 27
SP - 834
EP - 848
JO - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
JF - Journal of Intelligent Transportation Systems: Technology, Planning, and Operations
IS - 6
ER -