Feature Selection for Enhancing Purpose Imputation using Global Positioning System Data without Geographic Information System Data

This paper presents a method for enhancing purpose imputation from global positioning system data without using geographic information system data via relevant feature selection from six groups: (1) activity time; (2) user characteristics; (3) predicted travel modes; (4) actual travel modes; (5) estimated home location; and (6) estimated location of the most frequently visited non-home place (MFVP). Two datasets were collected in 2019 using TRavelVU, a smartphone application. The first one (the Hanoi dataset) comprised 652 days’ worth of data collected from 63 users in Hanoi, Vietnam, whereas the second one (the Donate dataset) comprised 932 days’ worth of information collected from 65 individuals in Denmark, Sweden, and Norway. The hyperparameters of the random forest models were tuned carefully in accordance with selected features, thereby facilitating a thorough evaluation of the improvement in prediction models. The findings of this study revealed that the addition of either actual or predicted modes resulted in improved imputation performance, albeit the former exhibited a stronger positive effect. This demonstrated the potential benefits of integrating mode detection and purpose identification into a continuous process. The newly adopted MFVP feature contributed to enhanced prediction results (around 2%). The proposed purpose-imputation models, which benefited from all features, demonstrated accuracies of the order of 75% and 85% for the Hanoi and Donate datasets, respectively. The imputation of home and work/education activities demonstrated high success, whereas reasonable prediction results with nearly all F-score levels ranging between 50% and 83% were observed for pick-up/drop-off, shopping/eating, visit/leisure, and business activities.