  We explain the methodology used to create the data submitted to HuMob
Challenge, a data analysis competition for human mobility prediction. We
adopted a personalized model to predict the individual's movement trajectory
from their data, instead of predicting from the overall movement, based on the
hypothesis that human movement is unique to each person. We devised the
features such as the date and time, activity time, days of the week, time of
day, and frequency of visits to POI (Point of Interest). As additional
features, we incorporated the movement of other individuals with similar
behavior patterns through the employment of clustering. The machine learning
model we adopted was the Support Vector Regression (SVR). We performed accuracy
through offline assessment and carried out feature selection and parameter
tuning. Although overall dataset provided consists of 100,000 users trajectory,
our method use only 20,000 target users data, and do not need to use other
80,000 data. Despite the personalized model's traditional feature engineering
approach, this model yields reasonably good accuracy with lower computational
cost.
