Personalized human mobility prediction for HuMob challenge
This work addresses personalized trajectory prediction for individuals in mobility analysis, though it appears incremental with traditional feature engineering.
The authors tackled human mobility prediction by developing a personalized model using Support Vector Regression with engineered features like time, activity patterns, and clustering of similar individuals, achieving reasonably good accuracy with lower computational cost while using only 20,000 target users' data instead of the full 100,000.
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.