Stay Time Prediction for Individual Stay Behavior

Stay time is important for understanding people's travel behavior and mobility motivation. In this paper, by leveraging private car trajectory data, we propose a novel systematic approach for implementing stay behavior detection and stay time prediction. Specifically, we first propose a fuzzy logic-based stay detection method for detecting stay behavior in a large-scale private car trajectory dataset. Then, we design a spatiotemporal feature extraction method called clustering and kernel (CaK) by considering the spatial similarity, temporal periodicity and spatiotemporal correlation of stay behavior data. Furthermore, we propose a stay time predictor (STP) based on gradient-boosting regression trees and a long short-term memory network that can estimate the future durations of private car users' stays in various scenarios. We perform extensive experiments based on two real-life trajectory datasets. The experimental results demonstrate that the STP achieves a predictive accuracy (specifically, the root-mean-square error) of 123.94 and R² of 0.893 for stay time prediction of individual stays. This study provides a new perspective for understanding people's stay behavior.