Abstract
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 R 2 of 0.893 for stay time prediction of individual stays. This study provides a new perspective for understanding people's stay behavior.
Highlights
RELATED WORK In this part, we briefly summarize research related to stay behavior prediction, mainly considering three aspects: stay behavior detection, spatiotemporal feature extraction and stay time prediction methods
Our work focuses on spatial clustering and kernel density estimation methods for spatiotemporal feature extraction, which is suitable for modeling discrete and sparse stay behavior
We present a fuzzy logic-based method to detect stay behaviors in a large-scale private car dataset collected by low-cost on-board location terminals integrated with the global positioning system (GPS), on-board diagnostics (OBD) and communication modules
Summary
A private car is used with a clear purpose, and its locations can directly reflect personal travel demand and stay behavior. Several studies have used this characteristic of private car trajectory data to analyze human mobility. Each location along a trajectory has been labeled by [13] with a visit purpose by mining semantic mobility patterns from trajectories of private vehicles, and private car data have been used by [14] to predict the stop-and-wait behavior in cities. The studies [12]–[14] show that private car trajectory data can reflect human mobility and can be used to mine valuable knowledge. The objective of our study is to provide a new perspective for the study on individual stay behavior and implement intelligent prediction models for private car users
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