Dehua Peng

TL;DR: A novel hierarchical temporal attention-based LSTM encoder-decoder model for individual location sequence prediction that captures both long-term and short-term dependencies underlying in individual longitudinal trajectories, and uncovers frequential and periodical mobility patterns in an interpretable manner.

TL;DR: A distributed computing method to accelerate space–time Ripley’s K function upon state-of-the-art distributed computing framework Apache Spark is presented, and four strategies are adopted to simplify calculation procedures and accelerate distributed computing respectively.

TL;DR: A real-time individual driving destination prediction model LSI-LSTM is proposed based on an attention-aware Long Short-Term Memory by taking Location Semantics and Location Importance of trajectory points into account, and a trajectory location semantics extraction method (t-LSE) enriches feature description with prior knowledge for implicit travel intentions learning.

TL;DR: A novel multi-scale grid clustering algorithm, which fuses dual scale factors, i.e., analytical scale and visual scale that sequentially integrates multi-analytical-scale clustering (MASC) and multi-visual-scale clusters (MVSC) and can eliminate noise adaptively and effectively identify clusters with arbitrary shapes is proposed.

TL;DR: In this paper , a boundary-seeking clustering algorithm using the local direction centrality (CDC) is proposed, which adopts a density-independent metric based on the distribution of K-nearest neighbors (KNNs) to distinguish between internal and boundary points.