Author name cluster

Hua Lu

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

5 papers

1 author row

AAAI Conference 2026 Conference Paper

MovSemCL: Movement-Semantics Contrastive Learning for Trajectory Similarity

Zhichen Lai
Hua Lu
Huan Li
Jialiang Li
Christian S. Jensen

Trajectory similarity computation is fundamental functionality that is used for, e.g., clustering, prediction, and anomaly detection. However, existing learning-based methods exhibit three key limitations: (1) insufficient modeling of trajectory semantics and hierarchy, lacking both movement dynamics extraction and multi-scale structural representation; (2) high computational costs due to point-wise encoding; and (3) use of physically implausible augmentations that distort trajectory semantics. To address these issues, we propose MovSem, a movement-semantics contrastive learning framework for trajectory similarity computation. MovSem first transforms raw GPS trajectories into movement-semantics features and then segments them into patches. Next, MovSem employs intra- and inter-patch attentions to encode local as well as global trajectory patterns, enabling efficient hierarchical representation and reducing computational costs. Moreover, MovSem includes a curvature-guided augmentation strategy that preserves informative segments (e.g., turns and intersections) and masks redundant ones, generating physically plausible augmented views. Experiments on real-world datasets show that MovSem is capable of outperforming state-of-the-art methods, achieving mean ranks close to the ideal value of 1 at similarity search tasks and improvements by up to 20.3% at heuristic approximation, while reducing inference latency by up to 43.4%.

PDF Details DOI

NeurIPS Conference 2025 Conference Paper

Not All Data are Good Labels: On the Self-supervised Labeling for Time Series Forecasting

Yuxuan Yang
Dalin Zhang
Yuxuan Liang
Hua Lu
Gang Chen
Huan Li

Time Series Forecasting (TSF) is a crucial task in various domains, yet existing TSF models rely heavily on high-quality data and insufficiently exploit all available data. This paper explores a novel self-supervised approach to re-label time series datasets by inherently constructing candidate datasets. During the optimization of a simple reconstruction network, intermediates are used as pseudo labels in a self-supervised paradigm, improving generalization for any predictor. We introduce the Self-Correction with Adaptive Mask (SCAM), which discards overfitted components and selectively replaces them with pseudo labels generated from reconstructions. Additionally, we incorporate Spectral Norm Regularization (SNR) to further suppress overfitting from a loss landscape perspective. Our experiments on eleven real-world datasets demonstrate that SCAM consistently improves the performance of various backbone models. This work offers a new perspective on constructing datasets and enhancing the generalization of TSF models through self-supervised learning. The code is available at https: //github. com/SuDIS-ZJU/SCAM.

PDF Details

IJCAI Conference 2024 Conference Paper

Personalized Federated Learning for Cross-City Traffic Prediction

Yu Zhang
Hua Lu
Ning Liu
Yonghui Xu
Qingzhong Li
Lizhen Cui

Traffic prediction plays an important role in urban computing. However, many cities face data scarcity due to low levels of urban development. Although many approaches transfer knowledge from data-rich cities to data-scarce cities, the centralized training paradigm cannot uphold data privacy. For the sake of inter-city data privacy, Federated Learning has been used, which follows a decentralized training paradigm to enhance traffic knowledge of data-scarce cities. However, spatio-temporal data heterogeneity causes client drift, leading to unsatisfactory traffic prediction performance. In this work, we propose a novel personalized Federated learning method for Cross-city Traffic Prediction (pFedCTP). It learns traffic knowledge from multiple data-rich source cities and transfers the knowledge to a data-scarce target city while preserving inter-city data privacy. In the core of pFedCTP lies a Spatio-Temporal Neural Network (ST-Net) for clients to learn traffic representation. We decouple the ST-Net to learn space-independent traffic patterns to overcome cross-city spatial heterogeneity. Besides, pFedCTP adaptively interpolates the layer-wise global and local parameters to deal with temporal heterogeneity across cities. Extensive experiments on four real-world traffic datasets demonstrate significant advantages of pFedCTP over representative state-of-the-art methods.

PDF Details DOI

TIST Journal 2023 Journal Article

A Query Optimizer for Range Queries over Multi-Attribute Trajectories

Jianqiu Xu
Hua Lu
Zhifeng Bao

A multi-attribute trajectory consists of a spatio-temporal trajectory and a set of descriptive attributes. Such data enrich the representation of traditional spatio-temporal trajectories to have comprehensive knowledge of moving objects. Range query is a fundamental operator over multi-attribute trajectories. Such a query contains two predicates, spatio-temporal and attribute, and returns the objects whose locations are within a distance threshold to the query trajectory and attributes contain expected values. There are different execution plans for answering the query. To enhance the capability of a trajectory database, an optimizer is essentially required to (i) accurately estimate the cost for alternative query strategies in terms of disk accesses, (ii) build a decision-making module that automatically sorts the data in an appropriate way and selects the optimal query plan, and (iii) update the analytical models when new trajectories are arrived. The cost model supports both uniform and non-uniform spatio-temporal data distribution and incorporates attribute distribution. The optimizer is fully developed inside a database system kernel and comprehensively evaluated in terms of accuracy and effectiveness by using large real and synthetic datasets.

Details DOI

TIST Journal 2015 Journal Article

City-Scale Social Event Detection and Evaluation with Taxi Traces

Wangsheng Zhang
Guande Qi
Gang Pan
Hua Lu
Shijian Li
Zhaohui Wu

A social event is an occurrence that involves lots of people and is accompanied by an obvious rise in human flow. Analysis of social events has real-world importance because events bring about impacts on many aspects of city life. Traditionally, detection and impact measurement of social events rely on social investigation, which involves considerable human effort. Recently, by analyzing messages in social networks, researchers can also detect and evaluate country-scale events. Nevertheless, the analysis of city-scale events has not been explored. In this article, we use human flow dynamics, which reflect the social activeness of a region, to detect social events and measure their impacts. We first extract human flow dynamics from taxi traces. Second, we propose a method that can not only discover the happening time and venue of events from abnormal social activeness, but also measure the scale of events through changes in such activeness. Third, we extract traffic congestion information from traces and use its change during social events to measure their impact. The results of experiments validate the effectiveness of both the event detection and impact measurement methods.

Details DOI