Trajectory similarity computation is essential for various downstream applications, such as anomaly route detection, order matching, and digital contact tracing. However, its effectiveness is confined within a single system due to privacy concerns associated with sharing raw trajectories across different systems. In this paper, we propose CrossSim, a novel framework designed to efficiently retrieve similar trajectories across all systems while preserving individual privacy. Our framework comprises three main components: i) a Trajectory Encoding Model that transforms trajectories into high-quality representations, where similarity relationships are reflected by their distances; ii) a two-stage optimization mechanism, including a Contrastive Similarity Learning stage and a Federated Similarity Learning stage, that alleviates the impact of heterogeneous similarity relationships across different systems on model training without aggregating raw trajectories; iii) a Similar Trajectory Retrieval procedure that obtains top-k similar trajectories from all systems without sharing raw trajectories. We conduct comprehensive experiments on three real-world datasets to evaluate the effectiveness of our proposed framework. The evaluation results demonstrate that CrossSim outperforms all existing schemees.

Massive urban-scale vehicle trajectories benefit various downstream applications. However, trajectories collected from existing sensing systems are often incomplete, necessitating the recovery of coarse-grained trajectories. Considering that mobility knowledge learned from a single system is less representative of all vehicles or covers only partial road segments, it becomes essential to combine diverse data from multiple systems to support trajectory recovery. Therefore, we learn the impacts of mobility intentions and dynamic traffic conditions on the movement of vehicles from trajectories aggregated across different systems to recover their travel routes on unobservable road intersections. Nonetheless, aggregating raw data across multiple systems raises privacy concerns. This data isolation compounds challenges in acquiring comprehensive mobility intentions and traffic conditions, thereby impairing recovery performance. In this paper, we proposeCrossTrace, a two-stage framework for privacy-aware cross-system trajectory recovery: in theTraffic Condition Inferencestage, a Split Learning pipeline with a multi-view graph neural network is utilized to infer complete traffic conditions for all road segments; in theTrajectory Recoverystage, a Federated Learning pipeline with dedicated modules is utilized to recover missing points by fusing inferred traffic conditions and mobility intentions. Extensive experiments on two large-scale trajectory datasets demonstrate thatCrossTraceoutperforms all alternative schemes.

Obtaining urban-scale vehicle trajectories is essential to understand urban mobility and benefits various downstream applications. The mobility knowledge obtained from existing vehicle trajectory sensing techniques is typically incomplete. To fill the gap, we propose$F^{3}VeTrac$, an efficient deep-learning-based vehicle trajectory recovery system that utilizes complementary characteristics of the Camera Surveillance System and the Vehicle Tracking System to obtain fine-grained, fully-road-covered, and fully-individual-penetrative ($F^{3}$) trajectories.$F^{3}VeTrac$utilizes five well-designed modules to model the co-occurrence relationships hidden in both coarse-grained and fine-grained trajectories from the two complementary sensing systems and fuse them to recover the coarse-grained trajectories. We implement and evaluate$F^{3}VeTrac$with two real-world datasets from over 100 million regular vehicle trajectories and 16 million commercial vehicle trajectories in two cities of China, together with an on-field case study based on 251 regular vehicle trajectories collected by 17 volunteers, demonstrating its great advantages over six state-of-the-art alternative schemes. Moreover, we present a downstream application of$F^{3}VeTrac$for traffic condition estimation, which obtains obvious performance gains.

Personalized federated learning (PFL) is a framework that targets individual models for optimization, providing better privacy and flexibility for clients. However, in challenging intelligent sensing applications, the heterogeneous client’s data distributions make the aggregation of local models in the server unstable or even hard to converge. To deal with the performance degradation caused by the preceding problem, existing PFL methods focus more on how to fine-tune the global model but ignore the impact of the global model fusion algorithm on the results. In this article, we propose a new explainable neural-aware decoupling fusion based PFL framework, p-FedADF , to address the preceding challenges. It contains two carefully designed modules. The local decoupling module, deployed on the client, utilizes the architecture disentangle technique to decouple the feature extractors in the client’s local model into sub-network according to data categories. It obtains the inference process of feature extraction for different categories of data by training. The global aggregation module, deployed on the server, aligns the sub-network positions for multiple clients and implements a fine-grained generic feature extractor aggregation. In addition, we provide a mask encoding scheme to reduce the communication overhead of transmitting the sub-network sets between the server and clients. Our p-FedADF obtains 1.6%, 0.2%, 2.3%, and 4.5% improvement on a real-world dataset and three benchmark datasets, compared to state-of-the-art methods.

Traffic prediction under various conditions is an important but challenging task. Latest studies have achieved promising results but suffer degraded performance without exception under abnormal conditions (e.g., accidents), as the traffic patterns under abnormal conditions often deviate from the normal seriously. To adapt to both normal and abnormal conditions, we propose theMulti-taskMulti-rangeMulti-subgraphAttentionNetwork (M3AN), a novel deep learning model to explicitly model the impacts of abnormal events for condition-aware traffic prediction. It constructs different subgraphs to model node features to address the abrupt traffic patterns with sparse abnormal event data, and uses an attention mechanism to capture dynamic spatial dependencies. Meanwhile, a multi-task fusion module is built upon a road-segment graph and an intersection graph to enhance the ability of capturing complicated dependencies, together with a multi-range attention module for automatically learning the influences of abnormal events with lower computational complexity. Experimental results on two real-world traffic datasets show that our M3AN outperforms state-of-the-art approaches under both normal and abnormal conditions.