Accurate travel time estimation (TTE) plays a crucial role in intelligent transportation systems. However, it remains challenging due to heterogeneous data sources and complex traffic dynamics. Moreover, traditional approaches typically convert trajectory data into fixed-length representations. This overlooks the inherent variability of real-world motion patterns, often resulting in information loss and redundancy. To address these challenges, this paper introduces the Multimodal Dynamic Trajectory Integration (MDTI) framework--a novel multimodal trajectory representation learning approach that integrates GPS sequences, grid trajectories, and road network constraints to enhance the performance of TTE. MDTI employs modality-specific encoders and a multimodal fusion module to capture complementary spatial, temporal, and topological semantics, while a dynamic trajectory modeling mechanism adaptively regulates information density for trajectories of varying lengths. Two self-supervised pretraining objectives, named contrastive alignment and masked language modeling, further strengthen multimodal consistency and contextual understanding. Extensive experiments on three real-world datasets demonstrate that MDTI consistently outperforms state-of-the-art baselines, confirming its robustness and strong generalization abilities. The code is publicly available at: https://github.com/City-Computing/MDTI.

The rapid advancement of Internet of Vehicles technology has led to massive growth in vehicular data generation, imposing strict computational demands for latency-sensitive applications. By offloading computational tasks to Road-side Units (RSUs), Vehicular Edge Computing (VEC) offers an efficient solution for those latency-sensitive applications. However, current task offloading schemes generally ignore the time-varying topology caused by vehicle mobility, which poses a risk of task interruption. Moreover, existing task offloading models primarily focus on serial tasks processing and fail to adequately account for the relationships among parallel tasks, leading to inefficient resource utilization and potential latency accumulation in multi-task VEC scenarios. To this end, we propose a mobility-awareCollaborativeTaskOffloading scheme forParallel tasks (CoTOP) in VEC. Integrated with vehicle mobility detection, a collaborative task offloading model is designed based on deep reinforcement learning, achieving effective coordination among RSUs to reduce task processing latency. Additionally, a task prioritization algorithm is incorporated to optimize resource allocation. Experimental results show that CoTOP significantly outperforms existing schemes in terms of task processing latency, energy consumption and completion ratio.

For high-resolution hyperspectral (HrHs) imaging, spatial-spectral fusion offers a promising alternative to expensive equipment. However, retraining multiple models for varied scaling factors is currently unavoidable, costing extra computational resource and human labor. To address this issue, we propose Arbitrary-scale Fusion Operator (AFO), a lightweight solution for HrHs fusion given arbitrary scalings, turning the retraining strategy into “training-free” ones. Specifically, AFO treats low-resolution hyperspectral (LrHs) images and high resolution multispectral (HrMs) images as light-wise degraded functions within the spectrum, which are initially embedded into a high-dimensional space to simulate the original light signals, tapping the potential of enriched prior learning. Then, a flow of kernel integration (KI) is meticulously crafted, followed by a rival step of dimension reduction for HrHs generation. For a well-behaved KI computation, an Attention-Driven Convolution Integration (ADCI) is engineered to restore the broken discretization invariance derived by convolutions, yet with the locally inductive bias preserved. In addition, we propose an Implicit Neural Functional Integration (INFI) to achieve cross domain interaction of spatial degradation functions, followed by the use of Galerkin-type Integration (GI) as a decoder to handle high-frequency information. Finally, the bonded activation functions are improved for the principle of continuous-discrete equivalence. Extensive experiments validate the superiority of our approach over cutting-edge methods. Notably, our proposal holds significantly better generalization on arbitrary scaling factors, yet requires only 0.07M parameters.

Point-of-Interest (POI) recommendation is a critical task in location-based services, which seeks to suggest a user's next potential location by leveraging their check-in history. However, the accuracy of such data is frequently compromised by noise, arising from factors such as inadvertent check-ins and the influence of recommendation biases, which hampers the accurate modeling of user preferences. To mitigate the impact of noisy implicit feedback, we propose the Diffusion-Based Implicit Feedback Modeling for Point-of-Interest Recommendation (DIPOI). First, a hybrid hypergraph convolutional network is utilized to perform multi-hop relational reasoning, enabling the discovery of deep-seated preferences, particularly for users with low activity levels. Subsequently, DIPOI integrates the spatial learning capacity of graph neural networks with the temporal modelling strengths of attention mechanisms. This integration enables the capture of the structural dependencies among POIs and the sequential dynamics of user behaviour. Finally, a diffusion model is incorporated to progressively denoise the latent representation space via a reverse generative process based on users' historical travel preferences, thereby increasing the fidelity of the implicit feedback modelling. Experiments conducted on three real-world datasets show that DIPOI consistently achieves higher recommendation accuracy than state-of-the-art baselines, validating its effectiveness and robustness. The implementation code is available at https://github.com/dengzhaolin/DIPOI.

Recommendation systems can establish vital connections between users and information. But they are still facing data sparsity and cold start issues. Cross-domain recommendation improves performance via knowledge transfer, yet often depends on data sharing, conflicting with privacy needs. Federated learning offers a privacy-preserving alternative, but still struggles with knowledge transfer and communication overhead. In this paper, we propose FedPce, a Prompt-based Federated Cross-domain Recommendation. FedPce improves cross-domain recommendation and promotes knowledge transfer by leveraging item description texts and prompt learning, while reducing communication overhead and accelerating convergence through low-rank gradient compression and elastic aggregation strategies. Experimental results show that FedPce performs well on multidomain recommendation tasks, balancing recommendation effect and privacy protection.

Spatial-spectral fusion offers a promising alternative to expensive equipment in high-resolution hyperspectral (HrHs) imaging. However, training separate models for different scaling factors remains costly. To address this, we propose the Arbitrary-scale Fusion Neural Operator (AFNO), a lightweight solution for HrHs fusion across arbitrary scalings. Instead of entities, AFNO treats low-resolution hyperspectral (LrHs) and high-resolution multispectral (HrMs) images as functions and performs meticulously designed integrations as the mapping operator. The key components include Attention-Driven Convolution Integration (ADCI) to restore discretization invariance disrupted by convolutions, Implicit Neural Functional Integration (INFI) for cross-domain interaction of spatial degradations, and Galerkin-type Integration as a decoder for high-frequency details. Additionally, the bonded activation opeartor are improved for the principle of continuous-discrete equivalence. Extensive experiments validate the superiority of our approach over cutting-edge methods. Notably, AFNO holds significantly better generalization on arbitrary scaling factors, yet requiring only 0.07M parameters.

Knowledge graph completion (KGC) is dedicated to deducing absent facts within incomplete knowledge graphs (KGs). The majority of antecedent studies exclusively address the transductive scenario, wherein all entities are observed throughout the training process, which is impractical for the everyday reality with constantly emerging entities. Recent works that concern this issue mainly focus on representing emerging entities with seen neighbors while ignoring the semantics in the queried relations. In this paper, we introduce a model named DKGC, which incorporates two purposefully designed entity encoding modules to capture the local and global entity representations in a relation-specific manner and a sampling module with a diffusion process to explore the potential semantics further. Extensive experiments on benchmark datasets indicate the superiority of our model for inductive KGC.

Predicting traffic accidents can help traffic management departments respond to sudden traffic situations promptly, improve drivers’ vigilance, and reduce losses caused by traffic accidents. However, the causality of traffic accidents is complex and difficult to analyze. Most existing traffic accident prediction methods do not consider the dynamic spatio-temporal correlation of traffic data, which leads to unsatisfactory prediction accuracy. To address this issue, we propose a multi-task learning framework (TAP) based on the Spatio-temporal Variational Graph Auto-Encoders (ST-VGAE) for traffic accident profiling. We firstly capture the dynamic spatio-temporal correlation of traffic conditions through a spatio-temporal graph convolutional encoder and embed it as a low-latitude vector. Then, we use a multi-task learning scheme to combine external factors to generate the traffic accident profiling. Furthermore, we propose a traffic accident profiling application framework based on edge computing. This method increases the speed of calculation by offloading the calculation of traffic accident profiling to edge nodes. Finally, the experimental results on real datasets demonstrate that TAP outperforms other state-of-the-art baselines.

Vehicle-to-vehicle interaction and collaboration can provide us with a large number of mobile traffic trajectories that can be used to analyze driving behavior. In this article, we propose a spatio-temporal cost combination based framework for taxi driving fraud detection. First, the point of interest where taxis interact and collaborate with collaborative Internet of Vehicles participants is identified, and a baseline trajectory model is built to determine the typical trajectory distribution. Second, a statistical model is used to calculate the travel distribution, travel time, and travel cost. At the same time, the taxi trajectory points are converted into evolving graphs to detect the abnormality of the local road segment. Then, we can analyze the causes of outlier trajectories combined with the perception of abnormal road environments. Finally, the trajectories of real taxis were used to evaluate outliers, which proves the effectiveness and efficiency of the method.