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.

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.

Missing traffic data is a common phenomenon in practice, which has serious implications for the development of downstream data-driven traffic research. Although numerous methods have been proposed to impute missing values, two limitations still exist: lack of complete ground truth and reliance on carefully constructed model structures. In this paper, we propose a novel deep neural imputation network framework named STIseq2seq, based on self-supervised contrastive learning. It follows a sequence-to-sequence architecture with attention mechanism to address the traffic data imputation problem. Specifically, the model consists of two key components: encoder and decoder, both composed of ST-block. Initially, we utilize dynamic graph convolution (DGC) and gated recurrent unit (GRU) to construct the fundamental unit, ST-block, for learning spatiotemporal dependencies. Then, a bidirectional ST-block is employed to model incomplete input data in the encoder; while a forward ST-block combined with an attention mechanism is used to accurately impute missing data in the decoder. Lastly, we devise a training method based on self-supervised contrastive learning, achieved through randomly shuffling the masking matrix, to enhance training effectiveness. Extensive experiments conducted on two available real-world traffic datasets demonstrate that our proposed STIseq2seq has superior and stable performance compared to multiple baseline models under different missing data scenarios with missing rates ranging from 20% to 80%. Visualization analysis of the imputation results further provides insights into the advantages of STIseq2seq.