Traditional physics-informed deep learning combines the data-driven methods with the model-based methods by incorporating physics loss as a constraint in total loss function, which aims to enforce the neural network to behave according to the physics laws. However, the potential of physical knowledge is severely underestimated by this approach. Firstly, the physical knowledge fails to demonstrate its intended effects since the physics loss could have extremely small magnitude, more fluctuating convergence rates, and conflicting directions of the gradients compared to the data loss. Secondly, existing methods implicitly employ physics laws as auxiliary terms, which ignores that explicitly utilizing certain properties of physics laws can compensate for the shortcomings of data-driven models, particularly with regard to the data noise and relationships between variables. To alleviate these issues, we propose a Twin-Propeller Network (TPN) to realize fully message exchange among physical knowledge and data information, that releases the potential of the physics laws. Practically, we independently train data-driven model and physics-based model as two student models to get the information separated. Considering the measurement noise present in the data-driven model and the relatively robust physics-based model, we quantify the data uncertainty and utilize it as a weight to balance the two students in a integrated robust teacher model. The stronger teacher in turn transfers the respective knowledge to another student, where we innovatively propose traffic state relation distillation and physical knowledge distillation to guide the training of the data student and the physics student respectively. Through extensive experiments on both synthetic and real-world datasets, our model demonstrates better performance than the existing state-of-the-art methods.

Trajectory data analysis benefits numerous real-world applications and has attracted substantial attention from the research community. With the rapid proliferation of IoT devices and the emergence of edge computing, there has been an increasing demand for efficient trajectory data analytics in edge environments. However, most existing trajectory analysis systems are designed for cloud-based architectures, which face significant limitations in edge settings. These include resource constraints, dynamic network conditions, and inefficient query handling, leading to sub-optimal performance in edge scenarios. To fill this gap, we propose TrajEdge, an efficient and lightweight framework for trajectory data analysis in edge environments. Implementing TrajEdge requires overcoming obstacles posed by limited resources and the dynamic nature of edge networks. To achieve this, we design a novel trajectory composite compression algorithm that delivers high compression ratios, significantly reducing storage pressure on edge devices. Additionally, we introduce three coflow control strategies optimized for varying network conditions, enabling higher system throughput. To further enhance the efficiency of trajectory queries, we develop a spatiotemporal-aware trie-based peer-to-peer (P2P) index. Experimental evaluations on two real-world datasets and one larger synthetic dataset demonstrate that TrajEdge achieves remarkable performance improvements: more than 200 × gains in storage and query efficiency, up to 64% increases in network throughput, compression ratios of up to 95%, and exceptional scalability compared to the state-of-the-art systems. Our source code is available at https://github.com/ZJU-DAILY/TrajEdge.

Long-term time series forecasting has gained significant attention in recent years due to its widely-application in various fields. Transformer-based models have gained popularity for the ability to capture long-sequence interactions. However, these models are limited in real-world use because of the memory consumption and computation explosion. The CNN-based models are also one of the main models used for time series prediction, but their performance has always been inferior to the transformer-based models in previous works. We have reconsidered the role of CNN components and redefined the way CNN basic components are used for time series prediction. In addition, the time lags information between periods in the time series is important. Unfortunately, existing works lack consideration of this classic but important information. Motivated by these factors, we propose a fast yet effective CNN model with time lags for multivariate long-term time series forecasting, named LagCNN. Specifically, the time series is transformed into lag-patches to capture the correlation between periods. Then, a fast CNN model is performed in the feature dimension rather than the time dimension like most previous works do. Meanwhile, information aggregation is performed in the time dimension to extract complex temporal patterns. LagCNN significantly outperforms state-of-the-art on multiple publicly available datasets. One step further, LagCNN exhibits significant efficiency advantages over the most efficient Transformer model (PatchTST), resulting in a significant reduction in memory usage (4.4×) and runtime (10.7×).

The ubiquitous GPS sensors collect massive trajectory data from moving objects, which is useful in data mining applications. However, trajectory data is enormous in volume, and thus, directly storing and processing the raw data is expensive. Using trajectory simplification, a trajectory can be reduced to a set of continuous line segments with acceptable data loss, which is an efficient method. Although many algorithms are proposed, they still suffer from the following issues including (i) non-data driven capability as most studies rely on human-crafted rules or pre-defined parameters, (ii) bound with error measures that yield high computational cost, and (iii) focusing only on the local information preservation in trajectories, but failing in capturing the global mobility patterns for trajectory compression.To address the above issues, we propose a Seq2Seq2Seq framework, abbreviated S3, which consists of two chained Seq2Seq. With differentiable reconstruction learning, S3 enables self-supervised trajectory simplification in a lightweight manner. Besides, we deploy S3 over the graph neural architecture to capture the context-aware mobility patterns and enhance the representation paradigm of trajectories with geographical semantics, where a context-aware distance measure is designed for quality evaluation. An online extension of S3 is also developed to enable streaming trajectory simplifications. Finally, extensive experiments using two real-world datasets in both offline and online scenarios show that S3 achieves much higher efficiency (e.g., it achieves up to one order of magnitude speed-up gains) and comparable compression quality, compared with both non-learning and state-of-the-art learning-based methods.

Travel time prediction is a critical task in intelligent transportation system and location-based service. Existing studies build models based on the features extracted from trajectories, but few of them consider the sparsity of trajectory data from both temporal and spatial dimensions, as well as the spatial structure and heterogeneity. To address these issues, we propose a novel Multi-scale spatial-temporal model for Travel Time Prediction, abbreviated as MTTPRE. Specifically, the study area is represented as a flexible Voronoi graph according to a variable-sized partition scheme and the missing features on it are recovered via a spatial-temporal context-based method. Subsequently, a geospatial network with POI information is established to represent the spatial structure based on the Voronoi graph. Next, the multi-dimensional traffic condition features and graph-trajectory-POI multilevel features are extracted as spatial-temporal features. Finally, these features are fed into a hierarchical multi-task learning layer to complete the travel time prediction task. Extensive experiments on two real-world datasets show that the MTTPRE outperforms all the competitors with significant improvement and remarkable robustness.