Modern transportation systems are integral to urban sustainability, economic vitality, and public safety. The effective analysis of spatiotemporal traffic patterns is critical for urban planning and smart city operations. However, real-world traffic monitoring system often produce high-dimensional and incomplete data, making accurate traffic modeling and control extremely challenging. This paper proposes a Gated-Recurrent Neural Tucker Factorization (GARNT) model for robust traffic data imputation. It integrates neural Tucker factorization with gated recurrent units (GRUs) to jointly model spatial correlations and temporal dynamics. A GRU-based temporal encoder captures temporal dependencies across time steps, while a novel latent spatiotemporal feature interactive learning module is responsible for modeling spatiotemporal interaction features based on the principle of Tucker Decomposition. Extensive experiments on six real-world traffic datasets demonstrates our model outperforms existing state-of-the-art methods in both accuracy and robustness. By combining the tensor decomposition’s interpretability with deep learning’s expressive power, GARNT offers a principled and scalable solution for data imputation tasks in intelligent transportation systems.

Low-rank latent factorization of tensors is a powerful method for analyzing high-dimensional and incomplete (HDI) data derived from cyber-physical systems, particularly when computational resources are limited. However, traditional tensor factorization models are inherently linear and struggle to capture the complex nonlinear spatiotemporal dependencies embedded in the data. This paper introduces a novel latent factorization model, namelyAuto-encodingNeuralTuckerFactorization (ANTucF) for accurate spatiotemporal representation learning on the HDI tensor. It constructs a low-rank Tucker factorization-based neural network to capture a potential latent manifold in space and time, built upon three core ideas: a) applying density-oriented modeling principles with neural networks to facilitate latent feature learning via positional and temporal encoding of mode indices; b) constructing a Tucker interaction tensor to represent all possible spatiotemporal interactions among distinct spatial and temporal modes; and c) enhancing the uniqueness of the core tensor in Tucker factorization by incorporating nonlinear spatiotemporal representation learning via auto-encoding latent interaction learning. The ANTucF model outperforms several state-of-the-art LFT models in estimating missing observations on real-world datasets. Additionally, visualizations demonstrate its ability to capture finer spatiotemporal dynamics by nonlinearly exploiting an optimal Tucker core tensor using a data-driven approach.

Graph Neural Networks (GNNs) have emerged as powerful tools for analyzing and learning representations from graph-structured data. A crucial prerequisite for the outstanding performance of GNNs is the availability of complete graph information, i.e., node features and graph structure, which is frequently unmet in real-world scenarios since graphs are often incomplete due to various uncontrollable factors. Existing approaches only focus on dealing with either incomplete features or incomplete structure, which leads to performance loss inevitably. To address this issue, this study proposes a mutual dual-stream graph neural network (MDS-GNN), which implements a mutual benefit learning between features and structure. Its main ideas are as follows: a) reconstructing the missing node features based on the initial incomplete graph structure; b) generating an augmented global graph based on the reconstructed node features, and propagating the incomplete node features on this global graph; and c) utilizing contrastive learning to make the dual-stream process mutually benefit from each other. Extensive experiments on six real-world datasets demonstrate the effectiveness of our proposed MDS-GNN on incomplete graphs.

Accurate traffic Flow Prediction can assist in traffic management, route planning, and congestion mitigation, which holds significant importance in enhancing the efficiency and reliability of intelligent transportation systems (ITS). However, existing traffic flow prediction models suffer from limitations in capturing the complex spatial-temporal dependencies within traffic networks. In order to address this issue, this study proposes a multi-segment fusion-enhanced spatial-temporal graph convolutional network (MS-STGCN) for traffic flow prediction with the following three-fold ideas: a) building a unified spatial-temporal graph convolutional framework based on Tensor M-product, which capture the spatial-temporal patterns simultaneously; b) incorporating hourly, daily, and weekly components to model multi temporal properties of traffic flows, respectively; c) fusing the outputs of the three components by attention mechanism to obtain the final traffic flow prediction results. The results of experiments conducted on two traffic flow datasets demonstrate that the proposed MS-STGCN outperforms the state-of-the-art models.

Recognizing handwritten chemical structural formulas is challenging due to the spatial complexity involved, especially for chemical skeletal structural formulas. In chemical skeletal structural formulas, the Carbon atoms are omitted in the drawing of chemical connected bonds, in which the single bonds are connected with hidden Carbon atoms as the connection points. To recognize chemical skeletal structural formulas, it is important to recognize the connected bond strokes and differentiate them from other similar symbols. Moreover, even if the connected bond stroke is identified, it is also challenging to split the connected bond stroke into several single bonds and identify the locations of the hidden Carbon atoms, and its positional relationship in the overall chemical structure. In this paper, we propose a progressive approach for recognizing chemical skeletal structural formulas, in particular the connected bonds. The proposed approach recognizes a chemical formula progressively while the user draws the chemical skeletal structural formula.

With the growing popularity of pen-based and touch-based devices such as Apple's iPad and Samsung's Galaxy Tablet, handwriting has become an important input method. Although handwriting recognition for text contents and mathematical formulae are well-supported in these devices, recognizing handwritten chemical expressions is still very challenging due to its complex spatial structure. In this research, we focus on chemical symbol recognition which is essential for accurate handwritten chemical expression recognition. In particular, we propose an online hybrid Support Vector Machine - Elastic Matching (SVM-EM) approach for handwritten chemical symbol recognition. Based on the proposed chemical symbol recognition approach and an online structural analysis, we have implemented an online handwritten chemical expression recognition system on Apple's iOS platform. In this paper, we present our proposed SVM-EM approach for handwritten chemical symbol recognition and evaluate it with several users to verify its promising performance as a real application.

With the recent emergence of pen-based and touch based input devices such as Apple's iPad and Samsung's Galaxy Tablet, it has become more feasible now to input chemical formulas directly by handwriting, which is more natural and efficient than the traditional template-based input methods. In this paper, we propose an effective graph-based chemical structural analysis approach for online progressive handwritten chemical formula recognition. The proposed approach can progressively generate the recognition result after recognizing each symbol and users can make any corrections to the recognition result immediately. In addition, the proposed approach can recognize both cyclic and non-cyclic chemical structures. Recognizing cyclic structural formulas is challenging as bond orientations are very flexible and the relationships between symbols are much more complex than non-cyclic structural formulas. In this paper, the proposed chemical structural analysis approach and its promising performance results will be presented.