Traffic forecasting is pivotal but challenging due to intricate spatio-temporal dynamics. Existing models often apply a uniform spatial mechanism across distinct temporal scales and rely on static feature embeddings. Consequently, they are inadequate in capturing scale-specific spatial heterogeneity and dynamic feature interdependencies. To address these limitations, we propose the Multi-Scale Spatio-Temporal Attention Network (MSSTAN) with a novel dual-branch architecture: (1) A Global-Local Feature Attention Network (GLFAN) that explicitly decouples spatial interactions across decomposed temporal components to capture multi-scale spatial patterns; and (2) A Spatio-Temporal Feature Attention Network (STFAN) that dynamically recalibrates feature importance based on specific spatio-temporal contexts. A dynamic branch fusion mechanism integrates these branches to optimally aggregate their complementary views. Extensive experiments on five real-world datasets demonstrate that MSSTAN achieves state-of-the-art or highly competitive performance, validating its efficacy for traffic forecasting.

Temporal data analysis plays a pivotal role in applications such as weather forecasting, traffic flow management, energy consumption monitoring, and other areas of urban computing. In recent years, temporal data modeling has transitioned from traditional deep learning methods to pre-trained models. However, existing approaches often exhibit significant task-specific limitations, requiring bespoke model designs and extensive domain data for training. To address these challenges, this study introduces KPT, a novel foundation model for temporal data analysis in urban computing. By leveraging temporal competitive attention and feature interaction attention mechanisms, KPT can effectively capture global context, integrate cross-variable features precisely, and achieve universal feature learning across diverse time series tasks. Additionally, the knowledge prompt network facilitates the deep fusion of cross-layer features via an intricate interaction mechanism, enabling the model to identify and align shared temporal patterns across different time series data. These patterns then transformed into knowledge prompts, thereby enhancing the universal feature learning capabilities of the pre-trained model. Experimental results demonstrate that KPT excels in four core temporal analysis tasks within urban computing, outperforming task-specific models. This highlights KPT’s ability to generalize across tasks and underscores its potential as a foundation model for multi-task scenarios in urban computing.

Urban flow analysis is an essential research for smart city construction, in which urban flow pattern analysis focuses on the continuous state of urban flow. How to mine, store and reuse traffic patterns from urban multi-source heterogeneous big data is challenging. Therefore, this paper proposes a knowledge mining network for regional flow pattern to mine and store the urban flow pattern. The proposed model consists of two modules. In the first module, the features of the region and its flow pattern are extracted as the entity and relation, respectively. In the second module, POI features are modeled to enhance the embedding representation of relation and entity. Based on the translation distance method, the knowledge triplets of regional flow patterns are mined. Finally, the proposed model is compared with some benchmark methods using Chengdu Didi order and POI datasets. Experimental results show that the proposed model is effective. In addition, the knowledge triplets are visualized and some application examples are introduced.

Urban metro flow prediction is of great value for metro operation scheduling, passenger flow management and personal travel planning. However, the problem is challenging. First, different metro stations, e.g. transfer stations and non-transfer stations have unique traffic patterns. Second, it is difficult to model complex spatio-temporal dynamic relation of metro stations. To address these challenges, we develop a spatio-temporal dynamic graph relational learning model (STDGRL) to predict urban metro station flow. First, we propose a spatio-temporal node embedding representation module to capture the traffic patterns of different stations. Second, we employ a dynamic graph relationship learning module to learn dynamic spatial relationships between metro stations without a predefined graph adjacency matrix. Finally, we provide a transformer-based long-term relationship prediction module for long-term metro flow prediction. Extensive experiments are conducted based on metro data in four cities, China, with experimental results demonstrating the advantages of our method compared over 14 baselines for urban metro flow prediction.

Air quality forecasting has been regarded as the key problem of air pollution early warning and control management. In this article, we propose a novel deep learning model for air quality (mainly PM2.5) forecasting, which learns the spatial-temporal correlation features and interdependence of multivariate air quality related time series data by hybrid deep learning architecture. Due to the nonlinear and dynamic characteristics of multivariate air quality time series data, the base modules of our model include one-dimensional Convolutional Neural Networks (1D-CNNs) and Bi-directional Long Short-term Memory networks (Bi-LSTM). The former is to extract the local trend features and spatial correlation features, and the latter is to learn spatial-temporal dependencies. Then we design a jointly hybrid deep learning framework based on one-dimensional CNNs and Bi-LSTM for shared representation features learning of multivariate air quality related time series data. We conduct extensive experimental evaluations using two real-world datasets, and the results show that our model is capable of dealing with PM2.5 air pollution forecasting with satisfied accuracy.