Predicting PM2.5 concentrations at an hourly temporal resolution in urban areas can provide key information for public health protection. The spatiotemporal dependency among monitoring stations and the spatiotemporal correlations between PM2.5 and relevant factors (e.g. meteorology and emissions) are both essential for such predictions. This study proposes a multi-branch temporal graph convolutional neural network (MB-TGCN) for short-term predictions of PM2.5 concentrations at city monitoring stations. Composed of a set of graph convolutional networks (GCNs) for spatial dependency modeling, a set of gated recurrent units (GRUs) for temporal dependency modeling, and a multi-branch structure for integrating PM2.5 and relevant factors, MB-TGCN aims to accurately predict PM2.5 concentrations by capturing both spatial and temporal relationships through a graph modeling approach. Experiments with an air quality dataset from 35 stations in Beijing showed that MB-TGCN achieved higher accuracy than several deep learning models for various prediction durations ranging from 1 to 12 h. The method described in this study can help enhance the prediction capability of PM2.5 and provide decision support for environment-aware activity planning.

Urban logistics is vital to the development and operation of cities, and its optimization is highly beneficial to economic growth. The increasing customer needs and the complexity of urban systems are two challenges for current logistics optimization. However, little research considers both, failing to balance efficiency and cost. In this study, we propose a hybrid sparrow search algorithm (SA-SSA) by combining the sparrow search algorithm with fast computational speed and the simulated annealing algorithm with the ability to get the global optimum solution. Wuhan city was selected for logistics optimization experiments. The results show that the SA-SSA can optimize large-scale urban logistics with guaranteed efficiency and solution quality. Compared with simulated annealing, sparrow search, and genetic algorithm, the cost of SA-SSA was reduced by 17.12, 18.62, and 14.72%, respectively. Although the cost of SS-SSA was 11.50% higher than the ant colony algorithm, its computation time was reduced by 99.06%. In addition, the simulation experiments were conducted to explore the impact of spatial elements on the algorithm performance. The SA-SSA can provide high-quality solutions with high efficiency, considering the constraints of many customers and complex road networks. It can support realizing the scientific scheduling of distribution vehicles by logistics enterprises.

Empirical data are limited to decipher where people live and work in large cities; however, neighborhood information, such as street view image, is rich and abundant. We construct a ResNet-50-based social detection model to explore the potential relationship between street view images and job-housing attributes. The method extracts street view images of a neighborhood in all eight directions to predict land parcels’ job-housing attributes and uses an entropy index to measure the degree of job-housing mixture in Shenzhen as an example. The social-detection model performs well with a low RMSE (0.1094) in identifying job-housing patterns. The eight-direction neighborhood method shows the best support for sufficient neighborhood information from street view images (RMSE = 0.1135) compared with other neighborhood methods. This study demonstrates the feasibility of using street-view images and deep learning to characterize job-housing attributes consistent with findings from urban studies with socioeconomic data; for example, the research finding concurs that Shenzhen has many high job-housing mixtures with very few areas designated for jobs or residences. The proposed method, when applied regularly, can help monitor spatial dynamics of urban job-housing patterns to inform city planning and development.