Rational use of urban underground space (UUS) and public transportation transfer underground can solve urban traffic problems. Accurate short-term prediction of passenger flow can ensure the efficient, safe, and comfortable operation of subway stations. However, complex and nonlinear interdependencies between time steps and time series complicate such predictions. This study considered temporal patterns across multiple time steps and selected relevant information on short-term passenger flow for prediction. A hybrid model based on the temporal pattern attention (TPA) mechanism and the long short-term memory (LSTM) network was developed (i.e., TPA-LSTM) for predicting the future number of passengers in subway stations. The TPA mechanism focuses on the hidden layer output values of different time steps in history and of the current time as well as correlates these output values to improve the accuracy of the model. The card swiping data from the Hangzhou Metro automatic fare collection system in China were used for verification and analysis. This model was compared with a convolutional neural network (CNN), LSTM, and CNN-LSTM. The results showed that the TPA-LSTM outperformed the other models with good applicability and accuracy. This study provides a theoretical basis for the pre-allocation of subway resources to avoid subway station crowding and stampede accidents.

The transport sector accounts for 23% of global carbon emissions, the second largest after electricity and heat generation. Low-carbon travel, such as walking, cycling, and use of public transit, has become an effective way to reduce transportation-related emissions, however, what factors and how they affect low-carbon travel behavior remain unclear. This paper analyzes the key factors that affect the low-carbon travel behavior of urban residents by exploring 26 potential correlation indicators. Low-carbon travel survey data of urban residents in Zhenjiang, China are used as an example. Five factors derived from 26 indicators were identified and named as key factors influencing urban low-carbon travel behavior: traveler personal attributes (contribution rate 31.646%), user satisfaction with travel processes (contribution rate 17.438%), attitude towards low-carbon travel and environmental awareness (16.090%), the level of public transportation system development (10.793%), and overall attributes of travelers’ family (10.561%). The results provide a theoretical basis for the development and implementation of novel urban low-carbon travel concepts in the future.

In response to the demand for high-density urban renewal and quality enhancement, the microclimate of street canyon spaces has become a critical factor influencing pedestrian experience and public space vitality. As key nodes connecting above-ground and underground spaces, the entrances and exits of underground commercial streets are particularly sensitive to temperature, humidity, and wind conditions. This study examined a semi-open street canyon adjacent to Entrance No. 11 of the Jianjun Road Underground Commercial Street in Yancheng City as a case study. Through continuous field measurements and numerical simulations using ENVI-met v5.5.1, we conducted a comprehensive analysis. Five monitoring points were established at a height of 1.5 m to simultaneously record the air temperature, relative humidity, wind speed, and thermal radiation images. The results indicate that ventilation acceleration zones form near openings and channel constrictions, whereas leeward sides and corners are prone to stagnant airflow and heat accumulation. During afternoon periods with strong solar radiation and low wind speeds, the predicted mean vote (PMV) values near the entrance increased significantly. The simulation results were in good agreement with the field observations in terms of both the trend and spatial distribution. On the basis of these findings, optimization strategies are proposed including controlling enclosure ratios and local height-to-width ratios, utilizing ventilation corridors and side openings to guide airflow, and incorporating shading devices and low-emissivity materials to improve pedestrian thermal comfort and accessibility.

CO ₂ ‐enhanced oil recovery (CO ₂ ‐EOR) is an economically viable carbon capture, utilization, and storage (CCUS) technique that is widely practiced and greatly contributes to the achievement of carbon‐neutral cities. However, studies on CO ₂ ‐EOR source–sink matching involving different emission sources, different carbon capture rates, and stepwise CO ₂ pipeline construction are scarce. Considering four types of carbon sources, including coal‐fired power, iron and steel, cement, and chemical plants, with different CO ₂ capture rates (85%, 90%, 95%, and 100%, respectively), and using a five‐phased construction plan with a 25‐year build‐up period, we developed a method for quantifying carbon emissions from different sources, calculating the effective storage of carbon in CO ₂ ‐EOR and optimizing CO ₂ ‐EOR source–sink matching to reduce project costs. Using the Subei Basin in the Jiangsu Province, China, as a case study, we calculated the theoretical CO ₂ ‐EOR storage to be 1.7408 × 10 ⁸ t and the effective CO ₂ ‐EOR storage to be 0.435 × 10 ⁸ t. We analyzed the completion rate of transportation pipelines, the number of connected carbon sources, and the mass of CO ₂ stored, as well as the cost‐effectiveness and sensitivity. Implementation of CO ₂ ‐EOR effectively reduced the total cost of source–sink matching in the five‐stage 25‐year construction approach. The reduction of CO ₂ capture rates had no effect on the value of oil repelling. The capture cost significantly affected the total cost of source–sink matching, and the impacts of the carbon sources on the total cost were in the order coal‐fired power > iron and steel > cement > chemical plants. This study provides an innovative tool for evaluating the CO ₂ storage potential of CO ₂ ‐EOR and provides an important framework for implementing CO ₂ ‐EOR and planning CCUS projects in the Subei Basin and similar regions.