Studying the urban heat island effect and actively exploring effective measures for its mitigation and alleviation can provide important parameters for urban ecological environment monitoring and propose rational strategies to address environmental degradation. This article, with the background of urban renewal projects in Beijing, focuses on the central area of Beijing as the research object. Landsat ETM+/OLI_ TIRS data from 2000 to 2020 are used as the main remote sensing imagery source, combined with functional information data and spatial attribute data of open spaces in the central area. Based on the mono-window (MW) algorithm, this study first quantitatively retrieves and categorizes the summer land surface temperature in Beijing’s central area and analyzes its spatiotemporal characteristics using the direction distribution method, revealing regular patterns in the temporal and spatial dimensions. The results show a gradual decrease in the size of the persistent high-temperature concentration area over time. Subsequently, the seasonal autoregressive integrated moving average (SARIMA) model is employed to predict the changing trends of the urban heat island and the occurrence time of the strongest and weakest heat islands. Higher land surface temperature (LST) years are projected for 2025 and 2035, with the lowest year being 2030. Lastly, the correlation coefficient and Moran’s index are used to analyze the correlation between the urban heat island and its corresponding influencing factors in different years. The results indicate that population density, nighttime light, and gross domestic product (GDP) have significant positive effects on the heat island intensity from a temporal perspective. Normalized difference vegetation index (NDVI) shows a significant negative relationship with the heat island intensity when analyzed over time. The research findings provide important reference for rational urban planning, layout, and construction, and hold significance for advancing urban renewal efforts.

Shared bicycle systems play a crucial role in promoting sustainable urban transportation, addressing challenges such as traffic congestion and air pollution. Understanding the spatiotemporal patterns of shared bike usage is essential for optimizing bike-sharing infrastructure and improving transportation planning. In this study, we analyzed 2.4 million records of shared bicycle data to explore the spatial distribution, interaction patterns, and flow dynamics within Beijing’s urban central area. We found that bike distribution peaks during commuting hours, particularly in central regions with employment centers. Complex networks are an important method for studying travel flows. Through a spatial interaction network, we identified key streets with high node strength and popularity, often concentrated in central areas. They experience heavy shared bicycle use during peak hours due to their employment-centric location. Conversely, peripheral areas see increased usage in the evenings, reflecting distinct commuting patterns. The morning exhibits higher positive central values compared to the evening, while negative values show the opposite trend. Based on these findings, we recommend enhancing bike infrastructure in high-density areas with bike lanes and ample shared bikes during peak hours. Implementing mixed-use zoning policies in the central region can reduce traffic congestion. Expanding shared bike services to peripheral regions can promote equitable access. This research underscores the importance of considering spatial and temporal factors in urban transportation planning. Future work should incorporate additional data sources, explore environmental impacts, and analyze usage in different seasons and special events, further contributing to sustainable urban mobility development.

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing’s six central districts. Subsequent to this, a comprehensive analysis of the network’s structural attributes was carried out. A Walktrap method, rooted in modularity analysis, was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies. These research findings not only contribute to enhancing urban transportation planning and bike-sharing systems but also provide robust guidance for advancing more efficient and sustainable urban transportation solutions, thereby fostering the sustainable development of cities.

As the urban transportation demand continues to grow, the effective management and optimization of bike-sharing systems are of significant importance for urban planning and transportation engineering. This study aims to identify the spatiotemporal distribution of the peak-period departures and arrivals of bike sharing within Manhattan, New York, and to analyze the community clustering patterns and their underlying rules. Additionally, a comparative analysis across multiple time periods was conducted to enhance the research’s practical value. This study utilized GPS trajectory data from the New York City bike-sharing system for 2023. After analyzing the travel patterns throughout the year, we selected August, the month with the highest usage, to study the origin-destination (OD) travel aggregation patterns using flow models and the theoretical constructs of travel networks, measuring and analyzing travel characteristics. Subsequently, community detection algorithms were applied to analyze the clustering patterns and relationships among various neighborhoods. The findings revealed that the use of bike sharing in New York exhibits an overall trend of increasing and then decreasing throughout the year, with significantly higher usage in the spring and summer compared to the fall and winter. Notably, August saw the highest usage levels, with hotspots primarily concentrated in the southwestern part of Manhattan, which is also the economic center of New York City. The OD aggregation patterns across the upper, middle, and lower parts of August show distinct variations. Through community analysis, several strongly associated neighborhood clusters were identified, which exhibited both aggregation and dispersion trends over time. In southern Manhattan, a community with high modularity emerged, showcasing strong interconnections among neighborhoods. These findings provide valuable insights into the usage patterns of bike sharing in New York and the factors influencing them, offering significant implications for the optimization of bike-sharing system operations and planning.

Examining the clustering characteristics and fluctuations within urban areas during peak hours through the lens of bike-sharing is of utmost importance in the optimization of bike-sharing systems and urban transportation planning. This investigation adopts the principles of urban spatial interaction network construction and employs streets as the fundamental units of analysis to model bike-sharing activities during morning and evening peak hours within Beijing's six central districts. Subsequent to this, a comprehensive analysis of the network's structural attributes was carried out. A walktrap method rooted in modularity analysis was introduced to discern and scrutinize the clustering patterns and characteristics of communities within the network across different temporal intervals. Empirical findings reveal a predominant usage pattern of shared bicycles for short-distance travel during both morning and evening peak hours. Notably, distinctive community structures manifest during these periods, characterized by two large communities and multiple smaller ones during the morning peak, while the evening peak showcases a single large community alongside several medium-sized and smaller ones. Moreover, the extended interaction radius points to an expanded geographic range of interactions among streets. These findings bear significant implications for the management of urban transportation, bike-sharing enterprises, and urban residents, proffering valuable insights for the optimization of bike-sharing schemes and transportation strategies.

Amidst the increasing complexity of urban traffic networks and the frequent pedestrian-vehicle conflicts at intersections, this study addresses the challenges inherent in conventional methodologies, which often fail to accurately capture dynamic decision-making processes and are heavily reliant on empirical data. We propose an innovative simulation framework that synergistically integrates multi-agent behavioral modeling with dynamic non-cooperative game theory. This framework enables precise representation of pedestrian and vehicle behavioral strategies, conflict evolution, and spatial distribution patterns, complemented by conflict density mapping for enhanced visual risk assessment. Empirical results demonstrate the model’s capability to accurately identify high-risk zones and to substantially mitigate conflict frequency and severity through the modulation of vehicle yielding probabilities and the regulation of pedestrian behavior. By transcending the limitations of traditional static-rule-based approaches, the proposed framework offers a robust and theoretically grounded tool for traffic safety risk evaluation, thereby furnishing valuable insights for intersection safety management and the design of intelligent transportation systems.