Search Results (2,201)

The rapid growth of urban vehicle and pedestrian flows has intensified congestion, delays, and safety concerns, underscoring the need for sustainable and intelligent traffic management in modern cities. Traditional centralized traffic signal control systems often face challenges of scalability, heterogeneity of traffic patterns, and limited real-time adaptability. To address these limitations, this study proposes a decentralized Multi-Agent Reinforcement Learning (MARL) framework for adaptive traffic signal control, where Deep Reinforcement Learning (DRL) agents are deployed at each intersection and trained on local conditions to enable real-time decision-making for both vehicles and pedestrians. A key innovation lies in the integration of Visible Light Communication (VLC), which leverages existing LED-based infrastructure in traffic lights, streetlights, and vehicles to provide high-capacity, low-latency, and energy-efficient data exchange, thereby enhancing each agent’s situational awareness while promoting infrastructure sustainability. The framework introduces a queue–request–response mechanism that dynamically adjusts signal phases, resolves conflicts between flows, and prioritizes urgent or emergency movements, ensuring equitable and safer mobility for all users. Validation through microscopic simulations in SUMO and preliminary real-world experiments demonstrates reductions in average waiting time, travel time, and queue lengths, along with improvements in pedestrian safety and energy efficiency. These results highlight the potential of MARL–VLC integration as a sustainable, resilient, and human-centered solution for next-generation urban traffic management. Full article

Extreme precipitation events, rapid urbanization, and irregular land use have significantly increased flood risk in recent years. In order to mitigate risks and enhance urban resilience, there is a need for the integration of innovative approaches with classical disaster management methods. This study uses geospatial artificial intelligence (GeoAI) methods to develop a flood risk analysis model. The proposed methodology is applied in the Marmara Region of Türkiye as a case study to highlight flood risk by evaluating factors such as precipitation, drainage density, and distance to waterways, population density, topography, water flow direction, and accumulation. Areas with high flood risk in the region are identified through the integration of hazard and vulnerability assessments, and explainable artificial intelligence (XAI) techniques are employed to identify the most significant factors contributing to flood susceptibility. Thus, a flood risk map of the Marmara Region is produced for eleven cities, utilizing open-source and government data to serve as an accessible guide for decision makers. This study aims to develop a flood risk analysis model through the integration of AHP-based hazard analysis and machine learning-based vulnerability assessment. This comprehensive hybrid approach facilitates the development of strategies for practical disaster risk reduction studies in a data-driven manner. Full article

While the inclusiveness of digital finance is widely acknowledged, existing research predominantly focuses on its developmental level, with limited attention to its spatial correlation network and structural characteristics. A city’s centrality within this network governs the flow and allocation of digital financial resources, thereby influencing interregional and urban-rural efficiency in resource allocation and income distribution, which ultimately shapes the trajectory of common prosperity. Based on panel data from 280 Chinese cities (2011–2021), this study employs social network analysis to measure urban centrality in the digital financial spatial correlation network and empirically investigates its impact and mechanisms on common prosperity. The main findings are as follows: (1) Benchmark regressions confirm that overall network centrality and its three dimensions—degree, betweenness, and closeness centrality—significantly promote common prosperity, specifically by enhancing the “wealth” dimension and reducing regional development disparities, with the growth effect currently surpassing the inclusion effect. (2) Robustness checks, including instrumental variable approaches addressing endogeneity, affirm the reliability of the core findings. (3) Heterogeneity analysis reveals that the positive effect is more pronounced in cities that are less developed or have weaker financial foundations, such as those in Western China, non-financial centers, cities with no presence of formal financial institutions in antiquity, fifth-tier cities, and small and medium-sized cities, suggesting that network centrality serves as a catalytic tool for urban catch-up strategies. (4) Mechanism analysis identifies that fostering entrepreneurship, particularly among self-employed individuals and wholesale/retail enterprises characterized by decentralized operations and abundant transaction data, is the primary channel through which centrality advances common prosperity. This study provides insights into promoting balanced regional development and common prosperity by optimizing the spatial structure of digital finance. Full article

Urban traffic congestion leads to daily delays, driven by outdated, rigid control systems. As vehicle numbers grow, fixed-phase signals struggle to adapt to real-time conditions. This work presents a decentralized Multi-Agent Reinforcement Learning (MARL) system to manage a traffic cell composed of five intersections, introducing the novel Strategic Anti-Blocking Phase Adjustment (SAPA) module, developed to enable dynamic phase time adjustments. The goal is to optimize arterial traffic flow by adapting strategies to different traffic generation patterns, simulating priority movements along circular or radial arterials, such as inbound or outbound city flows. The system aims to manage diverse scenarios within a cell, with the long-term goal of scaling to city-wide networks. A Visible Light Communication (VLC) infrastructure is integrated to support real-time data exchange between vehicles and infrastructure, capturing vehicle position, speed, and pedestrian presence at intersections. The system is evaluated through multiple performance metrics, showing promising results: reduced vehicle queues and waiting times, increased average speeds, and improved pedestrian safety and overall flow management. These outcomes demonstrate the system’s potential to deliver adaptive, intelligent traffic control for complex urban environments. Full article

Against the backdrop of global economic digital transformation and the rapid flow of creative factors, innovation spaces, as the key carriers of inventive activities, drive high-quality development in urban agglomerations. This study develops a three-dimensional framework of “Spatial Structure–Factor Synergy–Institutional Drivers” to uncover the evolution of innovation spaces and industrial shifts in the Beijing–Tianjin–Hebei urban agglomeration, China. Methodologically, spatial econometric techniques were applied to capture both the overall concentration and spatial disparities of innovation. Spatial Gini and variation coefficients measured innovation clustering, while standard deviation ellipses and location entropy identified spatial linkages among high-tech innovation clusters. Geographically weighted regression models explored spatial heterogeneity in influencing factors, and a policy intensity index was constructed to assess the effectiveness of differentiated policy interventions in optimizing innovation resources. Key findings include the following: (1) Innovation spaces are spatially polarized in a “core–periphery” pattern, yet require cross-regional collaboration. Concurrently, high-tech industries demonstrate a gradient structure: central cities leading in R&D, sub-central cities driving industrial applications, and node cities achieving specialized development through industrial transfer. (2) The driving mechanisms exhibit significant spatial heterogeneity: economic density shows diminishing returns in core areas, whereas R&D investment and ecological quality demonstrate increasingly positive effects, with foreign investment’s role evolving positively post-institutional reforms. (3) Regional innovation synergy has formed a preliminary framework, but strengthening sustainable policy mechanisms remains pivotal to advancing market-driven coordination and dismantling administrative barriers. These findings underscore the importance of integrated policy reforms for achieving balanced and high-quality innovation development in administratively coordinated urban agglomerations like BTH. Full article

A major challenge that will confront our society in the coming years is the socioecological transition. This involves a profound, systemic shift in how human societies interact with ecological systems. Beyond merely becoming “greener” or adding new technologies, it is about reorganising economies, lifestyles, institutions and cultural values to align with the planet’s ecological limits. The change also requires transforming the fundamental structure of societies to ensure their deep interconnection and compatibility with natural flows and ecological systems. To this end, it is valuable to explore the small, scattered practices which are currently leading to new organisational solutions or socioecological improvements. These initiatives are often regarded as forms of community resistance, adopting various approaches and strategies, which result in a disparate array of configurations. A comprehensive approach is thus needed to identify common patterns of development. A set of meaningful practices was analysed. The sample actions all took place in the urban context of Seville, a city located in Southwestern Europe and spanned various arenas driven by the transition to sustainability. Following the principles of qualitative research and a case study design, we adopted a qualitative method based on open-ended interviews, emphasising situated knowledge and collective construction of meaning. Moreover, a methodological approach based on interviews and further categorisation was followed to describe and organise ideas, motivations, risks, outcomes, as well as how the experiences evolved. The findings revealed that the core motivation driving the initiative in its initial phases is key. Outcomes nevertheless vary significantly depending on the initiative objectives. Generally, actions focused on specific elements—such as defending precise locations or activities—tend to be more successful and abundant. But the ones based on professional developments end up being somewhat stifled since they depend on the market to succeed. However, most rely somehow on public subsidies or support from public institutions, and their activities tend to diminish when such resources are reduced or withdrawn. The question is therefore how to make these initiatives more resilient in the future. The socioecological transition offers a path to strengthen social cohesion, empower collective action, and generate locally rooted and ecologically sustainable alternatives. Building community resilience—the capacity of local communities to adapt, recover and thrive amid these challenges—is, therefore, essential. Full article

The factors influencing urban pluvial flooding in cities with complex topography, such as hill–basin systems, are highly nonlinear and spatially heterogeneous due to the interplay between rugged terrain and intensive human activities. However, previous research has predominantly focused on plain, mountainous, and coastal cities. As a result, the waterlogging mechanisms in hill–basin areas remain notably understudied. In this study, we developed a geographically explainable artificial intelligence (GeoXAI) framework integrating Geographical Machine Learning Regression (GeoMLR) and Geographical Shapley (GeoShapley) values to analyze nonlinear impacts of flooding factors in Changsha, a typical hill–basin city. The XGBoost model was employed to predict flooding risk (validation AUC = 0.8597, R² = 0.8973), while the GeoMLR model verified stable nonlinear driving relationships between factors and flooding susceptibility (test set R² = 0.7546)—both supporting the proposal of targeted zonal regulation strategies. Results indicated that impervious surface density (ISD), normalized difference vegetation index (NDVI), and slope are the dominant drivers of flooding, with each exhibiting distinct nonlinear threshold effects (ISD > 0.35, NDVI < 0.70, Slope < 5°) that differ significantly from those identified in plain, mountainous, or coastal regions. Spatial analysis further revealed that topography regulates flooding by controlling convergence pathways and flow velocity, while vegetation mitigates flooding through enhanced interception and infiltration, showing complementary effects across zones. Based on these findings, we proposed tailored zonal management strategies. This study not only advances the mechanistic understanding of urban waterlogging in hill–basin regions but also provides a transferable GeoXAI framework offering a robust methodological foundation for flood resilience planning in topographically complex cities. Full article

Numerical simulation models are very useful for assessing groundwater flow and levels in a given region. With the scarcity of available groundwater resources after the 2000s, the city of Beijing adopted policies for the rehabilitation of these resources. This study establishes a numerical simulation model that evaluates the influence of these projects on groundwater levels over a given period. To achieve this, an unstructured model was established for the Beijing Plain region and run using GMS 10.6 software with a finer mesh around reservoirs, water stations, major rivers and flow boundaries. The calibration and the identification results indicated a correlation R² = 0.98 between calculated and observed heads. The model’s accuracy is good and the overall average relative error is less than 20%. The comparison of the calculated water balance with the results of numerous studies shows that the reliability of the equilibrium analysis result is relatively high. The groundwater numerical model is running to simulate the water level over a period of 15 years. Groundwater generally flows in a northwest/southeast direction. The simulation results also demonstrate the impact of some projects related to the South-to-North Water Transfer Project implemented for the restoration of overexploited groundwater resources. The model predicts a stabilized and significantly increasing groundwater level at the center of the Beijing area. Full article

Sustainable mobility policies are unlikely to succeed without efforts to tackle disagreement between different social groups. In this context, we describe a participatory process based around semi-structured interviews with expert stakeholders in sustainable mobility in the city of Madrid. Information elicited from interviews was structured using the Natural Step approach, based on detailed analysis of stakeholder discourse, into four scenarios of sustainable mobility: Remote Working, The 15-min City, Electric City and Public City. Subsequently, the four scenarios were subject to critical analysis by a second group of experts during a stakeholder workshop. The Remote Working scenario was considered a partial solution applicable to only ~30% of the population and saved commuter trips might be canceled out by increased mobility elsewhere. The 15-min City was seen as desirable but utopian and dependent on political consensus and major public investment. The Electric City was thought useful for reducing emissions but hard to implement due to infrastructure limitations and cost. The Public City was seen as an integrated vision from which other solutions should flow but also politically divisive. While no single scenario was unanimously backed by all participants, different coalitions of interest tended to support different approaches. Collectively, the four scenarios reveal divergent pathways to the same goal (a more sustainable city), suggesting ways forward for policy. Full article

In construction work at urban intersections, construction barriers can severely obstruct the view of right-turning vehicles, thereby posing safety hazards and increasing delays. Taking the intersection of Nanhu Avenue and Dongling South Street in Changchun City as an example, this paper innovatively proposes relocating the right-turn lane to the left, merging the right-turn traffic flow into the left-turn phase, and implementing dynamic left-turn relocation strategies for vehicles turning left in the opposite direction to separate traffic flows. This study provides detailed channelization design schemes for relocating the right-turn lane to the left and merging the left- and right-turn lanes. To optimize traffic efficiency and environmental benefits, a multi-objective signal timing optimization model was constructed to minimize the total intersection delay, maximize traffic capacity, and minimize carbon emissions. The model was analyzed using the non-dominated genetic algorithm (NSGA-II) to determine the effective green light duration for each phase, the pre-signal control scheme, and the length of the dynamically shifted left-turn lane, followed by VISSIM traffic simulation. The results show that compared with the traditional intersection, the optimized design reduced the delay for straight-through vehicles at the north entrance from 118 s to 21 s (a decrease of 82%). The delays for all phases at the south entrance decreased by 20–50%, and delays for straight and left-turn vehicles at the west and east entrances also decreased. The overall total delay decreased from 53.3 s to 41.1 s (a 23% reduction), validating the effectiveness and applicability of the method and model proposed in this work. Full article

Under the dual pressures of rapid urbanization and intense human socioeconomic activities, habitat fragmentation and poor landscape connectivity have become critical issues in cities. Constructing ecological networks is essential for maintaining urban ecosystem health and promoting sustainable environmental development. It represents an effective approach to balancing regional economic growth with ecological conservation. This study focused on the Shenzhen Special Economic Zone. Ecological sources were identified using Morphological Spatial Pattern Analysis (MSPA) and landscape connectivity assessment. Circuit theory was applied to extract ecological corridors, ecological pinch points, and ecological barriers. The importance levels of ecological corridors were classified to form an ecological network. The network was optimized by adding ecological sources, stepping stones, and restoring breakpoints. Its structure and functionality were evaluated before and after optimization. The results indicate the following: (1) The core area in Shenzhen City Area covers 426.67 km², the largest proportion among landscape types. It exhibits high fragmentation, low connectivity, and a spatial pattern characterized as “dense in the east and west, sparse in the center.” (2) Seventeen ecological sources were identified, consisting of 8 key sources, 5 important sources, and 4 general sources, accounting for 17.62% of the total area. Key sources are mainly distributed in forested regions such as Wutong Mountain, Maluan Mountain, Paiya Mountain, and Qiniang Mountain in the southeast. (3) Twenty-six ecological corridors form a woven network, with a total length of 127.44 km. Among these, 13 key corridors are concentrated in the eastern region, while 7 important corridors and 6 general corridors are distributed in the western and central parts. Few corridors exist in the southwest and southeast, leading to ecological flow interruption. (4) The optimized ecological network includes 12 newly added ecological source areas, 20 optimized ecological corridors, 120 ecological pinch points, and 26 ecological barriers. The maximum current value increased from 10.60 to 20.51, indicating significantly enhanced connectivity. The results provide important guidance for green space planning, biodiversity conservation, and ecosystem functionality enhancement in Shenzhen City Area. Full article

Efficient real-time computer vision-based passenger flow analysis is increasingly important for the management of intelligent transportation systems and smart cities. This paper presents the design and implementation of a system for real-time object detection, tracking, and people counting in tram stations. The proposed approach integrates YOLO-based detection with a lightweight tracking module and is deployed on an NVIDIA Jetson Nano device, enabling operation under resource constraints and demonstrating the potential of edge AI. Multiple YOLO versions, from v3 to v11, were evaluated on data collected in collaboration with Metropolitano de Tenerife. Experimental results show that YOLOv5s achieves the best balance between detection accuracy and inference speed, reaching 96.85% accuracy in counting tasks. The system demonstrates the feasibility of applying edge AI to monitor passenger flow in real time, contributing to intelligent transportation and smart city initiatives. Full article

With the development of cities, traffic congestion has become a common problem, which seriously affects the efficiency of motorway transport. This study proposed an improved ML-YOLO video data extraction model based on You Only Look Once (YOLOv8n) combined with the Deep Simple Online and real-time tracking (DeepSORT) algorithm, to classify the obtained Traffic Performance Index (TPI) into different congestion levels by extracting traffic flow parameters in real-time and combining with the K-means clustering algorithm. The Long Short-Term Memory Dropout (LSTM-Dropout) model and the emergency lane opening model were used to implement the road congestion warning successfully. The practicality and stability of the model were also verified by calculating the relative error between the predicted traffic flow parameters and the extracted parameters through the LSTM time series model. According to the model results, emergency lanes are opened when the motorway traffic TPI exceeds 0.17 and closed when below 0.17. This study provided a reasonable theoretical basis for motorway traffic managers to decide whether or not to open the emergency lane, effectively relieved motorway road congestion, improved efficiency of road traffic, and had important practical value and significance in reality. Full article

Forests are pivotal to hydrologic regulation, yet province-wide dynamics across complex terrain remain insufficiently quantified. We quantified Sichuan’s forest water conservation dynamics (1990–2023), coupling the InVEST water yield model with a topographic–hydraulic correction (topographic index, saturated hydraulic conductivity, land-cover-specific flow velocity). The model used precipitation and potential evapotranspiration, land-use/cover, soil texture, and rooting depth, and was calibrated to provincial water resources statistics. Outputs were stratified by elevation and slope and monetized via a replacement cost (reservoir capacity) method. Sichuan exhibited a persistent high-capacity belt along basin–mountain transitions and the southeastern ranges, contrasting with low values on the western plateau; period maxima intensified in 2020–2023. Interannual variability closely tracked precipitation anomalies against largely stable atmospheric demand; per-unit capacity declined monotonically with slope, and total capacity generally increased with elevation, with >3500 m both highest and most variable. Economic value rose overall but fluctuated and showed marked inter-city heterogeneity. We conclude that climate pacing operating on a terrain-anchored template governs Sichuan’s forest water conservation service, supporting precision, slope-aware forest management, and differentiated ecological compensation to stabilize hydrologic regulation under climate variability. Full article

Green technological innovation integrates the two major strategies of innovation-driven development and green development and serves as a crucial pathway to achieving the goal of high-quality and sustainable development in the Yangtze River Economic Belt (YREB). Against the backdrop of regional integration, it is of great significance to study the coordinated development trend of green technological innovation, with urban agglomerations as the unit of study. This study takes 108 cities in the YREB as research objects, constructs a Green Technological Innovation Efficiency (GTIE) measurement framework based on a two-stage DEA model, and decomposes GTIE into Technological Innovation Efficiency (TIE) and Green Production Capacity (GCP). On this basis, using the System GMM model, this study examines the mechanism by which the economic connection structure affects GTIE, TIE, and GCP from the perspective of urban agglomeration spatial networks. The empirical results show that from 2006 to 2020, the overall GTIE of the YREB showed a steady upward trend, and its spatial pattern evolved from “high in the east and low in the west” to “coordinated development of the three major urban agglomerations.” The three urban agglomerations played a core leading role in the diffusion of regional green innovation. Specifically, the economic integration development of urban agglomeration spatial networks significantly promoted the improvement of GTIE; the spatial network structure of TIE within the urban agglomerations exerted a significant positive spillover effect on GCP, while the GCP network structure also showed a significant feedback effect on TIE. Overall, through strengthening the inter-city flow of innovative factors and collaboration, regional integration has effectively promoted the coordinated growth and diffusion of green technological innovation, providing important support for the high-quality improvement of regional productivity and contributing to the sustainable development of the region. Full article