Search Results (2,032)

Urban crises rarely occur in isolation but emerge as interconnected disruptions across space, time, and institutions. The COVID-19 pandemic intensified existing vulnerabilities and intersected with other crises, producing cascading effects. This paper asks how cascading effects analysis can be used as a planning-oriented method to map and govern compound urban crises, drawing on case studies from Cape Town, Dortmund, and São Paulo. In Cape Town, South Africa, the pandemic intersected with high HIV and tuberculosis rates and load shedding, straining health and social services. In Dortmund, Germany, COVID-19’s economic disruptions overlapped with an energy price crisis, while in São Paulo, Brazil, lockdowns coincided with increased gender-based violence and constrained access to support services. Together, these cases show how pre-existing socio-political and economic conditions shape the impacts of crises, exacerbating marginalization and deepening systemic inequalities. Cascading effects analysis is used to visualize and address interdependencies in compound crises, helping planners move beyond sectoral silos, identify key intervention points for crisis management, and support more resilient and equitable urban planning. The paper calls for a methodological shift in urban crisis research toward tools that better communicate systemic risk and bridge risk assessment, social vulnerability, and planning practice. Full article

Metro systems are high-capacity urban rail networks designed to provide fast, reliable, and efficient transportation. This article presents a comparative benchmarking study of six leading metro systems in Brazil and six prominent international cases, aiming to identify best practices and recurring challenges based on key operational, planning, design, governance, and performance indicators. The Brazilian systems analyzed are located in Rio de Janeiro, São Paulo, Belo Horizonte, Fortaleza, Recife, and Salvador, while the international cases include London, Paris, Tokyo, Berlin, New York, and Madrid. The methodology combined documentary research with technical analysis of public data sources, institutional reports, and performance indicators. The results reveal significant contrasts in network scale, operational efficiency, governance models, funding mechanisms, and integration with urban planning. São Paulo’s system stands out for its network robustness, automation, and consolidated monitoring framework, while other Brazilian cities face limitations in service coverage and financial sustainability. The international cases offer valuable insights into fare integration, the use of emerging technologies, and the application of performance metrics to foster more sustainable and efficient high-capacity urban transit systems. The findings provide relevant evidence to support policymakers, transport authorities, and urban planners in improving the planning, management, and sustainability of high-capacity urban transit systems. Full article

Examining pedestrian crossing violations at high-risk road midblock crossings has become essential, particularly in high-speed corridors, as a result of accidents at crossings resulting in fatalities. Hence, this article investigates such behavior in Alexandria, Egypt, as a credible case study in a developing country. According to our research methodology, a comprehensive dataset of over 2400 field-observed video recordings was used for real-life data collection. Machine learning (ML) models, such as CatBoost and gradient boosting (GB), were employed to predict crossing decisions. The models showed that risky behavior is strongly influenced by waiting time, crossing time, and the number of crossing attempts. The highest predictive performance was achieved by CatBoost and gradient boosting, indicating strong interpersonal influence within small groups engaging in unsafe road-crossing behavior. In the same context, the Shapley additive explanation (SHAP) values for these variables were 3, 2, and 0.60, respectively. Subsequently, based on SHAP sensitivity analysis, the results show that pedestrian crossing time (s) had the highest tendency to push the model towards class 1 (e.g., crossing illegally), while total time (s) and age group (40–60 Y) had a significant negative influence on model prediction converging to class 0 (e.g., crossing illegally). The results also showed that shorter exposure times increase the likelihood of crossing illegally. This research work is among the few studies that employ a behavior-based approach to understanding pedestrian behavior at midblock crossings. This study offers actionable insights and valuable information for urban designers and transportation planners when considering the design of midblock crossings. Full article

Landscape ecology is the study of how different land uses and natural areas are arranged across a region, and how these spatial patterns affect biodiversity, ecosystem health, and human impacts. To measure and track these patterns, ecologists are using a range of tools and metrics that capture features such as connectivity, fragmentation, and the balance between natural and developed land. One such method is the Landscape Mosaic (LM) approach which classifies land into categories based on the mix of agriculture, natural habitats, and developed areas (e.g., urban), providing an integrated view of how humans are influencing ecosystems. Until recently, LM was only available through a specialized software package (i.e., GuidosToolbox), which limits its flexibility, interaction with other tools, and integration in scientific workflows. To address this, we present PyLM, a Python-based implementation of the LM model, making it easier for researchers, planners, and conservationists to analyze land use/cover (LUC) maps, generate statistics, and embed results into broader environmental workflows. The applicability of PyLM is demonstrated through a use case based on a LUC dataset for Switzerland. This new implementation enhances accessibility, supports sustainability assessments, and strengthens the ability to monitor landscapes over time. Full article

The gradual integration of driverless vehicles into urban traffic systems is expected to affect both operational performance and environmental outcomes, particularly during the mixed-automation phase of urban traffic systems, in which human-driven and driverless vehicles coexist. However, existing studies have rarely examined this phase through jointly accounting for behavioral heterogeneity among human drivers and varying levels of driverless vehicle penetration in signalized urban networks. This study addresses this gap through a behavior-based microscopic traffic simulation framework that explicitly incorporates different human driving styles together with driverless vehicles across penetration levels ranging from 0% to 100%. Network- and link-level indicators, including delay, queue length, fuel consumption, and emissions, are evaluated under coordinated signal control conditions. The results reveal a nonlinear relationship between the automation level and traffic performance. While changes remain limited at low and moderate penetration levels, more pronounced improvements emerge beyond a critical threshold of approximately 75% driverless vehicle penetration. At this level, network-wide average delay reductions of about 3–5% are observed, accompanied by consistent decreases in fuel consumption and emissions. By highlighting how behavioral interactions shape the effectiveness of automation, the findings provide practical insights for traffic engineers and urban planners, supporting the design and evaluation of signalized urban arterials under mixed traffic conditions while contributing to environmental sustainability and sustainable urban mobility through improved traffic efficiency and stability. Full article

Spatial accessibility to healthcare services is commonly determined by three core components: demand, supply, and traffic speed. Although understanding which factors contribute to accessibility changes can help prioritize interventions to enhance accessibility in underserved areas, limited research has examined the extent of their individual contributions. To better capture the local dynamics that shape healthcare accessibility, this study decomposes spatial accessibility to primary healthcare services using the chain substitution method (CSM), which quantifies the impact of each component by substituting them one by one. By examining how the order of factor substitution affects the relative impact of each factor on spatial accessibility, we analyzed the importance of substitution order in the CSM. This study found that the order of factor substitution plays a significant role in measuring the relative contribution of each factor. To mitigate the effects of substitution order, we proposed an averaging CSM that uses the average value across all possible substitution combinations. Based on the averaging CSM, our findings offer insight for healthcare policymakers and urban planners by clarifying how demand, supply, and traffic speed interact in determining accessibility, ultimately supporting targeted interventions in underserved areas. Full article

Access to large-scale public transport journey data is essential for analysing accessibility, equity, and urban mobility. Although digital platforms such as Google Maps provide detailed routing for individual users, their licensing and access restrictions prevent systematic data extraction for research purposes. Open-source routing engines such as OpenTripPlanner offer a transparent alternative, but are often limited to local or technical deployments that restrict broader use. This study evaluates the feasibility of deploying a publicly accessible, open-source routing platform based on OpenTripPlanner to support large-scale public transport route simulation across multiple cities. Using Australian metropolitan areas as a case study, the platform integrates GTFS and OpenStreetMap data to enable repeatable journey queries through a web interface, an API, and bulk processing tools. Across eight metropolitan regions, the system achieved itinerary coverage above 90 percent and sustained approximately 3000 routing requests per minute under concurrent access. These results demonstrate that open-source routing infrastructure can support reliable, large-scale route simulation using open data. Beyond performance, the platform enables public transport accessibility studies that are not feasible with proprietary routing services, supporting reproducible research, transparent decision-making, and evidence-based transport planning across diverse urban contexts. Full article

Spatial equity of healthcare services is a critical concern in social equity and spatial justice research. Despite the availability of various methods to measure this equity, few studies have integrated the supply–demand coupling perspective with the analysis of impacts of residents’ travel behaviors’ on equity. This study develops and applies a Travel Behavior-based Coupling Coordination Degree (TB-CCD) method to assess the spatial equity of healthcare services in the Xi’an region. The results show the following: (1) Traditional single-mode models may fail to accurately assess this equity, whereas the TB-CCD model provides a more realistic evaluation. (2) Public transportation and driving provide a more equitable distribution of healthcare services compared to walking and cycling modes. The spatial equity of healthcare services exhibits a distinct core–periphery pattern, where accessibility and equity levels are significantly higher in city centers than in suburban areas. (3) The distribution of inequity ‘deserts’ and ‘oases’ in healthcare services is found to be travel-mode dependent, with the walking and public transportation modes exhibiting the highest incidence of these classifications. These findings provide valuable insights for urban planners and policymakers to formulate strategies and spatial plans aimed at enhancing equity in healthcare services. Full article

Emerging multi-unmanned aerial vehicle (multi-UAV) applications in smart cities must navigate cluttered airspace while meeting tight mobile edge computing (MEC) deadlines. Classical grid planners, including A-star (A*), D-star Lite (D* Lite), and conflict-based search with D-star Lite (CBS-D*) and metaheuristics such asparticle swarm optimization (PSO), either replan too slowly in dynamic scenes or waste energy on long detours. This paper presents PPO-adjusted incremental refinement (PAIR), a decentralized hybrid planner that couples an A* global backbone with a continuous PPO refinement module for multi-UAV navigation on two-dimensional (2-D) urban grids. A* produces feasible waypoint routes, while a shared risk-aware PPO policy applies local offsets from a compact state encoding. MEC tasks are allocated by a separate heterogeneous scheduler; PPO optimizes geometric objectives (path length, risk, and a normalized propulsion-energy surrogate). Across nine benchmark scenarios with static and Markovian dynamic obstacles, PAIR achieves 100% mission success (matching the strongest baselines) while delivering the best energy surrogate (104.9 normalized units) and shortest mean travel time (207.8 s) on a reproducible $100\times 100$ grid at fixed UAV speed. Relative to the strongest non-learning baseline (PSO), PAIR reduces energy by about 4% and travel time by about 3%, and yields roughly 10–20% gains over the remaining planners. An obstacle-density sweep with 5–30 moving obstacles further shows that PAIR maintains shorter paths and the lowest cumulative replanning time, supporting real-time multi-UAV navigation in dynamic urban MEC environments. Full article

Effective disaster management depends on rapidly understanding earthquake damage, yet traditional methods struggle to operate at scale and rely on expert inspections that become difficult when access is limited or time is critical. Satellite-based damage detection also faces limitations, particularly under adverse weather conditions and delays associated with satellite overpass schedules. This study introduces a machine learning-based approach to assess post-earthquake building damage using real observations collected after the event. The aim is to develop fast and reliable estimation techniques that can be deployed immediately after the mainshock by integrating structural, seismic, and geographic data. Three machine learning models—Random Forest, Histogram Gradient Boosting, and Bagging Classifier—are evaluated across both reinforced concrete and masonry buildings and across multiple spatial levels, including building, district, and city scales. Damage is categorized using practical three-class (traffic light) and detailed four-class systems. The models generally perform better in simpler classifications, with the Bagging Classifier offering the most consistent results across different scales. Although detecting severely damaged buildings remains challenging in some cases, the three-class system proves especially effective for supporting rapid decision-making during emergency response. Overall, this study demonstrates how machine learning can provide faster, scalable, and practical earthquake damage assessments that benefit emergency teams and urban planners. Full article

With the rapid growth of the social economy and the increasing prevalence of e-commerce, urban distribution centers (UDCs) have become vital hubs for the efficient functioning of cities. The decision regarding the location of UDCs not only impacts the operational efficiency of logistics companies but also plays a crucial role in urban sustainable development planning. Traditional location models are limited in addressing these complexities, which is why this paper introduces an innovative multi-objective location decision-making model. This model accounts for both the construction and operational costs of enterprises, and it uniquely incorporates the industrial sustainable development potential (ISDP) as a core objective function. The goal is to balance enterprise costs with the needs of urban development in location decision-making. This research adopts an interdisciplinary approach, initially using ecological theories to quantify ISDP, then employing System Dynamics to simulate the future trajectories of key industry drivers, and finally applying genetic algorithms to find solutions. The results from the numerical example demonstrate that the model and algorithm are both effective and practical. This research presents a novel approach and method for UDC location decision-making based on the long-term sustainable development of cities for logistics enterprises and urban planners. It also contributes to the related research on urban sustainable development and logistics location. Full article

This paper examines the growing field of AI-assisted urban planning within the context of sustainable urban development, with a particular focus on spatial optimization of urban green spaces under conditions of scarcity, density, and economic pressure. While the economic, ecological, and social values of UGS are widely acknowledged, urban planners lack a cohesive, data-driven framework to quantify and spatially optimize these often-conflicting values for effective land-use optimization. To address this gap, we propose a methodology that combines Geographic Information Systems (GISs), the Analytic Hierarchy Process (AHP), and an Artificial Intelligence-Based Genetic Algorithm (AI-GA). Vračar was chosen as the case study area. Our approach evaluates (1) the economic value of UGS through housing prices; (2) the ecological value through UGS density; and (3) the social value by measuring access to urban green pockets. The integrated method simulates environmental scenarios and optimizes UGS placement for resilient urban areas. Results demonstrate that properties in mixed-use green areas proximate to urban parks have the highest economic and social value. Additionally, higher densities of UGS correlate with higher housing prices, highlighting the economic impact of green space distribution. The methodology enables planners to make decisions based on evidence that integrates statistical modeling, expert judgment, and artificial intelligence into one cohesive platform. Full article

Cultural ecosystem services are intangible benefits people gain from ecosystems that enhance well-being. However, the Millennium Ecosystem Assessment indicates that about 70% of cultural ecosystem services are degraded or unsustainably used. To mitigate this decline, many regions and policies promote the assessment and mapping of cultural ecosystem services. Since 2005, related research and publications have increased, yet place-based cultural ecosystem services assessments remain limited. This study aims to clarify key aspects of cultural ecosystem services assessment, including categories, methods, and case study area types. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses method, this study systematically reviewed 163 articles on place-based cultural ecosystem services assessment from Web of Science and Scopus from 2010 to September 2024. The results show diverse ecosystem types, assessment categories, and methods, with urban ecosystems most frequently studied. Fourteen cultural ecosystem service categories were identified based on term definitions and relevance. Non-monetary methods, such as questionnaires and social media data, were most commonly applied. Future research trends will focus on spatial visualization and mapping of supply and demand of cultural ecosystem services, emphasizing public perception. These findings provide planners and decision-makers with more detailed and specific information to better manage, design, and develop regions in a sustainable and culturally sensitive way. Full article

Flooding is a significant natural hazard in arid regions, particularly in Saudi Arabia, where intense rainfall events pose serious risks to both infrastructure and public safety. Bahrah City, situated between Jeddah and Makkah, has experienced recurrent flooding owing to its topography, rapid urbanization, and inadequate drainage systems. This study aims to develop a comprehensive flood hazard mapping approach for Bahrah City by integrating remote sensing data, Geographic Information Systems (GISs), and Multi-Criteria Decision Analysis (MCDA). Key input factors included the Digital Elevation Model (DEM), slope, distance from streams, and land use/land cover (LULC). The Analytical Hierarchy Process (AHP) was applied to assign relative weights to these factors, which were then combined with fuzzy membership values through fuzzy overlay analysis to generate a flood susceptibility map categorized into five levels. According to the AHP analysis, the high-susceptibility zone covers 2.2 km², indicating areas highly vulnerable to flooding, whereas the moderate-susceptibility zone spans 26.1 km², representing areas prone to occasional flooding, but with lower severity. The low-susceptibility zone, covering the largest area (44.7 km ²), corresponds to regions with a lower likelihood of significant flooding. Additionally, hydraulic simulations using the rain-on-grid (RoG) method in HEC-RAS were conducted to validate the hazard assessment by identifying inundation depths. Both the AHP analysis and the RoG flood hazard maps consistently identify the western part of Bahrah City as the high-susceptibility zone, reinforcing the reliability and complementarity of both models. These findings provide critical insights for urban planners and policymakers to improve flood hazard mitigation and strengthen resilience to future flood events. Full article

The increasing demand for active and light mobility (including bicycles, e-bikes and e-scooters) has become a key driver of sustainable urban transport, calling for a renewed approach to urban planning. A central challenge is redesigning infrastructure around users’ needs, inspired by the “15-min city” concept developed by Carlos Moreno. However, the existing literature on user preferences in this domain remains fragmented, both methodologically and thematically, and often lacks integration of user behaviour analysis. This paper presents a structured review of recent international studies on factors influencing route and infrastructure choices in active and light mobility. The findings are organized into an analytical framework based on five macro-criteria: external and infrastructural factors, transport mode, user typology, experimental methodology and infrastructure attributes. The synthesis tables aim to summarize the findings to guide planners, researchers and decision-makers towards more inclusive, adaptable and effective mobility systems, through the development of user-oriented planning tools, attractiveness indexes and strategies for cycling and micromobility networks. Moreover, the review contributes to an ongoing national research initiative and lays the groundwork for developing decision-making tools, attractiveness indexes and route recommendation systems. Full article