Urban Model Research Articles

Resilient Urban Flood Management: A Multi-Objective Assessment of Mitigation Strategies

This study employs a comprehensive multi-objective efficiency index (EI) to assess urban flood mitigation strategies. The EI enables the simple interpretation of a mitigation strategy’s efficiency with a value range between −1 (low efficiency) and 1 (high efficiency), which represents a practical communication tool for decision makers, engineers, and researchers. This was tested at the study site of Feldbach (Austria) with an integrated 1D–2D urban flood model and a distributed hydrological model. A total of 112 scenarios were analysed for six mitigation strategies, which were built from three future challenge scenarios, two observed heavy storm events, and two hydrological pre-conditions. For the given study site, the analysis identifies mitigation strategies implemented in rural boundary areas as the most effective. A novel aspect of this study is the consideration of the urban water balance change, highlighting its impact on the EI. The analysis highlights the importance of analysing each relevant process separately to determine the EI in order to understand why a mitigation strategy is more or less efficient.

Deep Learning based Semantic Segmentation for Buildings Detection from Remote Sensing Images

Building extraction from remote sensing images is the process of automatically identifying and extracting the boundaries of buildings from high-resolution aerial or satellite images. The extracted building footprints can be used for a variety of applications, such as urban planning, disaster management, city development, land management, environmental monitoring, and 3D modeling. The results of building extraction from remote sensing images depend on several factors, such as the quality and resolution of the image and the choice of algorithm.The process of building extraction from remote sensing images typically involves a series of steps, including image pre-processing, feature extraction, and classification. Building extraction from remote sensing images can be challenging due to factors such as varying building sizes and shapes, shadows, and occlusions. However, recent advances in deep learning and computer vision techniques have led to significant improvements in the accuracy and efficiency of building extraction methods. This research presents a deep learning semantic segmentation architecture-based model for developing building detection from high resolution remote sensing images. The open-source Massachusetts dataset is used to train the suggested UNet architecture. The model is optimized using the RMSProp algorithm with a learning rate of 0.0001 for 100 epochs. After 1.52 hours of training on Google Colab the model achieved an 83.55% F1 score, which indicates strong precision and recall.

Revisiting China’s Urban Transition from the Perspective of Urbanisation: A Critical Review and Analysis

Amidst global sustainability challenges across domains such as energy, water, and transportation, China’s urbanisation process presents unique socio-economic dynamics, particularly following the Reform and Opening Up policy. The existing literature has not fully captured the complex interplay between urban growth and sustainability challenges in China, nor has it adequately explored the phased nature of urbanisation informed by evolving policies. This paper provides a comprehensive review of China’s urban transition, emphasising its evolution since the Reform and Opening Up policy. Through systematic analysis, it delineates the multifaceted nature of urbanisation, reflecting on policy impacts and socio-economic shifts. Findings indicate transformative urban growth characterised by new challenges in environmental sustainability, social equity, and urban–rural integration. The research unveils the transition to a human-centric urban development model, highlighting policy shifts towards sustainable practices. The results suggest a nuanced urban–rural convergence, advocating for participatory governance and balanced growth. The unique contribution of this study lies in its empirical and theoretical examination of China’s urbanisation within a global context, offering a critical perspective on policy formulation and sustainable urban development.

Urban traffic-parking system dynamics model with macroscopic properties: a comparative study between Shanghai and Zurich

Analyzing the dynamics of parking traffic can better represent the real dynamic states of road networks, thereby allowing for a deeper analysis of the parking system’s impact. This paper comparatively investigates the impact of parking policies on two traffic networks with different infrastructure, socio-economic, and policy characteristics. Parking space, average parking duration, and parking fee policies were analyzed as a function of cruising distances and cruising time with indirect effects on traffic emissions. Empirically, the system dynamics model application is tested and validated with the macroscopic data from two central business districts (CBDs) in Shanghai (Xujiahui area) and Zurich (Bahnhofstrasse area). Results showed Bahnhofstrasse CBD is more sensitive against the policy shifts with relatively higher elasticity and indicated greater responsiveness in aggregating traffic emissions when compared with Xujiahui CBD. The findings of this study may provide an overall framework to empirically assess the performance of different traffic conditions and strategies on urban parking systems.

Simulating Future Exposure to Coastal Urban Flooding Using a Neural Network–Markov Model

Urbanization and climate change are two major challenges of the 21st century, and the effects of climate change, combined with the urbanization of coastal areas, increase the frequency of coastal flooding and the area exposed to it, resulting in increased risk of flooding and larger numbers of people and properties being vulnerable. An urban growth modeling system was used to simulate future growth scenarios along the coast of the Vendée region in western France, and the potential exposure to flooding with each scenario was evaluated. The model used was an Artificial Neural Network combined with a Markov Chain, using data obtained by the remote sensing and geographic information system techniques to predict three future urban growth scenarios: business as usual, environmental protection, and strategic urban planning. High-risk flood areas and future sea level projections from the Sixth Assessment Report of the Intergovernmental Panel on Climate Change were then used to assess future flood risk under each growth scenario in the study area. According to the results, the different growth scenarios are associated with different development patterns, and the strategic urban planning scenario significantly reduces the risk of flooding compared to the other two scenarios. However, the rise in sea level considerably expands the areas vulnerable to flooding. Finally, the methodology adopted can be used to prepare for the impact of climate change and develop strategies to mitigate the risk of flooding in the future.

Application of ensemble fuzzy weights of evidence-support vector machine (Fuzzy WofE-SVM) for urban flood modeling and coupled risk (CR) index for ward prioritization in NCT Delhi, India

Application of ensemble fuzzy weights of evidence-support vector machine (Fuzzy WofE-SVM) for urban flood modeling and coupled risk (CR) index for ward prioritization in NCT Delhi, India

Assessing the impact of heat mitigation measures on thermal performance and energy demand at the community level: A pathway toward designing net-zero energy communities

AbstractIn the context of escalating global energy demands, urban areas, specifically the building sector, contribute to the largest energy consumption, with urban overheating exacerbating this issue. Utilizing urban modelling for heat-mitigation and reduction of energy demand is crucial steps towards a sustainable built-environment, complementing onsite energy generation in the design and development of Net-zero Energy (NZE) Settlement, especially in the context of Australian weather conditions. Addressing a significant gap in existing literature, this study offers empirical analysis on the climate and energy efficacy of integrated heat mitigation strategies applied in 14 neighbourhood typologies located in Sydney, Australia. Examining the application of cool materials on roads, pavements, and rooftops, alongside urban vegetation enhancement, the analysis demonstrates scenario effectiveness on heat mitigation that leads to reduce ambient temperature and energy demands along with CO2 emissions within the neighbourhoods. Considering building arrangement, built-area ratio, building height, and locations, ENVI-met and CitySim are utilized to assess the heat-mitigation and the energy demand of neighbourhoods, respectively. Results indicate that mitigation measures can lead up to a 2.71 °C reduction in ambient temperature and over 25% reduction in Cooling Degree Hours, with a 34.34% reduction in cooling energy demand and overall energy savings of up to 12.49%. In addition, the annual energy-saving yields a CO2 reduction of approximately 141.12 tonnes, where additional vegetation further amplifies these reductions by enhancing CO2 absorption. This study showcases the pathway towards achieving NZE goals in climates similar to that of Australia, highlighting significant benefits in heat-mitigation, environmental impact, and energy-savings.

Development of an Integrated Urban Flood Model and Its Application in a Concave-Down Overpass Area

Urban floods caused by extreme rainstorm events have increased in recent decades, particularly in concave-down bridge zones. To simulate urban flooding processes accurately, an integrated urban flood model (IUFM) was constructed by coupling a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM), a widely used pipe convergence module in the storm water management model (SWMM), with an inundation module that describes the overflow expansion process associated with terrain and land-cover. The IUFM was used in a case study of the Anhua Bridge (a typical concave-down overpass) study area in Beijing, China. The spatial-temporal variations in flood depth modeled by the IUFM were verified to be reliable by comparison with actual measurements and other simulations. The validated IUFM was used to obtain temporal variations in flood range, depth, and volume under four rainstorm scenarios (return periods of 3-year, 10-year, 50-year, and 100-year). The results showed that the surface runoff process, overflow from drainage networks, and overflow expansion process could affect the flooding status by changing the composition and spatial configuration of pervious or impervious patches, drainage capacity, and underlying surface characteristics (such as terrain and land-cover). Overall, although the simulation results from the IUFM contain uncertainties from the model structures and inputs, the IUFM is an effective tool that can provide accurate and timely information to prevent and control urban flood disasters and provide decision-making support for long-term storm water management and sponge city construction.

Analysis and development of criteria for the operational efficiency of urban electric buses

Problem. Currently, manufacturers and research specialists primarily utilize the amount of autonomous mileage and/or the specific electricity consumption of autonomous DEEs per unit of mileage to describe the general characteristics of urban electric buses. However, the structural mass and the key operational parameter, the nominal passenger capacity, are often disregarded. Therefore, comparing electric buses of the same type with identical dimensional parameters based solely on these criteria is illogical and incorrect. Goal. This study aims to establish criteria for the operational efficiency of urban low-floor electric buses of various types, considering autonomous DEE and their charging or replacement systems. These criteria should account for structural mass and nominal passenger capacity, facilitating a proper comparative assessment and the selection of the most optimal options for use in urban passenger transportation systems. Methodology. Criteria for the operational efficiency of city electric buses of various types are proposed based on an analysis of electricity consumption required for both movement and the operation of heating, ventilation, and air conditioning systems. The criteria consider the mass in the equipped state and the main parameter - nominal passenger capacity. Results. Several operational efficiency criteria for urban electric buses, incorporating mass parameters and passenger capacity, are suggested. The feasibility of their application is demonstrated through a comparison of five sketch projects of electric buses of various types with modern models. Originality. The proposed criteria for the operational efficiency of urban electric buses offer a method for adequately comparing any selected models, regardless of their types and technical specifications. Practical value. The proposed criteria for the operational efficiency of urban electric buses of various types can be beneficial for transport company specialists in selecting suitable rolling stock for specific routes. Additionally, design bureau and scientific and technical center specialists can utilize these criteria during the design process of new urban electric transport models.

Biophysical Drivers of Seasonal Hysteresis of Urban Heat Islands Across Climates and Urban Landscapes

AbstractThe phenomenon of seasonal hysteresis between urban heat islands (UHIs) and background surface temperature was reported in many studies, but a unifying explanation for the causes of its magnitude, shape and looping direction remained less clear, and the impacts of urban landscapes and background climate on such hysteresis were also the subject of enquiry. Here we performed a systematic study based on a long‐term offline simulation for surface energy budgets, surface air and skin temperature of the urban‐biosphere system in East China. It was carried out with an up‐to‐date land surface model — the CLM5‐LCZs (the Community Land Model version 5 (CLM5) coupled with a newly developed urban canopy model representing diverse urban landscapes with the local climate zones classification scheme). To isolate the causes of UHIs hysteresis, a simplified framework was constructed by combining a mathematical representation of grid/subgrid model outputs, the two‐resistance mechanism method and the time‐lagged cross correlation analysis. The results exhibited distinct hysteresis patterns of UHIs such as twisted, concave‐down, concave‐up and convex‐up curve across diverse background climates and urban landscapes. The basic shapes of hysteresis depict a gradual transition from concave/convex‐up to concave/convex‐down by characterized by a larger winter UHIs from a wetter and warmer climate to a drier and colder climate. Magnitude and seasonality of surface UHIs are separately dominated by the urban‐rural contrast of evapotranspiration and heat storage at daytime and nighttime, whereas across‐climate variations of daytime and nighttime surface UHIs hysteresis are regulated by the phase shift of convection efficiency and anthropogenic heat. Urban landscapes affect the hysteresis mainly by altering the amplitude rather than the shape of looping curves. Our work could provide a fuller picture of how to mitigate urban warming considering inter‐seasonal tradeoffs over a broader region.

Analysis of Uncertainties and Associated Convective Processes in Simulations of Extreme Precipitation Over Cities With a Regional Earth System Model: A Case Study

AbstractThis study utilizes the Weather Research and Forecasting model coupled with an atmospheric chemistry model, a multi‐layer urban canopy model (UCM), and a building energy model to simulate the extreme rainfall event influencing the Guangzhou city in South China on 7 May 2017. By employing small variations in the longwave emissivity of buildings within the UCM, 11 convective‐permitting experiments are conducted. The maximal 18‐hr and hourly rainfall accumulation vary from a 2‐year return period to as high as a 20 or 40‐year return period with notable spatial differences. Comparisons between the more accurate group (GOOD) and less accurate group (POOR) simulations highlight that some minor differences in the near‐surface air thermodynamic conditions in urban area could lead to substantial differences in local convection and its impacts on subsequent convective systems. With persistent transportation of warm, moist airflows from the northern South China Sea, formation of a slow‐moving mesoscale outflow boundary to the north of the urban agglomeration leads to the development of a quasi‐stationary, compactly structured meso‐γ‐scale rainstorm in the GOOD simulations. The stronger low‐level to near‐surface convergence and mid‐level cyclonic shear within this system substantially enhance low‐level updrafts, leading to increased microphysical production and stronger horizontal advection of rainwater within the system. These findings offer some process‐based understanding about the uncertainties in simulating urban extreme rainfall and underscore the need to develop ensemble forecasting methods for convection‐permitting numerical models that incorporate increasingly complicated representations of anthropogenic influences.

Evaluation of urban ecological security model based on GIS sensing and MCR model

Urban ecological security is not only a basic safety requirement for human security and development, but also an important part of national security and other security and infrastructure carriers. Protecting the environment is an important factor in the construction of China's natural civilization. In recent years, with the deterioration of the environment, the region and its people have paid more and more attention to the safety of urban ecological environment, which has become one of the hottest issues in current research. Land use/cover rate change (LUCC) affects the environment, which will also affect environmental safety, and changes in its appearance will also affect nature and the environment, providing a positive impetus for the control of natural processes. In this paper, researches on the above problems in City K and Zhengzhou are carried out, using MSPA and visual image forms to better connect local resources, and through the MCR model based on ecological control, a weight-based multi-structure network model is established, which is conducive to the establishment of a multi-structure network model based on ecological control. Build a city of environmental science network. The GIS control line was explored, and the concept of the control line was filled to a certain extent. At the same time, taking City K and Zhengzhou as examples, determining the breadth of their layout and management, as well as the control strategy of their environmental control lines, provides a specific reference angle for the layout and research of other cities. Urban ecological security is very important to each of us living in cities. It is a difficult problem at this stage to establish an ecological security model to open a new security pattern.

Participatory Framework for Urban Pluvial Flood Modeling in the Digital Twin Era

The recent advancement in digital twin technology, which creates virtual replicas of real-world processes, offers an interactive testbed for understanding and predicting environmental changes. As pluvial flood damage escalates globally in urban areas, there remains a gap in understanding the most effective collaboration between governments and local residents for sustainable flood risk management. To address this gap, we develop a participatory framework for urban pluvial flood modeling, incorporating open source software, virtual reality, minimum viable product, and gamification components. This framework engages citizens in every phase of the participatory modeling process, from input data preparation, through hydrological model construction, to model verification, and experiments. We present a case study on the recurring pluvial flood damages in South Korea's Gangnam region, demonstrating the practical implications of an interactive, web-based crowdsourcing platform to leverage community engagement and local knowledge. The results underscore the evidence of a proactive role for citizens, not merely as recipients of disaster information but as key contributors collaborating with a range of stakeholders in stormwater management and modeling. Combining digital twin technology with citizen participation can empower informed decision-making and collective actions in the evolving digital era, leading to a disaster-resilient community.

“Airport city” or “VIP” urbanism? Questioning the market-led land development strategies of airports

Airport authorities are gradually shaping urban spaces through their property development: hotels, business parks, conference centers, etc. This article challenges these development strategies pursued by increasingly financialized and privatized airports in the name of “airport city” policies. It shows that the changes underway are a vivid expression of a VIP model of urbanism, in which the airport authority, maximizing its revenues and land value, naturalizes its emphasis on high-end real-estate projects, and trivializing their social reach. The analysis draws on the case of the Paris city region, characterized by land scarcity and housing issues, and its airport authority, Aéroports de Paris (ADP), one of the largest landowners in the region. Using documents from ADP, a press corpus, and interviews, we highlight how the distinctive geography at play in air terminals changes scale by being projected onto real-estate “diversification” projects, as ADP opts for urbanization centered around the upper fractions of the flying public. This market-led development leads to a form of elite capture that seeks to dwarf or endogenize other existing and potential uses and users of airport land. This article further deconstructs this urban model by shedding light on the multiple tensions it generates and pleads for a more critical debate on airport land uses and planning.

Modelling urban canopy with object-based porosity model

We present a novel approach to modelling urban canopies based on the porosity model. The impact of objects in the canopy layer is implicitly accounted for by applying a high-value drag force in the parts of the canopy layer that objects would occupy. This effectively halts fluid flow in the defined space of the objects, mimicking the impact of solid objects in the canopy layer. The model addresses two common issues in urban canopy modelling: the definition of the drag coefficient and the loss of canopy morphology, which significantly affect flow and turbulence fields. The model is assessed for three cases: two idealised urban-like canopies with realistic urban flow conditions and one benchmark case based on the measurements of Hilderman and Chong (2007). The benchmark results, where the explicitly modelled objects are reduced from 109 to 9, show that the new model exhibits a level of accuracy similar to explicit modelling. We also computed the commonly used zone-based porosity and roughness models for the two idealised urban-like canopies. The new model produced results comparable to the results of explicit modelling and superior to those of the alternative approaches. All cases were computed in unsteady mode using a hybrid RANS/LES model based on elliptic-relaxation eddy-viscosity RANS and the dynamic Smagorinsky model. The simulations were performed using the open-source computational fluid dynamics code T-Flows, available on Github.

Enhancing accuracy of surface wind sensors in wind tunnel Testing: A Physics-Guided neural network calibration approach

Irwin’s surface wind sensor is widely used in wind tunnel testing for urban and environmental aerodynamics studies. However, the conventional physics-based calibration of this sensor could result in reduced measurement accuracy in regions with low flow velocities and high turbulence intensity. To address this issue, this study proposes a novel physics-guided neural network (PGNN) calibration approach, which couples a physics-based calibration model, derived from extended Taylor series expansions of measured wind speed, with an adaptive, data-driven general regression neural network. Sensors are calibrated within the turbulent boundary layer of an empty flat plate, considering both mean and standard deviation of wind velocity measured by high-accuracy thermal anemometry. The accuracy of calibrated sensors is then assessed using a 1:400 benchmark urban model. Experimental results show significant improvement in measurement accuracy, reducing mean absolute percentage error for wind speed standard deviation from 92.3 % with the current model to 9.8 % using PGNN.

Walking in Tandem with the City: Exploring the Influence of Public Art on Encouraging Urban Pedestrianism within the 15-Minute Community Living Circle in Shanghai

As a new urban model, the 15-min city has gradually become a touchstone with which to measure the future sustainability of cities. With a time-limited planning of urban living circles, urban residents can be allowed to access basic daily needs, such as food, health and education, while walking or cycling, thus reducing motor traffic and carbon dioxide emissions and contributing to the improvement of people’s well-being and the environmental climate. Within the temporal and spatial confines of the 15-min living sphere, governmental authorities and community bodies commonly integrate public art installations into public spaces to enrich spatial dynamics, cultivate cultural identities, enhance environmental aesthetics, elevate service quality, and foster communal interactions. This study aims to probe into the impact of public art on encouraging urban pedestrianism within the specific context of the 15-min community living sphere along the Suzhou River in northern Shanghai. Drawing upon Stimulus–Organism–Response (SOR) theory, a theoretical framework is constructed to unravel the mechanisms by which public art influences residents’ propensity for walking, encompassing the attributes of public art, perceived value, and walking intention. Employing Confirmatory Factor Analysis (CFA), the model is analyzed to scrutinize the proposed hypotheses. Through this research, we establish and substantiate a novel and pertinent theoretical perspective for advancing human-centric and sustainable urban regeneration. The findings underscore that integrating public art within the framework of constructing 15-min community living spheres contributes to catalyzing proactive urban pedestrianism by enhancing its value proposition.

Unlock city-scale energy saving and peak load shaving potential of green roofs by GIS-informed urban building energy modelling

Urban green roofs have emerged as a significant trend in urban architecture worldwide, offering numerous benefits, including enhanced energy performance, improved urban microclimate, and public health. This study proposes a holistic framework for assessing the green roofs' energy-saving potential at the city scale, achieved by scaling up building-scale energy simulations to city-scale energy demands. Firstly, massive building information is collected using Geometric Information System (GIS) technologies. Subsequently, prototype buildings are generated to accurately represent the geometric characteristics of buildings at the city scale. Building performance simulation is further conducted considering three types of plants on roofs. A case study in Xiamen, China, demonstrates the effectiveness of the proposed framework to efficiently quantify the city-scale energy-saving potential of green roofs. By implementing green roofs in Xiamen, energy savings of 1.62–1.83% and peak load shaving of 1.10–1.63% can be achieved for the whole city. Overall, the proposed framework has the potential for widespread application in other cities with minor adjustments to accommodate variations in climate and building parameters.

A novel acceleration approach to shadow calculation based on sunlight channel for urban building energy modeling

Shadow effect among buildings has great impacts on the building energy consumption and the performance of building photovoltaic, and calculating shadows on building surfaces in urban building energy modeling (UBEM) faces challenges of inefficiency, especially for dense urban areas. In this study, a novel shadow calculation approach based on sunlight channel is proposed that can streamline the surrounding environment and accelerate the shadow calculation process. The sunlight-channel algorithm can further accelerate the shadow calculation process by dynamically predetermining the shading surfaces according to the actual solar position. In a real urban context, the proposed approach can accelerate the computation process by over 10 times over the baseline and over 34 times over the non-accelerated method, with a mean absolute percentage error (MAPE) of 1.13% for the total solar radiation. The proposed approach copes well with both large-scale urban models and the complexity of building structures, particularly for urban models with complex changes in building heights. This approach can significantly enhance the computational efficiency in complex urban environments, facilitating an accurate and rapid analysis of the energy consumption and solar potential of buildings in dense cities.

Evaluation of the number of events’ influence on model performance and uncertainty in urban data-scarce areas based on behavioral parameter ranking method

Rainfall-runoff data in drainage systems in urban areas is the essential input variable for urban hydrological modeling. Obtaining high quality and sufficient size of urban flood events is always difficult due to the inconvenient underground observation and the untimely capture of the rapid rising and recession stages of urban runoff. It largely constrains the efficiency of model calibration and brings large uncertainty in urban rainfall-runoff simulation. Therefore, the evaluation of the number of events’ influence on model performance and uncertainty is of great significance, which can provide useful information to help the decision makers select sufficient useful observation data for model calibration with relatively fewer events. In this study, we constructed a comprehensive method of behavioral parameter ranking of multi-events (BPROME) based on coupling the Generalized Likelihood Uncertainty Estimation (GLUE) algorithm and the Storm Water Management Model (SWMM). The results in the two small demonstration areas (named Case #A and Case #B) in Shenzhen city of China proved the good performance of the BPROME in uncertainty assessment in urban areas. We found the model uncertainty (average bandwidth (B) and average deviation amplitude (D)) and model performance (containing Ratio (CR) and the maximum value of behavioral samples’ NSE (NSEmax)) became stable at a certain number of events in both two case areas. The B and D decrease and the CR and NSEmax increase as the number of event increases. In particular, the model performance and uncertainty reach their appropriate state concerning the limited observations at a certain range of numbers of events (both three to five in our two case areas). Our results demonstrate the potential influence of the numbers of events for the urban rainfall-runoff modeling calibration which can balance the model efficiency and data collection cost (the number of input rainfall-runoff data). The findings could help decision-makers seek a trade-off between data investment and acceptable model performance.