City Model Research Articles

HYBRID MODEL AND FRAMEWORK FOR PREDICTING AIR POLLUTANTS IN SMART CITIES

The pollution index of any urban area is indicated by its air quality. It also shows a fine balance is maintained between the needs of the populace and the industrial ecosystem. To mitigate such pollution in real-time, smart cities have a significant role to play. It's common knowledge that air pollution in a city severely affects the health of its dependents. More alarmingly, human health damage and disease burden are caused by phenomena like acid rain, and global warming. More precisely, lung ailments, CPOD, heart problems and skin cancer are caused by polluted air in congested urban places. Amongst the worst air pollutants, CO, C6H6, SO2, NO2, O3, RSPM/PM10, and PM2.5 cause maximum havoc. The climatic variables like atmospheric wind velocity, direction, relative humidity, and temperature control air contaminants in the air. Lately, numerous techniques have been applied by researchers and environmentalists to determine the Air Quality Index over a place. However, not a single technique has found acceptance from all quarters as being effective in every situation or scenario. Here, the main aspect relates to achieving authentic prediction in AQI levels by applying Machine Learning algorithms so worst situations can be averted by timely action. To enhance the performance of Machine Learning methods study adopted imputation and feature selection methods. When feature selection is applied, the experimental outcomes indicate a more accurate prediction over other techniques, showing promise for the application of the model in smart cities by syncing data from different monitoring stations.

Construction of urban low-carbon development and sustainable evaluation system based on the internet of things

Low-carbon (LC) cities are the cities that people long for today. LC environmental protection plays a very important role in people's health. The construction of a city in LC is a great cause that contributes to the present and benefits the future. In this study, we propose a development and sustainability evaluation system for building low-carbon cities based on the Internet of Things (IoT). The study is novel in that it considers key areas such as urban planning, environmental issues and solutions, and how the Internet of Things can optimize low-carbon logistics and smart grids, with the aim of promoting the formation of low-carbon city models. This comprehensive approach not only presents problems and solutions in low-carbon urban planning but also focuses on how the Internet of Things can be used as a key technology to promote low-carbon urban development. The urban development of LC was constructed with a sustainable evaluation system, so that people could experience the life of LC. Through the investigation of the degree of atmospheric pollution of LC cities using the Internet of Things, this article found that the highest degree of atmospheric pollution was 30. The highest degree of atmospheric pollution in cities in LC without IoT was 53. The severity of water pollution in cities in LC using IoT technology ranged from 10 to 25, while those without IoT ranged from 30 to 60. The degree of soil pollution in LC cities using IoT technology was concentrated in 10–30, while those without using IoT were concentrated in 30–50. Through these experimental data, it could be seen that IoT technology could reduce environmental pollution, thus achieving the effect of LC cities. This shows that the use of IoT technology in LC cities was highly feasible.

Maximization of equilibrium utility vs minimization of resources in the urban equilibrium

We study welfare and the role of absentee landlords’ rent capture in one of the building blocks of urban economics: the monocentric city model. If only a fraction (but not all) of the absentee landlords’ land rents is captured and then redistributed, the market outcome minimizes resource usage but does not maximize residents’ utility. Therefore, when the utility-maximizing planner is unable to implement full taxation of land rents, she would prefer to intervene in the market outcome, while a planner minimizing resources would not. This difference implies that policy prescriptions crucially depend on the chosen welfare function (utility vs resources) and the land rent taxation rate.

Research on the Urban Renewal Planning from the Perspective of Suturing: A Case Study of the Ancient City Suturing Plan in Luoyang, Henan Province

With the acceleration of urbanization in China, historical and cultural districts in ancient cities have experienced rapid development under the promotion of various policies, gradually forming a homogenized development model for ancient cities. The rapid development has brought about environmental pollution and distortion in the protection of ancient city culture, making the contradiction between ancient city protection and renewal planning increasingly prominent. This study extends the theory and techniques of spatial suturing to the macro scale of historical districts in ancient cities, exploring the suturing effects and practical applications of ancient city design planning at the level of street and alley spaces and historical contexts. It aims to promote the sustainable development of historical and cultural ancient cities, and achieve the coordinated development of cultural heritage protection and socio-economic progress.

Positioning the Creative City Agenda Within Urban Policy Discourse: The Malaysian Scenario

As the hype of the creative city model transcends Western borders to Eastern settings, Malaysian cities are fast embracing it as sine qua non urban development strategy. The global creative city model, however, is fraught with conceptual and operational ambiguities when dissecting the notions of “cultural industries” and “creative industries”. The “knowledge economy-based concept of creative industries” is widely critiqued as being void of cultural content. Malaysia is also caught in this conundrum due to national aspirations to be a knowledge and creative economy. The aim of this article is to examine how the Malaysian creative city agenda is positioned within urban policy discourse as the nation’s structural base shifts from industrial to services and the creative economy. Based on qualitative research techniques (i.e. interviews, focus group discussions, fieldwork observations), primary data was collected in selected Malaysian cities from 2019 to 2021. The study indicated that the creative city strategy is still nascent in Malaysia and not systematically integrated and intersected across urban policy domains. This article recommends the need to integrate the elements of culture and creativity more distinctively across all urban policy domains in the quest to develop culturally sensitive and sustainable Malaysian creative cities.

How to Create Healthy, Stress-Resilient Post-Pandemic Cities

This article analyzes the correlation between the city size, population density and character of three Polish cities and the following aspects: (1) inhabitants’ subjective stress level and its change during the COVID-19 pandemic, (2) sources and effects of their stress during the pandemic, (3) ways of reducing stress by residents and the role of greenery in this respect and (4) directions of changes in the structure of post-pandemic cities desired by their residents. The main research method was geo-surveys, the results of which were developed using statistical analysis and GIS methods. On the basis of the constructed scale, respondents subjectively assessed the level of experienced stress and its change during the COVID-19 pandemic. The research shows that none of the considered features of cities (including size measured by the number of inhabitants) are related to the level of pandemic stress of their inhabitants. All of them, on the other hand, are correlated with the directions of urban spatial development desired by the residents, conducive to stress reduction. Furthermore, the size of the city and its character affect the subjectively perceived change in stress levels during the pandemic (compared to the stress level before pandemic). Some of the effects of pandemic stress and ways of reducing it depend on the character of the city. The stress experienced by the inhabitants is the lowest in cities with the largest share of green areas in the spatial structure (especially with forest complexes and naturally shaped areas of river valleys), where an effective system of greenery has been adopted (the Howard’s garden city model), and at the same time with dominant single-family housing. The research results were used to indicate new directions for shaping post-pandemic cities that are more resistant to stress and thus healthier for their residents.

Enhancing smart city operation management: Integrating energy systems with a subway synergism hub

This paper is centered on establishing a secure framework for the optimal concurrent operation of a smart city, encompassing transportation, water, heat, electrical, and cooling energy systems. The studied smart city includes the microgrid, smart transportation system (STS), energy hub (EH) and smart grid. In this regard, a subway synergism hub (SSH) as a new non-energy system is added to the smart city with the aim of serving the subway's water, heat, electrical and cooling demands as well as diminishing the operation cost of the smart city. The EH within the SSH cooperated with a desalination unit is considered to supply the subway's stations water demand by using the sea water. The investigation of the optimal allocation of the SSH unit for reducing the cost of smart city operation is also conducted by introducing a novel intelligent priority selection (IPS) analytical algorithm. In comparison to common meta-heuristic algorithms for allocation problems, the accurate optimal solution can be found in low runtime by the IPS algorithm. To achieve an accurate model of the smart city, directed acyclic graph (DAG) based blockchain approach is provided which can enhance the data and energy exchanges security within the smart city. This research paper introduces a security framework deployed in a smart city setting to establish a secure platform for energy transactions. The findings validate the effectiveness of this model and highlight the value of the IPS method. The effectiveness of the suggested approach has been assessed using the smart city system is comprised of various sections, including EVs, smart grid, microgrid, and SSH, demonstrating the credibility and accuracy of this study.

Integrating 3D city data through knowledge graphs

ABSTRACT CityGML is a widely adopted standard for representing and exchanging 3D city models. The representation of semantic and topological properties in CityGML makes it possible to query such 3D city data for analysis in various applications. Nevertheless, the potential of querying CityGML data has not been fully exploited. The official GML encoding of CityGML is mainly an information model used for data storage and exchange, but not suitable for performing complex queries. The most common way of dealing with CityGML data is to store them as tables in the 3DCityDB system. However, it remains a challenging task for end users to formulate SQL queries over 3DCityDB directly for their ad-hoc analytical tasks because of the gap between the semantics of CityGML and the relational schema adopted in 3DCityDB. The technology of Knowledge Graphs (KGs), where an ontology is at the core, is a good solution to bridge such a gap. Moreover, embracing KGs makes it easier to integrate with other spatial data sources, e.g. OpenStreetMap, and to perform queries combining information from multiple data sources. In this work, we describe a CityGML-KG framework to expose the CityGML data in 3DCityDB as a KG. To evaluate our approach, we use CityGML data from the city of Munich as a test area and integrate OpenStreetMap data.

Automatic Building Roof Plane Extraction in Urban Environments for 3D City Modelling Using Remote Sensing Data

Delineating and modelling building roof plane structures is an active research direction in urban-related studies, as understanding roof structure provides essential information for generating highly detailed 3D building models. Traditional deep-learning models have been the main focus of most recent research endeavors aiming to extract pixel-based building roof plane areas from remote-sensing imagery. However, significant challenges arise, such as delineating complex roof boundaries and invisible boundaries. Additionally, challenges during the post-processing phase, where pixel-based building roof plane maps are vectorized, often result in polygons with irregular shapes. In order to address this issue, this study explores a state-of-the-art method for planar graph reconstruction applied to building roof plane extraction. We propose a framework for reconstructing regularized building roof plane structures using aerial imagery and cadastral information. Our framework employs a holistic edge classification architecture based on an attention-based neural network to detect corners and edges between them from aerial imagery. Our experiments focused on three distinct study areas characterized by different roof structure topologies: the Stadsveld–‘t Zwering neighborhood and Oude Markt area, located in Enschede, The Netherlands, and the Lozenets district in Sofia, Bulgaria. The outcomes of our experiments revealed that a model trained with a combined dataset of two different study areas demonstrated a superior performance, capable of delineating edges obscured by shadows or canopy. Our experiment in the Oude Markt area resulted in building roof plane delineation with an F-score value of 0.43 when the model trained on the combined dataset was used. In comparison, the model trained only on the Stadsveld–‘t Zwering dataset achieved an F-score value of 0.37, and the model trained only on the Lozenets dataset achieved an F-score value of 0.32. The results from the developed approach are promising and can be used for 3D city modelling in different urban settings.

Factors influencing the content of Smart City initiatives(particularly based on institutional theories of organization)

Objectives This paper aims to consider the factors shaping the smart city structures from the perspective of institutional organizational theories and in context of sustainable development. Prior work The previous findings have revealed significant lack of attention to factors impacting the smart city concept. Approach The paper applies literature reviews and survey analysis involving the experts in the field of smart cities. Results The paper applies literature reviews and survey analysis involving the experts in the field of smart cities. Implications The study provides the perspective useful for local and regional authorities as well as other parties involved in smart city implementation and development. Value The paper proposes the extended multi-factor smart city model approach encompassing the factors from different theories.

Spatial modeling tool to assess and rank peri-urban land use in an agricultural region of the Midwestern United States

America is the continent with the largest area of genetically modified crops, the United States being the leading producer. Numerous studies show a panorama of potential exposure from agricultural pesticide use for this types of crops near to cities across a vast region of the United States. For the reasons mentioned above, we have chosen to investigate the following issues in this study: How does the implementation of an indexbased spatial modeling tool effectively rank the proximity of peri-urban crops, and what factors impact its effectiveness across diverse peri-urban agricultural landscapes? To address these questions, the research employs the Crop Proximity Index (CPI) model in various cities across the Midwest region of the United States. Six hundred and seventy cities in the state of Iowa were selected, and their peripheries were analysed using weighted perimeter rings, from 0 to 2000 m. The Crop Proximity Index was used to simulate a model of proximity to crops by considering the spatial quantification occupied by agriculture, forest cover, shrubs, pastures and buffer zones. This index varies from 0 to 1 and serves to rank the cities under study. It was estimated that a Crop Proximity Index equal to or >0.8 is a good approximation to a model with less proximity of crops and that only 62 cities (9%) meet this condition. Some 457 cities (68%) have CPIs equal to or <0.5 due to the large areas of crops and the low peripheral forest levels. The CPI is an index that makes it possible to obtain vital exploratory data in order to focus on future research that would determine how the proximity of agro-industrial crops has possible negative consequences for the environment and human health in greater detail.

A COMPARATIVE STUDY OF CATBOOST AND ARTIFICIAL NEURAL NETWORKS IN ENHANCING TRIP GENERATION MODELLING FOR ILORIN CITY

Trip generation plays a crucial role in transportation planning, and the choice of an appropriate model is essential for predicting future travel patterns. This study focuses on comparing the suitability and performance of CatBoost and ANN for trip generation (production and attraction) modelling of Ilorin City. By incorporating Ilorin household and trip characteristics, population data, and maps, this study evaluates the performance of the models. The two models demonstrated high accuracy and performance. In terms of trip production, the CatBoost model displayed exceptional accuracy, attaining an R-squared value of 0.99999992016446, accompanied by an impressively low mean squared error (MSE) of 3.93870930136429e-05. In contrast, the neural network exhibited a slightly lower accuracy of 0.999873850524181, with an error value of 0.0581313408911228. Similarly, for trip attraction, the CatBoost model showcased remarkable accuracy and precision, achieving an accuracy score of 0.9999999999999994 and an extremely low error value of 2.26762031965784e-13. The neural network model demonstrated an accuracy of 0.99999999990335 and a negligible error value of 0.000000041994. These findings underscore the strong predictive capabilities of both models for trip production and attraction, with the CatBoost model notably excelling in achieving nearly flawless accuracy and minimal error values across both aspects in Ilorin. Further research can explore the application of other advanced machine-learning techniques and combine their strengths to enhance the accuracy and robustness of trip-generation models.

Mapping the CityGML Energy ADE to CityGML 3.0 Using a Model-Driven Approach

With the increasing adoption of semantic 3D city models, the relevance of applications in the field of Urban Building Energy Modelling (UBEM) has rapidly grown, as the building sector accounts for a large part of the total energy consumption. UBEM allows us to better understand the energy performance of the building stock and can contribute to defining refurbishment strategies. However, UBEM applications require lots of heterogeneous data, eventually advocating for standards for data interoperability. The Energy Application Domain Extension has been created to cope with UBEM data requirements and offers a standardised data model that builds upon the CityGML standard. The Energy ADE 1.0, released in 2018, creates new classes and adds new properties to existing classes of the CityGML 2.0 Core and Building modules. CityGML 3.0, released in 2021, has introduced several changes to the data model and its ADE mechanism. These changes render the Energy ADE incompatible with CityGML 3.0. This article presents how the Energy ADE has been ported to CityGML 3.0 to allow, on the one hand, for a lossless data conversion and, on the other hand, to exploit the new characteristics of CityGML 3.0 while keeping a logical symmetry between the ADE classes of both CityGML versions. The article describes the chosen methodology, the mapping strategies, the implementation steps, as well as the data conversion tests to check the validity of the “new” Energy ADE for CityGML 3.0.

HVAC system operation, consumption and compressor size optimization in urban buses of Mediterranean cities

Electric buses are a key element in the transition to sustainable urban mobility. The HVAC system is the primary auxiliary load and significantly affects the efficiency and the driving range. In this work, advanced dynamic models have been developed to simulate accurately and optimize the urban bus energy consumption under real operating and extreme conditions, with a particular emphasis on HVAC systems. This study integrates a 3D city model, a weighted stochastic driving cycle, a climate model, a transient thermal model coupled with a physical HVAC system model using IMST-ART and a battery model. Different bus lines and urban typologies have been analyzed in the city of València. The results indicate that the overall consumption is similar across the different bus lines, around 2.10 kWh/km. The HVAC is the second largest contributor, after the powertrain, and can reduce the driving range by 15%–20% on mild and hot summer days, respectively. Finally, the size of the compressor has been optimized, revealing that a scale factor of 75% is more convenient, since the energy consumption can be reduced by 3% with lower costs in the compressor.

Interrupted Time Series Analysis of Bar/Tavern Closing Hours and Violent Crime

It is well established that alcohol outlets (ie, places that sell alcohol) attract crime, particularly during late-night hours. To evaluate the association of Maryland Senate Bill 571 (SB571), which reduced the hours of sale for bars/taverns in 1 Baltimore neighborhood from 6 am to 2 am to 9 am to 10 pm, with violent crime within that neighborhood. This controlled interrupted time series analysis compared the change in violent crime density within an 800-ft buffer around bars/taverns in the treatment neighborhood (ie, subject to SB571) and 2 control areas with a similar mean baseline crime rate, alcohol outlet density, and neighborhood disadvantage score in the City of Baltimore between May 1, 2018, and December 31, 2022. The interrupted time series using Poisson regression with overdispersion adjustment tested whether the violent crime density differed before vs after the policy change in the treatment neighborhood and whether this difference was localized to the treatment neighborhood. Statutory reduction of bar/tavern selling hours from 20 to 13 hours per day in the treatment neighborhood. The primary outcome was all violent crime, including homicide, robbery, aggravated and common assault, and forcible rape. Secondary outcomes were homicides and assaults. All violent crime measures summed the monthly incidents within 800 ft of bars/taverns from 8 pm to 4 am. For each outcome, a level change estimated the immediate change (first month after implementation), and a slope change estimated the sustained change after implementation (percent reduction after the first month). These level and slope changes were then compared between the treatment and control neighborhoods. The treatment neighborhood included 26 bars/taverns (mean [SD] population, 524.6 [234.6] residents), and the control neighborhoods included 41 bars/taverns (mean [SD] population per census block, 570.4 [217.4] residents). There was no immediate level change in density of all violent crimes the month after implementation of SB571; however, compared with the control neighborhoods, the slope of all violent crime density decreased by 23% per year in the treatment neighborhood after SB571 implementation (annualized incidence rate ratio, 0.77; 95% CI, 0.60-0.98; P = .04). Similar results were seen for homicides and assaults. Several sensitivity analyses supported the robustness of these results. This study's findings suggest that alcohol policies that reduce hours of sale could be associated with a reduction in violent crimes. Given these findings, SB571 may serve as a model for other cities looking to create safer neighborhoods.

Time series forecasting of electricity consumption using hybrid model of recurrent neural networks and genetic algorithms

The forceful energy efficiency to manage the demand is essential to meet development goals. Palestine has suffered from an electricity deficit, whereas the city of Tulkarm suffers from a chronic one. The dataset was collected from Tulkarm city in Palestine; this city is considered one of the cities that suffers the most from frequent power outages. It's difficult to determine the most powerful Artificial intelligence (AI) approaches that can accurately forecast electricity consumption. This paper presents a hybrid model that combines Recurrence Neural Networks (RNNs) and Genetic Algorithms (GAs) [RNN-GAs] to forecast electricity consumption and optimize demand. In the proposed model the K-means clustering technique produces specific initial population seeding and optimization crossover operators to enhance the efficiency and find the optimal solution. The results showed that the proposed Nonlinear Autoregressive with External (Exogenous) (NARX) (NARX-GAs) with the K-means clustering technique outperforms the hybrid model NARX-GAs. The NARX-GAs-K Mean Clustering recorded an RMSE value of 0.08759, which performs a good balance with the lowest RMSE, especially in long-term forecasting, and also outperforms the other hybrid forecasting models that depend on RNN-GAs. Finally, the forecasting results of the hybrid NARX-GAs-K Mean Clustering can predict accurately the energy consumption in a city, which leads to the use of the model in similar cities to forecast and manage the demand for electricity consumption.

Strategic analysis on improving the proximity and accessibility in China: an exploratory development based on the study of the 15-minute city

Against the backdrop of rapid urbanization in China's megacities, the growing need for accessible and convenient transportation has become more pronounced. The rise of the 15-minute city concept in chrono-urbanism has become increasingly evident in recent years. In this context, the paper presents a comprehensive strategic analysis and explores future possibilities for enhancing proximity and accessibility in urban transportation. The focus is on the exploratory development of the 15-minute city concept. By adopting a qualitative analytical approach, the paper delves into an analysis based on Melbourne’s '20-minute city' to assess the applicability and diverse development of the 15-minute city model in Chinese urban centers. By acquiring current data and examples, the findings reveal the strengths and challenges of the 15-minute city concept, underscoring its adaptability and uniqueness across different geographical, national, and targeted contexts. The paper indicates that among megacities in China, the 15-minute city highlights the potential for integrating advanced technology to enhance urban mobility and proximity. It also calls for a shift towards high-quality development in urban planning to strengthen the resilience of city transport. The study’s exploratory nature also opens up avenues for future development within the 15-minute city framework, providing a robust base for improving urban transportation in China. The significance of this research lies in its contribution to shaping high-quality and livable urban environments, thereby supporting the ongoing transformation of China’s large urban centers into more accessible and human-centered spaces.

Traffic noise modelling at intersections in mid-sized cities: an artificial neural network approach.

Traffic noise has emerged as one major environmental concern, which is causing a severe impact on the health of urban dwellers. This issue becomes more critical near intersections in mid-sized cities due to poor planning and a lack of noise mitigation strategies. Therefore, the current study develops a precise intersection-specific traffic noise model for mid-sized cities to assess the traffic noise level and to investigate the effect of different noise-influencing variables. This study employs artificial neural network (ANN) approach and utilizes 342h of field data collected at nineteen intersections of Kanpur, India, for model development. The sensitivity analysis illustrates that traffic volume, median width, carriageway width, honking, and receiver distance from the intersection stop line have a prominent effect on the traffic noise level. The study reveals that role of noise-influencing variables varies in the proximity of intersections. For instance, a wider median reduces the noise level at intersections, while the noise level increases within a 50-m distance from intersection stop line. In summary, the present study findings offer valuable insights, providing a foundation for developing an effective managerial action plan to combat traffic noise at intersections in mid-sized cities.

Determination of Radiation Value by Month Using Artificial Neural Network Model; Ankara, Sivas, Erzurum example

This research examines the estimation of solar radiation using artificial neural network (ANN) models in Turkish cities with similar latitude values such as Ankara, Sivas and Erzurum. The aim of this study is to investigate whether cities at similar latitudes exhibit similar trends in solar radiation values, despite their geographical differences. In this study, solar radiation prediction was made for 3 cities with a single-layer neural network. Monthly solar radiation intensity was estimated for the 10-year period between 2012 and 2022 with a total of 4764 samples taken from the General Directorate of State Meteorology. An artificial neural network model was developed with 8 units in the first hidden layer and 4 units in the second hidden layer. The optimizer used in compiling the model was determined as Adam, the loss function as 'mean_squared_error' and the metric as 'mse'. ReLU activation function was used in the input layer and hidden layers. A 10-year solar radiation intensity value was used in the output layer. 70% of the data set is reserved for training and 30% for testing data set. As a result, similar solar radiation trends were obtained in the same latitude regions, the results were confirmed by meteorological data.

UV-SAM: Adapting Segment Anything Model for Urban Village Identification

Urban villages, defined as informal residential areas in or around urban centers, are characterized by inadequate infrastructures and poor living conditions, closely related to the Sustainable Development Goals (SDGs) on poverty, adequate housing, and sustainable cities. Traditionally, governments heavily depend on field survey methods to monitor the urban villages, which however are time-consuming, labor-intensive, and possibly delayed. Thanks to widely available and timely updated satellite images, recent studies develop computer vision techniques to detect urban villages efficiently. However, existing studies either focus on simple urban village image classification or fail to provide accurate boundary information. To accurately identify urban village boundaries from satellite images, we harness the power of the vision foundation model and adapt the Segment Anything Model (SAM) to urban village segmentation, named UV-SAM. Specifically, UV-SAM first leverages a small-sized semantic segmentation model to produce mixed prompts for urban villages, including mask, bounding box, and image representations, which are then fed into SAM for fine-grained boundary identification. Extensive experimental results on two datasets in China demonstrate that UV-SAM outperforms existing baselines, and identification results over multiple years show that both the number and area of urban villages are decreasing over time, providing deeper insights into the development trends of urban villages and sheds light on the vision foundation models for sustainable cities. The dataset and codes of this study are available at https://github.com/tsinghua-fib-lab/UV-SAM.