City Model Research Articles

The Impact of Slow Food Consumption in Cities and Smart Cities

Processed, nutrient-poor foods around the world are causing serious health problems such as obesity and diabetes as well as environmental problems such as urban carbon emissions. Making cities more ecological and livable is an urgent global priority for sustainable development. The Slow Food Movement is a significant initiative that promotes sustainable development by providing high quality, environmentally friendly and socially just food for everyone. The aim of this article is to propose a framework that demonstrates how a slow food approach, focused on providing good, clean, and fair food, can be integrated into a sustainable development model for smart cities. This model is rooted in the principles of local economic development, which considers the roles of consumers, producers, distribution channels, and local authorities in promoting sustainability. Smart cities can play a crucial role in reducing food waste and transportation costs by promoting seasonal, locally sourced, and sustainable food options. This can be achieved through preserving traditional techniques and knowledge, as well as promoting shorter production and distribution chains. Community-run markets are an excellent way to provide consumers with healthy, high-quality food at fair prices, while also encouraging local economic development. To achieve a sustainable and smart city, it is essential to foster collaboration between governments, businesses, local communities, and consumers. This collaborative effort will promote a smart economy, smart mobility, smart environment, smart people, smart living, and smart governance. By working together, these stakeholders can create a more sustainable and resilient food system, which is essential for the health and wellbeing of both the city and its inhabitants.

TSUNAMI MODELLING IN A BUILT-IN COASTAL ENVIRONMENT WITH ADAPTIVE MESH RIFINEMENT AND VARYING BOTTOM FRICTION

The great Japan earthquake on March/11/2011provoked an extreme tsunami wave toward the northeastern Japanese coast. The tsunami inundation covered a wide range of territories stretching over 500 km2 and caused devastating influences that claimed more than 15,000 lives (Mori et al. (2011)). Miyagi Prefecture, the closest mainland to the epicentre, received massive tsunami waves that reached over 40 m in run-up height. Prasetyo et al. (2019) aimed to reproduce the tsunami wave inundation in Onagawa Town of Miyagi Prefecture by building a physical model using Hybrid Tsunami Open Flume in Ujigawa Open Laboratory (HyTOFU) at Kyoto University to study the inundation process by measuring the wave height and arrival time. For that purpose, two wave types were used, the (Pump-type) hydraulic bore, and the (Piston-type) solitary wave. Also, 2D and quasi-3D (Q3D) models were tested for their robustness and applied to emulate tsunami propagation in the complex coastal city model. In this study, we adopted adaptive mesh refinement (AMR) for its efficiency in tsunami modelling with Basilisk open-source flow solver for non-linear shallow water (Saint-Venant) equations and fully non-linear Boussinesq (Green-Naghdi) equations (Popinet (2015)). The numerical experiment was conducted to validate the model by comparing the wave height with the physical model experiment, the 2D, and Q3D model results by Prasetyo et al. (2019).

Generalized Wardrop Equilibrium for Charging Station Selection and Route Choice of Electric Vehicles in Joint Power Distribution and Transportation Networks

This paper presents the equilibrium analysis of a game composed of heterogeneous electric vehicles (EVs) and a power distribution system operator (DSO) as the players, and charging station operators (CSOs) and a transportation network operator (TNO) as coordinators. Each EV tries to pick a charging station as its destination and a route to get there at the same time. However, the traffic and electrical load congestion on the roads and charging stations lead to the interdependencies between the optimal decisions of EVs. CSOs and the TNO need to apply some tolling to control such congestion. On the other hand, the pricing at charging stations depends on real-time distributional locational marginal pricing, which is determined by the DSO after solving the optimal power flow over the power distribution network. This paper also takes into account the local and the coupling/infrastructure constraints of EVs, transportation and distribution networks. This problem is modeled as a generalized aggregative game, and then a decentralized learning method is proposed to obtain an equilibrium point of the game, which is known as variational generalized Wardrop equilibrium. The existence of such an equilibrium point and the convergence of the proposed algorithm to it are proven. We undertake numerical studies on the Savannah city model and the IEEE 33-bus distribution network and investigate the impact of various characteristics on demand and prices.

Development of a synthetic dataset generation method for deep learning of real urban landscapes using a 3D model of a non-existing realistic city

In the urban landscaping field, training datasets for instance segmentation in the detection of building facades are needed for complex analysis and simulation based on data. Manual dataset generation methods are costly, so automatic generation is preferred. However, previous methods are costly and are more reliant on 3D real-city models than LOD2. The objective of this research is to propose a method for automatically generating synthetic datasets for instance segmentation using an image translation technique and procedural modeling. The image translation technique is used to generate street view images for training data, and procedural modeling can automatically specify the composition of generated street view images and generate annotation data, as well as a variety of 3D models. The proposed method generates various kinds of synthetic data at a rate of 4.45 s per set, and the model trained on the generated dataset can detect real buildings for each instance.

Modern Approaches to Quantitative Assessment of Habit Formation Based on Consumer Demand Research. On the Example of Alejandro Tatsuo Moreno Okuno (2010) Price competition in mixed strategies in markets with habit formation

This article examines the impact of consumer habit formation on the competition of differentiated product firms based on the analysis of the article "Price competition in mixed strategies in markets with habit formation". The work consists of six parts, including consideration of related methodological approaches, the author’s position, the results of the study, as well as ways to expand the author’s model. The distribution of habits for the use of certain products is studied, as well as the characteristics of these habits are studied, taking into account the influence of the psychological aspect on the behavior of an economic agent. This paper considers the "rational maximizer model", models that take into account the influence of social and psychological factors on economic decision-making by agents, models that take into account social welfare and its components, differences in economies in terms of participation in market exchange. The presented study analyzes the linear city model underlying this and other works on this topic. The presented study offers a new look at the phenomena described by other economists and operates not only with theoretical models, but also with digital solutions. Computer technology allows us to model complex economic processes where standard theoretical methods are powerless. However, the weakness of this work lies in the same approach: a large number of prerequisites that do not have a detailed theoretical justification narrow the scope of the research results, thereby leveling their usefulness. The main conclusion is that it is advantageous for firms competing in the habit formation market to differentiate their product as much as possible, taking into account the structure of the consumer’s utility function. The presented research includes the works of the Theory of Consumer Behavior, models of the Theory of Industrial Markets and the Theory of Games.

Building a program to automatically classify point cloud data

Along with cartography science and technology development, data acquisition through aeronautical laser scanning systems has been developing. This is an essential and detailed data source for database construction, mapping, and city 3D modeling,... City 3D modeling requires processing many types of data, at which point cloud data processing and classification play an essential role in creating input data sources for the model. However, the processing and classification of point cloud data mainly depend on commercial software with very high costs; moreover, the algorithms and parameters of commercial software are locked. That makes it impossible for the user to intervene to improve product accuracy. Therefore, building a program to automatically classify point cloud data into different geographical objects helps us master data processing technology for creating 3D models. It makes an important contribution to building and developing smart cities. The article introduces the step-by-step classification of LiDAR point cloud data and the process of automatically building a program to classify point cloud data based on Visual Studio.Net language. The result is a bilingual program automatically classifying point cloud data (Vietnamese - English). The program can read and fully deploy algorithms to process LiDAR point cloud data containing information with four color bands (red, green, blue, and near-infrared). The primary processing is based on proposed classification steps and thresholds for point cloud data classification into eight feature classes, including hydrology, solar land, traffic, low plants, medium plants, high plants, houses, and other objects, to establish 3D city models.

Building use and mixed-use classification with a transformer-based network fusing satellite images and geospatial textual information

Assigning detailed use categories to buildings is a challenging and relevant task in urban land use classification with applications in urban planning, digital city modelling and twinning. This study aims to provide the categorisation of buildings with detailed use information by considering the possibilities of mixed-use. Mixed-use combines different use forms, and serves as a new type of use category. We obtain attributive information by combining satellite imagery that reflects spatial information and textual information from publicly available point-of-interest data collected by citizens and available on online maps. We propose a multimodal transformer-based building-use classification method to capture and fuse these different data sources within an end-to-end learning workflow. We evaluate the effectiveness of our proposed method on four urban areas in China. Experiments show that the proposed method effectively maps building use according to eight types of fine-grain categories, with a Micro F1 score equal to 80.9%, and a Macro F1 score equal to 62% for Wuhan research area. The proposed method is able to harness the relationship between the features obtained from the different data sources and results in higher accuracy than the state-of-the-art fusion-based multimodal integration methods. The proposed method can effectively increase the attributive grain of building use resulting in high classification accuracy.

Algorithm for planning shelters in oil and gas energy resource-based cities based on artificial intelligence resilient city model

Cities based on oil and gas energy resources are crucial to energy production and economic development, but they also face various disasters and security risks. To ensure the safety and well-being of urban residents during disaster events, the planning of urban shelters is crucial. In this paper, comprehensively considering multiple factors such as disaster risk, population distribution, and convenient transportation, the artificial bee colony algorithm is used to optimize the site selection and capacity planning of shelters. By comprehensively evaluating the disaster resistance capacity of urban refuges, the response speed of residents and other related indicators, the planning algorithm of refuges is continuously optimized to better meet the needs of oil and gas energy resource-based cities. The results of the study showed that the average overall disaster resilience of AI-based urban shelters reached 0.64. When the distance to the shelter was 4 km, the average response speed of residents reached 10.22 min, and other indicators also improved. The research shows that the oil and gas energy urban refuge planning algorithm based on the artificial intelligence elastic city model provides an innovative approach for urban planners and disaster managers. Further research and practice will help promote the application of this algorithm in real cities, improving the resilience and disaster resistance of cities and the safety and security level of residents.

Towards data security assessments using an IDS security model for cyber-physical smart cities

Technology has enabled many devices to exchange huge amounts of data and communicate with each other as Edge Intelligence in Smart Cities (EISC), as a result of rapid technological advancements. When dealing with personal data, it is paramount to ensure that it is not disclosed and that there is no disclosure of any confidential information. In recent decades, academics and industry have spent considerable time and energy discussing security and privacy. Other systems, known as intrusion detection systems, are required to breach firewalls, antivirus software, and other security equipment to provide complete system security in smart operation systems. There are three aspects to an intrusion detection system: the intrusion detection method, the architecture, and the intrusion response method. In this study, we combined linear correlation feature selection methods and cross-information. The database used in this article is KDD99. This paper examines applying two feature selection methods in predicting attacks in intrusion detection systems based on INTERACT and A multilayer perceptron (MLP). Since the number of records associated with each attack type differs, one of our suggestions is to continue using data balancing techniques. As a result, the number of records associated with each type of network status becomes closer together. The results in the categories can also be improved using information synthesis methods, such as majority voting.

A FEATURE-BASED DEEP LEARNING APPROACH FOR THE EXTRACTION OF GROUND POINTS FROM 3D POINT CLOUDS

Abstract. Extracting ground points from 3D point clouds is important for sustainable development goals, infrastructure planning, disaster management, and more. However, the irregularity and complexity of the data make it challenging. Deep learning techniques, particularly end-to-end and non-end-to-end approaches, have shown promise for 3D point cloud segmentation and classification, but both require a comprehensive understanding of the features and their relationship to the problem. This paper presents a study on the filtering of 3D LiDAR point clouds into ground and non-ground points using a non-end-to-end deep learning approach. The aim of this research is to investigate the effectiveness of utilizing geometric features and a binary classifier-based deep learning model in accurately classifying point clouds. The publicly available ACT benchmark datasets were employed for training, validation, and testing purposes. The study utilized a k-fold cross-validation method to address the limited availability of training data. The results demonstrated highly satisfactory performance, with validation averages reaching 96.83% for the divided Dataset-1 and an accuracy of 97% for the test set. Furthermore, an independent dataset, Dataset-2, was used to evaluate the generalizability of the trained model, achieving an accuracy of 93%. These findings highlight the potential of the proposed non-end-to-end approach to filtering point cloud data and its applicability in various domains such as DEM and DTM production, city modeling, urban planning, and disaster management. Moreover, this study emphasizes the need for accurate data to achieve sustainable development goals, positioning the proposed approach as a viable option in various studies.

A House Price Prediction Model Based on K-means Clustering and Random Forest in Guangzhou

This paper addresses the key issues in house price forecasting from multiple perspectives by establishing a house price forecasting model for Guangzhou city, providing valuable information and decision support for home buyers, developers, and the government. First, this paper employs the Person coefficient, stepwise regression model and t-test to address the problem of quantifying data and exploring house price factors. By analyzing the correlation between the relevant variables and house prices, the key characteristics that have significant and strong correlation effects on house prices are obtained. Second, the K-means clustering model was used to classify houses into three categories: economic houses, comfortable houses and high-end houses. This classification result provides a more detailed data base for the subsequent construction of the house price prediction model. Finally, the random forest house price prediction model was established in this paper, and the model was validated by error analysis and stability analysis. The average absolute value error and goodness-of-fit obtained were 0.08 and 0.92, respectively, indicating that the model has high accuracy and reliability. The research in this paper has important implications for all parties, including home buyers, developers, and the government. For home buyers, the model can help them better understand the market situation; for developers, the model can guide their reasonable pricing and development strategies; for the government, the model can provide a scientific basis for real estate market regulation and policy control to promote market stability and sustainable development.

Integrating Cadastral Data with Seismic Risk Data in an Online Building Database for the Historical Centre of Bucharest City

The Historical Centre of Bucharest City is a highly populated area and a popular tourist attraction, known for its old buildings, some of which were built before the year 1900 and were severely damaged by the 7.4 magnitude earthquake in 1977. Seismic risk available data consist of lists published by the Municipal Administration for the Consolidation of Buildings with Seismic Risk, and include various information about the buildings for which technical expertise was conducted. Available cadastral data can provide additional information about the buildings, including the buildings’ location and area. In this article, we aim to integrate the available cadastral data with seismic risk data in an online building database for the Historical Centre of Bucharest City. Such a database can be used for further 3D city modelling, which can be helpful in disaster management and emergency situations response planning. Previous initiatives in this regard did not provide precise geospatial data or important attributes regarding the situation of these buildings. It is vital to take into account the official data related to the property, mentioned in the National Land Registry.

Modelling and visualisation of land surface temperature in urban areas represented by 3D city models

Modelling and visualisation of land surface temperature in urban areas represented by 3D city models

Analysis of Cartographic Symbols as Visual Support in Interactive VR Geovisualizations

Abstract. Game engines and head-mounted displays (HMD) can create interactive virtual reality (VR) cartographies. Many VR geovisualizations try to depict places as close to reality as possible. The scaling is 1:1 for most VR geovisualizations. If VR geovisualizations are too close to reality or complex, users can navigate through them better, but the amount of detail makes it more challenging to target information. With the help of cartographic elements, a more targeted focus can again be acquired. The focus and style of VR cartography depend on the topic. The topics selected were "Floods and heavy rainfall events", "Noise propagation analysis", and "Lighting simulation". These are three topic examples in which 3D city models are used. CityGML models and fictitious 3D objects were used. Different rendering techniques and point signatures were implemented for each of the themes. All three topics were implemented in an application as a serious game. With the help of an interactive interface and motion controllers, users can change and navigate all three themes. Solution-oriented objects can be placed on all three topics as an additional interactive element. These options allow users to learn targeted information through various presentation options and implement solutions.

Effect of national COVID-19 control measures on water quality in China using an improved dual difference-in-differences method

The impact of COVID-19 on the natural environment, particularly the aquatic environment, is a major concern. However, there is a lack of national-level research on the impact of COVID-19 on the aquatic environment. This study analysed the impacts of COVID-19 on water quality using an improved dual spatial-temporal Difference-in-Differences Method (DDiD) models in cities with and without formal lockdown policies all over the China. The study found that the effectiveness of national COVID-19 control measures varied spatially for different types of pollutants. The lockdown measure resulted in a 0.052-point decline in TN concentration and a 0.056-point decline in CODMn concentration, while the non-formal lockdown measures led to a 0.2-point increase in TN concentration and a 0.016-point decline in CODMn. Spatial-temporal analysis revealed that COVID-19 and control measures immediately reduced TN concentration. The decrease effect of non-formal lockdown measures was lower than that of the lockdown measure and caused an opposite overall effect of TN concentration change. The impact of COVID-19 and control measures on CODMn concentration had a time lag effect, as it maintained or slightly increased local CODMn concentration once the lockdown measure was implemented, and then decreased after 4 weeks. Heterogeneity analysis showed that the reduction effect on TN was more significant in cities that rely more on industrial activities, while the increase effect on CODMn was more significant in cities that rely more on general and domestic services industries, revealing the diversity of water pollution sources. Overall, the impact of the COVID-19 pandemic on water quality is not as significant as that on the atmospheric environment due to the diversity of pollution sources. The study partially proves that China has achieved excellent point source control for water pollution, but there is little potential reduction of water pollution at point sources, especially industrial sources. The best way to improve the aquatic environment is still to enhance its capacity by increasing water flow and storage ability and reducing the amount of discharge from every pollution source by improving production levels and pollution treatment ratios, especially for nonpoint sources.

A Cooperative Attack Detection Framework for MANET-Iot Network Using Optimized Gradient Boosting Convolutional Neural Network

The implementation of large-scale Internet of Things (IoT) devices results in smart cities. Using standard mobile ad-hoc networks and IoT, developers establish the communication model for a smart city. The rapid growth of IoT devices based on smart cities poses different Quality of Service (QoS) and security problems. This research presents a novel Modified Elephant Herd Optimization (MEHO) method and a Gradient Boosting Convolutional Neural Network (CNN) strategy to address these issues. The cooperative attacks with varied disruption probabilities are initially assessed at the edge nodes of the IoT network. The MEHO-based Gradient Boosting CNN (MEHO-CNN) approach effectively detects cooperative attacks, ensuring the identification of malicious entities. For traditional cloud access, both bandwidth utilization as well as expected latency are minimized in edge computing. By using the IoT network, the proposed MEHO-CNN model identifies and eliminates malicious nodes. To establish the claimed trustworthy background, the legitimate accusations are based on an examination of trust-based allegations. When compared to existing methodologies, the proposed approach lowers the impact of cooperative attacks, resulting in increased throughput, reduced attack detection rates, lower packet loss ratio, lower packet delivery ratio, and other benefits.

A methodology for the strategic design of city logistics networks and its application in Turkish automotive spare parts industry

City logistics approaches and modeling struggles have a significant role in urban areas in increasing the efficiency of logistics operations and reducing traffic jams and their environmental effects. By developing an effective distribution network for cities, it is possible to compete with the changing world and satisfy flexible customer requirements. In this study, as a real-world case, a city logistics model for Istanbul metropolitan area is designed using multi-objective linear programming that considers the different objectives of the stakeholders in cities by integrating the fuzzy Choquet integral technique in a multi-level distribution network for the automotive spare part industry. This paper makes decisions regarding the amount of product flowing among the echelons, the amount of stock to be kept in the warehouses, and the product delays allowed. While minimizing the transportation cost, holding cost and emission levels during these decisions, the study also aims to maximize the service quality in the warehouses. The model is applied to a logistics network of fifty demand points and thirty time periods which can be considered a middle or large-scale problem. In the model, it is also decided to transport the products with electric or fuel vehicles. In the transport sector, electric vehicles are the key to meet future needs for social, health and other human services. The results are discussed under different scenarios. This research allows the use of such a model in making strategic decisions for the distribution network design in big cities.

A conceptual model for a circular city: a case study of Maribor, Slovenia

Cities play a crucial role in achieving sustainable development. Decision-makers require assistance in developing city transformation plans amidst the emergence of various city models. A content analysis using concept mapping was conducted to examine smart, circular, and green city models. The analysis, supported by Leximancer, revealed that city models are evolving by adopting beneficial solutions from competitors, reflecting a strong focus on sustainable development. Additionally, twenty-four research areas essential for implementing a circular city were identified and validated. Furthermore, a conceptual model for a circular city was developed, incorporating the Define-Measure-Analyse-Improve-Control tool and a problem-solving system. Testing the model on Maribor highlighted challenges in monitoring the transition towards circularity. The study validates the established model but emphasises the need for further research and case studies to verify its practicality. This scientific research enhances the understanding of city models and their evolution towards sustainability, providing valuable insights for decision-makers and urban planners.Graphical abstract

Deep learning aided web-based procedural modelling of LOD2 city models

Deep learning aided web-based procedural modelling of LOD2 city models

Open Geospatial Data Integration in Game Engine for Urban Digital Twin Applications

Increasing interest has recently been shown towards the digital twins of cities. These urban digital twins utilize 3D city models together with other data sources, such as open data, which can be accessed, e.g., through application programming interfaces (APIs). A large amount of open geospatial data is already available through various open data portals and APIs, such as REST and MQTT. Game engines, on the other hand, have been seen as highly potential platforms for urban digital twins, with recent development increasing the useability of geospatial data in game engines. This study explores the possibilities of game engines for urban digital twins by integrating and visualizing API-based open geospatial datasets with a 3D city model data set in a game engine environment. Three open data integration approaches are demonstrated, and the performance of these approaches is tested for urban digital twins. Based on the results, modern game engines offer a great platform for creating urban digital twin applications; however, some problems and limitations must be overcome before the use of geospatial data in game engines becomes feasible on a larger scale.