Urban Management Research Articles

Unraveling Urban Disaster Management: A Deep Dive into SETS Implications through a Case Study of Toyama City, Japan

Unraveling Urban Disaster Management: A Deep Dive into SETS Implications through a Case Study of Toyama City, Japan

Game Study on Morning Peak Road Congestion Problem

The morning peak road congestion problem has always been a hot issue in urban traffic management. Road congestion during the morning peak not only affects the travel efficiency of travelers, but also has a serious negative impact on the urban environment and economic development. This article starts from the perspective of game theory and focuses on the problem of road selection. This article considers government supervision and builds a game model from the two game subjects of the driver and the traffic controller. This article analyzes the relationship between the driver and the driver, the driver and the traffic controller. This paper studies the impact of traffic time, whether the driver investigates in advance, and whether the traffic commander conducts road command on the stable strategy of the driver's road choice game. The research results of this article show that drivers and drivers can achieve Nash equilibrium by choosing different roads, and drivers adopting collaborative strategies during the morning peak period can effectively alleviate road congestion. Under the condition of ensuring the cost of arranging traffic commanders for drivers and the government, drivers can investigate the road in advance and traffic commanders can direct road 2, which can effectively promote the smooth operation of the traffic system. At the same time, this article puts forward targeted policy suggestions, aiming to optimize the allocation of transportation resources, improve road traffic efficiency, reduce morning peak road congestion, and provide scientific reference and decision-making support for urban traffic management.

Portable Arduino-Based Multi-Sensor Device (SBEDAD): Measuring the Built Environment in Street Cycling Spaces

The built environment’s impact on human activities has been a hot issue in urban research. Compared to motorized spaces, the built environment of pedestrian and cycling street spaces dramatically influences people’s travel experience and travel mode choice. The streets’ built environment data play a vital role in urban design and management. However, the multi-source, heterogeneous, and massive data acquisition methods and tools for the built environment have become obstacles for urban design and management. To better realize the data acquisition and for deeper understanding of the urban built environment, this study develops a new portable, low-cost Arduino-based multi-sensor array integrated into a single portable unit for built environment measurements of street cycling spaces. The system consists of five sensors and an Arduino Mega board, aimed at measuring the characteristics of the street cycling space. It takes air quality, human sensation, road quality, and greenery as the detection objects. An integrated particulate matter laser sensor, a light intensity sensor, a temperature and humidity sensor, noise sensors, and an 8K panoramic camera are used for multi-source data acquisition in the street. The device has a mobile power supply display and a secure digital card to improve its portability. The study took Beijing as a sample case. A total of 127.97 G of video data and 4794 Kb of txt records were acquired in 36 working hours using the street built environment data acquisition device. The efficiency rose to 8474.21% compared to last year. As an alternative to conventional hardware used for this similar purpose, the device avoids the need to carry multiple types and models of sensing devices, making it possible to target multi-sensor data-based street built environment research. Second, the device’s power and storage capabilities make it portable, independent, and scalable, accelerating self-motivated development. Third, it dramatically reduces the cost. The device provides a methodological and technological basis for conceptualizing new research scenarios and potential applications.

Recent Advances for Aerial Object Detection: A Survey

Aerial object detection, as object detection in aerial images captured from an overhead perspective, has been widely applied in urban management, industrial inspection, and other aspects. However, the performance of existing aerial object detection algorithms is hindered by variations in object scales and orientations attributed to the aerial perspective. This survey presents a comprehensive review of recent advances in aerial object detection. We start with some basic concepts of aerial object detection and then summarize the five imbalance problems of aerial object detection, including scale imbalance, spatial imbalance, objective imbalance, semantic imbalance, and class imbalance. Moreover, we classify and analyze relevant methods and especially introduce the applications of aerial object detection in practical scenarios. Finally, the performance evaluation is presented on two popular aerial object detection datasets VisDrone-DET and DOTA, and we discuss several future directions that could facilitate the development of aerial object detection.

Experimental Study on Automated Controller of Street Light Management System using IoT

In order to ensure public safety and support nighttime activities, street lighting is crucial to the urban landscape, and this study starts by contextualizing its importance. It then goes on to analyze the shortcomings of the current street lighting systems, which are typified by their static control methods and poor environmental response.These shortcomings underscore the urgent need for a paradigm shift towards more intelligent and adaptive solutions. The core proposition of this project lies in the design and implementation of an advanced street light management system empowered by IoT technology. At its heart, the system comprises a sophisticated network of interconnected devices, including sensors, actuators, and a central control unit. These components collaborate synergistically to capture, analyze, and act upon real-time data gleaned from the surrounding environment. Through the application of cutting-edge data analytics and machine learning algorithms, the system autonomously adjusts lighting levels in accordance with fluctuating parameters such as ambient light levels, traffic density, weather conditions, and pedestrian activity. By dynamically optimizing energy usage while ensuring adequate illumination, the system not only enhances operational efficiency but also contributes to cost reduction and environmental sustainability. Moreover, the proposed system incorporates proactive maintenance features, enabling early fault detection and remote diagnostics. This capability minimizes downtime and enhances system reliability, thereby mitigating the operational challenges associated with traditional street lighting infrastructure. The project further explores the conceptual framework of IoT, elucidating its transformative potential in revolutionizing urban infrastructure management. By seamlessly integrating physical devices with digital intelligence, IoT facilitates the creation of interconnected ecosystems wherein data-driven decision-making and automation converge to drive unprecedented efficiency and innovation

Automated GIS-based decision tool for rehabilitation of a drinking water infrastructure in Tlemcen, Algeria

Water resource management relies heavily on the utilization of decision-making systems to guide the strategic rehabilitation and renovation of infrastructure within water distribution networks (WDNs). This study aims to develop and apply a scripting tool in ArcGIS for decision-making in WDN. It combines a collection of decision support systems, including Geographic Information Systems (GIS) and multi-criteria analysis, for infrastructure renovation and rehabilitation. The research is divided into two main sections. The first focuses on creating and implementing the analytic hierarchy process tool in ArcGIS, while the second discusses its application in the study area, which is the drinking water distribution network of an urban area in Tlemcen, located in the Northwest of Algeria. The study’s results reveal that 25% of the network is in critical condition and require immediate rehabilitation. Sixty-two percentage of the network is classified as moderately urgent, indicating a pressing but less immediate need for intervention. Finally, the remaining 13% of the network requires attention over a longer timeframe for rehabilitation. The main criteria influencing pipe prioritization are material (26% of total weight), laying date (26% of total weight), and pressure (20% of total weight). This developed tool can be easily adapted and applied by engineers and water management officers. It serves as a guide for decision-makers and planners in urban water management in Tlemcen and can be replicated and applied to other areas worldwide.

Mapping urban green space of the 36 major cities in China using ZY-3 satellite images based on a feature robust to low-brightness pixels

Abstract. Urban green spaces (UGS), integral to modern urban life, play a pivotal role in shaping ecological, low-carbon, resilient, and livable cities. Understanding the microstructure and macro patterns of UGS is necessary but limited. In this context, we investigated the spatial patterns in the 36 major cities in China using Ziyuan-3 remote sensing images. We used a method for extracting UGS suitable for complex urban environments, and then selected 5 landscape pattern indices such as percent of landscape and patch density to analyse the spatial pattern of UGS in the 36 cities. The results show that: (1) The overall accuracy, recall, F1 value, and intersection over union of the extraction results of UGS were 94.62%, 94.11%, 94.36%, and 89.33% respectively; (2) Overall, the green space rate of the 36 cities exhibited a spatial distribution pattern of “high in the east and low in the west, low in the north and high in the south”; (3) Variations among cities were weak in terms of patch shape, but significant in patch density. The findings of this study cater to the escalating demands of urban planning and management.

PCINet: a Prototype- and Concept-based Interpretable Network for Mutli-scene Recognition

Abstract. With the development of remote sensing techniques, a large number of high-resolution aerial images is now available and benefit many applications. Multi-scene recognition plays a key role in applying remote sensing images to these applications, which refers to predicting multiple scenes coexisted in an aerial image and has attracted an increasing attention. Recently, most researchers tend to invent deep learning-based recognition models and has gained great achievements. However, few efforts have been deployed to explaining the success of deep neural networks in multi-scene recognition. To address this, we introduce concept bottleneck model (CBM) to interpreting model performance and propose a novel network, namely Prototype- and Concept-based Interpretable Network (PCINet), that projects aerial imagery into a prototype-concept memory bank and encode their correlations for explaining how a network can identify coexisting scenes in an aerial image. Specifically, the proposed network mainly consists of two branches: prototype matching that measures similarity scores between image features and scene prototypes, and concept bottleneck branches that aligned image features to textual embeddings and compute their relations with concept embeddings. Afterwards, Outputs are integrated for inferring scene categories. Experimental results show that the model enhances interpretability, providing valuable insights for urban planning and resource management, thereby bridging the gap between deep learning models and practical applications.

Smart urban management identification by integrating optical flow characteristics and HSV space

n smart cities, sanitation workers are the key to urban construction. Accurate targeting of sanitation workers can help managers better monitor and manage them. Pedestrian detection is the core and key component of object detection technology. The difficulty of pedestrian detection in the actual feature recognition is still how to quickly and accurately identify the identity in the complex video scene. To realize the effective detection of sanitation workers, the study designs an identity identification scheme conducive to the friendly management of smart urban management. Since the optical flow feature extraction method and HSV color space extraction method can not meet the actual detection efficiency, this study innovatively integrates the two methods to improve the detection accuracy of the mode. Meanwhile, the study also introduces PCA algorithm to identify the specific identity of sanitation workers. In the actual detection of sanitation workers, the identification rate of two sanitation workers is high, and the similarity is 98.61%. This technology greatly reduces the false detection rate of actual detection and improves the detection accuracy.

Informational Modeling of Cities: Method and Challenges for Institutional Implementation

ABSTRACT Although cities are increasingly permeated by information and communication technologies (ICTs), these urban centers still face theoretical and practical obstacles regarding the application of 3D technologies as a tool for urban planning and management. This study aims to ontologically structure an informational model as an urban management tool and assess the perceived challenges for implementing this model in the municipal public management of Curitiba, Brazil. The study was carried out through the collective subject discourse, the interviews’ thematic saturation, and Delphi techniques. The results indicate that the technical capacity, technological infrastructure, and financial resources for the development of a city information modeling (CIM) exist, although its activation for such development depends on a more significant political and managerial entrepreneurship in the decision-making in arrangements of multilevel governance, and, consequently, there is a need to develop public policies for staff training to stimulate and promote advanced technological innovations for urban management and planning. Implementing a CIM in large cities seems to face more management and planning problems due to the lack of institutional ontologies than technological, infrastructural, or knowledge limitations.

Urban amenity and urban economic resilience: evidence from China

Under the influence of multiple uncertain factors at home and abroad, urban amenities, as the underlying support for urban renewal activities, are of great significance in enhancing urban economic resilience. The panel data of Chinese cities from 2011 to 2019 is used in this study. Urban amenity is measured from artificial amenities and climate amenities, respectively. By using a two-way fixed effects model, we empirically test the impact of urban amenities on urban economic resilience. The key findings of this study are as follows. (1) Urban amenities can significantly enhance urban economic resilience. (2) Heterogeneity analysis shows that there are regional differences in the role of urban amenities in promoting urban economic resilience, with cities in the eastern region, strong environmental regulations, and high urbanization rates benefiting more. (3) We further find that urban amenities mainly enhance economic resilience by promoting population agglomeration, attracting labor migration, improving the quality of human capital, and stimulating urban innovation. Our conclusions recommend to rationally allocate and optimize urban amenity resources, strengthen urban planning and construction management, and create a more livable urban environment, thereby enhancing urban economic resilience.

Evaluating nickel removal efficacy of Filtralite under laboratory conditions: Implications for sustainable urban drainage systems

Our study deals with the critical issue of heavy metal pollution in urban runoff; we focused particularly on the threat posed by heavy metals such as nickel to living organisms and water resources owing to their toxicity at low concentrations and non-biodegradability. In this study, we evaluated the efficacy of Filtralite, a filtration medium with excellent hydraulic properties, in removing nickel from water through laboratory experiments and advanced simulation tools. We analysed the geochemical processes involved in nickel removal, including precipitation and adsorption. Importantly, we implemented an innovative approach for numerical modelling using HP1, by combining HYDRUS-1D and PHREEQC for modelling solute transport in porous media and geochemical reactions, respectively. The experimental results revealed that the Filtralite-laden column achieved a removal efficiency of 93.5 % for injected nickel. We observed a significant increase in the effluent pH from 7.0 to 11.5 during the interaction with Filtralite, affecting the solubility of the nickel phases. The model showed the highest nickel concentration in the initial column layers because of rapid chemical precipitation and adsorption upon contact with Filtralite. High-resolution scanning electron microscopy images confirmed the presence of amorphous precipitates around the soil particles, aligning with the changes in pH. This study contributes significantly to environmental engineering and urban water management by introducing an innovative numerical model in HP1 to accurately simulate the removal of nickel using Filtralite. These findings validate the efficiency of Filtralite in extracting nickel from aqueous solutions. Thus, Filtralite is a strong candidate for inclusion in sustainable urban drainage systems.

Spatio-temporal analysis of carbon storage in urban areas after wildfires: the case of Marmaris fire

Cities and urban areas are the primary source of CO2 worldwide by using around 70% of global energy and emitting more than 71% of CO2. Urban vegetation, referring to all trees and shrubs, are important components of urban environments. They provide many ecosystem services to human beings both directly and indirectly. Especially, they play a key role in reducing carbon emissions in urban areas by storing and capturing the carbon. However, recently, an increase in the number and intensity of wildfires that occur within urban areas has been observed. It resulted in losing stored carbon, releasing GHG to the atmosphere. Hence, quantifying above-ground carbon stored by urban trees and its distribution is essential to better understanding urban vegetation's role in urban environments and to better urban vegetation management. This study aimed to examine how forest fire affects the amount and distribution of stored carbon in the urban environment for the case of the Marmaris fire in the Summer of 2021 in Türkiye. For the study, urban forest carbon storage maps were generated before and after the Marmaris forest fire using remote sensing-based methodology with freely available remote sensing (RS) data. The results indicated that using the existing methodology could be rapid and cost-effective in monitoring the carbon storage change after an anthropogenic and natural disaster. However, for precise and reliable estimation of total carbon storage and the change in total urban carbon storage, the methodology needs to be developed at a local scale using field sampling along with RS data.

Advance Emergency and Traffic Management System

Urban Alert is an innovative Android application that addresses urban emergencies, traffic management, and safety concerns. The system has a strong accident or emergency feature that allows users to quickly capture and upload incident details with location information. The traffic management component uses advanced algorithms such as YOLO and CNN to monitor and analyze real- time traffic conditions through IoT-enabled cameras. The application optimizes ambulance routes for zero traffic, minimizing response times during emergencies. Furthermore, safety options allow users to send alerts to relevant departments based on their specific needs. This paper examines the technical implementation, results, and evaluation of each feature, demonstrating how they can impact urban safety and response efficiency. Urban Alert is a comprehensive solution that enhances emergency response, traffic management, and public safety in urban areas. Keywords – Emergency Response, Traffic Management, IoT and AI Integration, Ambulance Routing, Safety Options, Real-time Incident Monitoring.

Local and catchment-scale effects of water retention measures at Lake Velence

AbstractClimate change manifested its adverse impacts last year, with an extreme drought leading to a drastically low water level in Lake Velence, Hungary. Nature-based solutions have the potential to alleviate these impacts locally. While a few initiatives have been implemented in Hungary, widespread adoption of these solutions is expected to be a goal for the more distant future.This research focuses on one catchment at Lake Velence to evaluate decision-maker's readiness and urban water management infrastructure for broadly implementing nature-based solutions. Methods include delineating the stormwater system and creating a numerical model to evaluate rainfall-runoff processes and the possible impacts of nature-based retentions. Surveys among local mayors were conducted to assess their perception of existing water infrastructures and implementations of nature-based solutions. Its widespread use may become significant, but its effect on the lake's water level remains negligible.

Multi-Scenario analysis of rooftop greening regulation on runoff effects based on adaptive Evaluation: A case study of Macau, China

The reduction in internal ecological spaces within urban environments and the expansion of impervious surfaces have been identified as factors significantly increasing the susceptibility of cities to flood disasters. Rooftop greening, an essential component of urban stormwater management strategies, offers a versatile solution for augmenting permeable surfaces and delivering ecological services while seamlessly integrating with existing urban infrastructure. This approach has been proven effective in enhancing urban resilience against rainfall-induced flooding events. Hence, the precise identification of suitable building rooftops for green infrastructure within urbanized regions, combined with the simulation of runoff regulation effects brought about by these rooftop greening interventions, holds substantial practical significance. This study innovatively constructs a technical method for measuring the runoff control performance of urban-scale green roofs from identification, evaluation to simulation. Taking the Macao Special Administrative Region as an example, a green roof suitability evaluation index system that is consistent with urban characteristics has been constructed, integrating machine learning, comprehensive evaluation, SWMM simulation methods to evaluate the suitability of roof greening in urban areas and simulate the impact of multi-senarios green roof construction types on runoff regulation. Finally, a comprehensive strategy for rooftop greening construction in Macau is proposed, with the aim of amplifying the runoff regulation capabilities of such initiatives. The main findings of the research include the following: (1) The research area exhibits a notably high aggregate potential for rooftop greening, with suitable rooftop areas comprising approximately 34% of the total rooftop expanse. Notably, construction vintage and roof elevation are identified as the most substantive constraints on rooftop greening. (2) Discernible differences in rooftop greening construction scale exert a significant influence on runoff regulation capacity, with the magnitude of greening positively correlated with its runoff regulation efficacy. Distinct greening typologies elicit varying impacts on runoff regulation, with semi-intensive rooftop greening surpassing extensive approaches in runoff regulation performance. Disparate distribution patterns of rooftop greening engender dissimilar runoff regulation effects, with rooftop greening layouts situated distant from drainage outlets facilitating runoff mitigation, while those in close proximity to drainage outlets enhance peak flow reduction effects.

Underpass Water Logging System Using IOT and ML

In recent years, the surge in population growth has exacerbated numerous challenges for both humans and the environment, including issues like inadequate infrastructure and frequent occurrences of waterlogging in underpasses. During heavy rainfall, underpasses often become inundated, posing significant risks to passing vehicles and exacerbating traffic congestion. This is primarily due to ineffective drainage systems and flawed designs for diverting rainwater away from the underpasses. To address these pressing issues, this paper proposes an IoT-based solution aimed at monitoring and managing heavy traffic within underpasses. The primary objective is to establish a comprehensive framework capable of continuously monitoring the water levels within underpasses. By leveraging IoT technology, this framework can activate warning signs and implement traffic control measures in real-time to mitigate the risks posed by waterlogging and alleviate traffic congestion. This innovative approach not only offers a proactive solution to the immediate challenges faced by underpasses but also lays the foundation for a more sustainable and efficient urban infrastructure management system. Through the integration of IoT sensors and intelligent control mechanisms, this framework promises to enhance the resilience of underpasses to adverse weather conditions while optimizing traffic flow, ultimately contributing to safer and more livable urban environments . Keywords — Internet of Things, sensors, Wi-Fi module, LCD, cloud architecture.

Improving the Efficiency and Environmental Friendliness of Urban Stormwater Management by Enhancing the Water Filtration Model in Rain Gardens

Rain gardens are used to solve urban problems related to the negative impact of stormwater. (1) Scientific contributions from different countries provide general guidelines for the design and operation of rain gardens in different geographical areas. Given the small spatial scale of rain gardens, the use of existing infiltration models often leads to design errors. (2) The purpose of this paper is to develop a hydrological model by introducing a system of equations that extends the ability to calculate the rate, flow rate and time of saturation of layers with moisture and rainwater leakage from the rain garden system. (3) The results obtained allow us to describe the dynamic processes of passage and saturation of layers of the rain garden at a certain point in time, which extends the ability to calculate the flow rate. It was established that the smaller the area of the rain garden compared to the area of the catchment basin, the faster it reaches its full saturation. Increasing the thickness of the rain garden layers allows for an increase in the efficiency of water retention at a lower value of the area ratio. (4) The practical significance of the results obtained is especially important for the correct description of hydrodynamics in the system and determining the optimal conditions for the effective functioning and management of the rain garden structure for any climatic region.

Reflections on the Application of Digital Design in Future Urban Construction

As globalization deepens, it brings new opportunities for urban construction. With the acceleration of urban planning processes and the pressures of population growth, higher demands are placed on urban construction. At the same time, rapid developments in science and technology provide new ideas and directions for urban construction. Digital design, as an emerging design concept and technical method, is becoming an important support for future urban construction. By utilizing advanced technologies such as big data, cloud computing, and artificial intelligence, digital design can achieve comprehensive optimization of urban planning, architectural design and construction, and urban management and services, bringing revolutionary changes to urban construction. Therefore, this paper explains the technological basis of digital design in current urban construction to provide a background for understanding its application. It then delves into the specific application advantages of digital design in three aspects: urban planning, architectural design and construction, and urban management and services. However, digital design still faces multiple challenges in application, such as technology, security, and privacy issues, which need to be addressed through corresponding strategies. Through in-depth research and practice, it is believed that digital design will bring more sustainable solutions to future urban construction.

Digital Image Shaping of Micro-celebrity City: Brand Building and Communication of Chongqing City Image on the Platform of TikTok

This paper aims to discuss how the social media platform represented by TikTok influences and shapes the micro-celebrity city in the digital era. This paper takes Chongqing as the research object. Through in-depth analysis and interviews, it was found that Chongqing is very popular on TikTok because of its unique three-dimensional traffic, magical architecture, and food culture. These elements form a unique visual symbol of Chongqing through creative digital image expression and social interaction, attracting many tourists and deepening people’s impression and comprehension of the city. This study reveals the crucial role of digital images in shaping the image of contemporary cities and provides new perspectives and strategic suggestions for urban brand management and social media marketing. Through an in-depth discussion of content creation, communication mechanisms, and user interaction on platforms such as TikTok, this study provides valuable insights and references for city image communication and city brand building.