Urban Infrastructure Management Research Articles

THE CONCEPT OF THE SMART CITY AS AN ALTERNATIVE APPROACH TO RESTORE THE URBAN INFRASTRUCTURE OF UKRAINE DURING THE POST-WAR PERIOD

Modern information technologies in the world are developing at a very fast pace and gradually cover all spheres of human life. This greatly facilitates and improves a person's life, fills it with new meaning. This is how smart cities are built in developed countries. These are approaches to the management of urban infrastructure, where innovations and breakthrough information technologies allow citizens not only to use resources economically, but also to actively participate in the life and development of their own city. This allows to form a strategy for a successful and competitive future of the city. To date, the concept of a smart city for Ukraine is not the most relevant due to the fact that the country is in the active phase of the war. However, in order to speed up the country's recovery processes in the post-war period, one should reasonably approach the choice of the development strategy that will become the basis for restoring the Ukrainian economy in the near future. A deeper study of the essence of the concept of a smart city has led to the conclusion that this approach can be saving for the weakened economy of Ukraine and create conditions for its future rapid development.
 The paper considers the content of the concept of a smart city and its evolution over time. It has been established that the main components of the successful implementation of the concept of a smart city are the economy, mobility, environment, human capital, life potential and management. The essence of each of the six selected components is detailed. Based on a study of the goals, content and best practices for implementing the concept of a smart city within such developed cities of the world as New York, Amsterdam, Barcelona and Shanghai, the features of the functioning of successful smart cities in the world are determined. The most successful smart city projects are given, which can become guidelines for the restoration of Ukrainian cities. Taking into account the current global trends in the construction of smart cities and taking into account the Ukrainian geopolitical specifics, the key directions for the restoration of Ukrainian cities in the post-war period are formulated. These areas include security, infrastructure, mobility, healthy life, information and ecology.

Ensembled masked graph autoencoders for link anomaly detection in a road network considering spatiotemporal features

Road anomaly detection aims to find a small group of roads that are exceptional with respect to the rest of the physical links in a transportation network, posing great challenges for spatial data mining and urban infrastructure management applications. Road links involve complex features from both spatial and temporal perspectives, but existing detection methods mostly rely on the topological features of road networks because they face difficulty in capturing the spatial and temporal characteristics of road networks. To overcome this difficulty, we constructed an ensembled autoencoder framework for road anomaly detection by combining spatiotemporal graph learning and graph masking methods, and our main focus was to effectively detect the topological and attribute anomalies mapped by spatiotemporal features in a road network based on traffic flow data. To model local contexts, we performed random masking on the input graph to obtain heterogeneous masked subgraphs. On this basis, a spatiotemporal graph learning model was used to extract topological and traffic flow features from masked subgraphs. Then, we carried out feature reconstructions for each subgraph and calculated the anomaly scores for every node in the graph. Finally, an ensemble model was designed to integrate the anomaly detection results from multiple autoencoders that were trained on the subgraphs to improve feature learning. Our experimental results show that the proposed method is superior to traditional topological analysis methods, machine learning methods (DBScan, isolation forest, LOF, and one-class SVM), and recent deep learning methods (GAE and VGAE).

Urban Domestic Gardens: Characteristics and Contributions to Urban Green Sustainability

Domestic gardens (e.g., home, allotment) are important features of the urban green infrastructure (GI) and represent significant component of the urban landscape. The present work aimed to evaluate the overall role and benefits of urban allotments and home gardens for the urban GI in the Global North countries. A literature review was performed to identify published evidence from 2000 onwards on the impact of domestic gardens for GI sustainability within developed countries. A narrative synthesis was used to interpret available research pertaining to urban domestic gardens per se, to define their unique role in GI and to highlight areas in need of further research. The review suggested key differences in both nature and management of urban domestic gardens with major implications for their contribution to GI sustainability. We also acknowledged the heterogeneous social, cultural, ecological, and economic contexts underlying the impact of urban domestic gardens for GI. Further, urban domestic gardens also act as a source of food security and provide additional ecosystem services. The review concluded that home and allotment gardens are important contributors to urban GI sustainability and should be incorporated within the planning, design and management of urban infrastructures.

Building an Augmented Reality Experience on Top of a Smart Pavement Management System

Pavement Management Systems (PMS) offers a systematic collection, storage, analysis, and modeling of road condition data to optimize resources across a road network. Adding artificial intelligence (AI) and augmented reality (AR) to PMS could improve their technical or visual aspects. This paper tries to identify a method to improve the understanding of the consequences of the city council’s decisions in the urban pavement management system field. This paper establishes the potential of AR. It provides future maintenance and rehabilitation (M&R) actions needed based on the recommendation of the future distress in the study area. The road cracks are discovered through technical analysis, and a CityEngine model is established based on the PMS results. Additionally, in terms of visualization, this paper’s unique feature delivers the result as an AR experience. Applying the Unity game engine and importing the built CityEngine model and the embedded textures as input empowered us to provide a dynamic product in terms of data and analysis and a real-time Decision Support System (DSS) for the final users. This paper concludes that researchers need many different modules to design and implement an efficient PMS to move toward a smart PMS. The smart city concept is meaningless without a tight collaboration between all distinctive parts of each urban infrastructure management system. Additionally, this paper attempts to provide answers for researchers and an outlook for future research, the development of the proposed method, and its application in other fields

Scenario Analysis for Resilient Urban Green Infrastructure

With the advancement of urbanization, the stress on the green infrastructure around the urban agglomeration has intensified, which causes severe ecological problems. The uncertainty of urban growth makes it difficult to achieve effective protection only by setting protection red lines and other rigid measures. It is of practical significance to optimize the resilience of the stressed green infrastructure. To this end, we explore a scenario simulation analysis method for the resilience management of green infrastructure under stress. This research applies artificial neural network cellular automata to simulate the impacts of the Chang-Zhu-Tan urban agglomeration expansion on the green infrastructure in 2030 in three scenarios: no planning control, urban planning control, and ecological protection planning control. Based on the analysis, we identify four green infrastructure areas under stress and formulate resilience management measures, respectively. The results show that: (1) The distribution pattern of green infrastructure under stress is different in three scenarios. Even in the scenario of ecological protection planning and control, urban growth can easily break through the ecological protection boundary; (2) Residential, industrial, and traffic facility land are the main types of urban land causing green infrastructure stress, while forest, shrub, and wetland are the main types of the stressed green infrastructure; (3) Efficient protection of green infrastructure and the management of the urban growth boundary should be promoted by resilient management measures such as urban planning adjustment, regulatory detailed planning, development strength control and setting up the ecological protection facilities for the stressed green infrastructure areas of the planning scenarios and the no-planning control scenarios, for the areas to be occupied by urban land, and for the important ecological corridors. The results of this study provide an empirical foundation for formulating policies and the methods of this study can be applied to urban ecological planning and green infrastructure management practice in other areas as well.

Automatic Detection and Assessment of Pavement Marking Defects with Street View Imagery at the City Scale

Pavement markings could wear out before their expected service life expires, causing traffic safety hazards. However, assessing pavement-marking conditions at the city scale was a great challenge in previous studies. In this article, we advance the method of detecting and evaluating pavement-marking defects at the city scale with Baidu Street View (BSV) images, using a case study in Nanjing. Specifically, we employ inverse perspective mapping (IPM) and a deep learning-based approach to pavement-marking extraction to make efficient use of street-view imageries. In addition, we propose an evaluation system to assess three types of pavement-marking defects, with quantitative and qualitative results provided for each image. Factors causing pavement-marking defects are discussed by mapping the spatial distribution of pavement-marking defects at the city scale. Our proposed methods are conducive to pavement-marking repair operations. Beyond this, this article can contribute to smart urbanism development by creating a new road maintenance solution and ensuring the large-scale realization of intelligent decision-making in urban infrastructure management.

Advanced Techniques for Monitoring and Management of Urban Water Infrastructures—An Overview

Water supply systems are essential for a modern society. This article presents an overview of the latest research related to information and communication technology systems for water resource monitoring, control and management. The main objective of our review is to show how emerging technologies offer support for smart administration of water infrastructures. The paper covers research results related to smart cities, smart water monitoring, big data, data analysis and decision support. Our evaluation reveals that there are many possible solutions generated through combinations of advanced methods. Emerging technologies open new possibilities for including new functionalities such as social involvement in water resource management. This review offers support for researchers in the area of water monitoring and management to identify useful models and technologies for designing better solutions.

A clearance pipeline-based method for street tree maintenance information detection using MLS data

Street trees provide a multitude of benefits but may cause hidden dangers to traffic safety under inaccurate maintenance. Mobile laser scanning (MLS) system can efficiently provide high-precision urban environment information. This article aims to develop a clearance pipeline-based method to obtain street tree maintenance information by analysing the spatial relationship between street trees and the clearance pipeline using MLS data. Our method consists of two steps: pipeline construction and maintenance information detection. The proposed approach can map street tree maintenance information and serve as an urban management tool. An urban block with a total length of 6.4 km was selected as the case area. The results indicated our method can effectively detect obstructive objects and pruning the street trees identified by our method can effectively improve the visibility of traffic signs. Our study shows the potential of MLS data in urban facilities management and aims to provide a safer road environment. Highlights A clearance pipeline-based method for detecting street tree maintenance information is developed. Three indexes derived from MLS data for quantitative analysis of the impact of maintenance information are defined. The thematic map has been drawn to assist the urban infrastructure management department.

Challenges, drivers, and benefits to integrated infrastructure management of water, wastewater, stormwater and transportation systems

Infrastructure systems are known to be interdependent systems; however, infrastructure systems are typically managed individually by agencies in charge of these systems. The existing literature has proposed the concept of an integrated infrastructure management approach, yet this literature has focused on managing infrastructure systems in short-term contexts (e.g., response to natural and human-made disasters). As such, limited understanding exists about implementing an integrated infrastructure management approach in the long-term (i.e., maintenance and rehabilitation). This study aims to explore the challenges, drivers, and benefits of implementing an integrated urban infrastructure management approach by agencies managing urban water, wastewater, stormwater, and transportation infrastructure systems in the long-term. This study qualitatively analyzed 15 semi-structured interviews with stakeholders in the management of infrastructure systems in Chile. It was found that the implementation of an integrated infrastructure management approach is challenged and driven by organizational instead of technical aspects. It was found that the local context where agencies operate represents a challenge for the successful implementation, the willingness to collaborate of infrastructure agencies’ workers represents a driver to implementation, and that benefits encompass agencies and users. Infrastructure agencies can use our findings to account for contextual conditions to successfully implement an integrated infrastructure management approach.

Data-driven decision management of urban underground infrastructure through groundwater-level time-series cluster analysis: the case of Milan (Italy)

The significant increase in urbanization has resulted in greater use of the subsurface in urban planning and, therefore, increased interaction between groundwater and underground infrastructure. Numerical models are the primary tool adopted to manage the resulting problems; however, their construction is time- and cost-consuming. Groundwater-level time-series analysis can be a complementary method, as this data-driven approach does not require an extensive understanding of the geological and boundary conditions, even if providing insights into the hydrogeologic behaviour. Thus, a data-driven approach was adopted to analyse groundwater time-series of the shallow aquifer, occupied by several underground structures, beneath Milan city (Northern Italy). Statistical (Mann-Kendall and Sen’s slope estimator, autocorrelation and cross-correlation, hierarchical cluster analysis) and geospatial techniques were used to detect the potential variables influencing the groundwater levels of 95 monitoring wells, covering the period 2005–2019. A general rising trend of the water table was identified, with local hydrogeologic differences in the western and southernmost areas. Based on time-series analysis results, four management areas have been identified. These areas could act as future geographic units with specific groundwater management strategies. In particular, subsurface public car parks can be classified with respect to groundwater flooding as (1) not submerged, (2) possibly critical, or (3) submerged at different groundwater conditions. According to these outcomes, targeted guidelines for constructing new car parks have been elaborated for each management area. The methodology proved to be efficient in improving the urban conceptual model and helping stakeholders design the planned underground development, considering groundwater aspects.

Automatic modelling of urban subsurface with ground-penetrating radar using multi-agent classification method

ABSTRACT The subsurface of urban cities is becoming increasingly congested. In-time records of subsurface structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the ground. Ground-penetrating radar (GPR) is a nondestructive testing method that can survey and image the subsurface without excavation. However, the interpretation of GPR relies on the operator’s experience. An automatic workflow was proposed for recognizing and classifying subsurface structures with GPR using computer vision and machine learning techniques. The workflow comprises three stages: first, full-cover GPR measurements are processed to form the C-scans; second, the abnormal areas are extracted from the full-cover C-scans with coefficient of variation-active contour model (CV-ACM); finally, the extracted segments are recognized and classified from the corresponding B-scans with aggregate channel feature (ACF) to produce a semantic map. The selected computer vision methods were validated by a controlled test in the laboratory, and the entire workflow was evaluated with a real, on-site case study. The results of the controlled and on-site case were both promising. This study establishes the necessity of a full-cover 3D GPR survey, illustrating the feasibility of integrating advanced computer vision techniques to analyze a large amount of 3D GPR survey data, and paves the way for automating subsurface modeling with GPR.

Appropriate urban infrastructure management strategies against floods from the perspective of passive defense using SWOT and QSPM (Case study: Kashan City)

Appropriate urban infrastructure management strategies against floods from the perspective of passive defense using SWOT and QSPM (Case study: Kashan City)

Adaptive Road Configurations for Improved Autonomous Vehicle-Pedestrian Interactions Using Reinforcement Learning

The deployment of Autonomous Vehicles (AVs) poses considerable challenges and unique opportunities for the design and management of future urban road infrastructure. In light of this disruptive transformation, the Right-Of-Way (ROW) composition of road space has the potential to be renewed. Design approaches and intelligent control models have been proposed to address this problem, but we lack an operational framework that can dynamically generate ROW plans for AVs and pedestrians in response to real-time demand. Based on microscopic traffic simulation, this study explores Reinforcement Learning (RL) methods for evolving ROW compositions. We implement a centralised paradigm and a distributive learning paradigm to separately perform the dynamic control on several road network configurations. Experimental results indicate that the algorithms have the potential to improve traffic flow efficiency and allocate more space for pedestrians. Furthermore, the distributive learning algorithm outperforms its centralised counterpart regarding computational cost (49.55\%), benchmark rewards (25.35\%), best cumulative rewards (24.58\%), optimal actions (13.49\%) and rate of convergence. This novel road management technique could potentially contribute to the flow-adaptive and active mobility-friendly streets in the AVs era.

IoT-Enabled Smart Waste Management Systems for Smart Cities: A Systematic Review

With urbanization, rising income and consumption, the production of waste increases. One of the most important directions in the field of sustainable development is the design and implementation of monitoring and management systems for waste collection and removal. Smart waste management (SWM) involves for example collection and analytics of data from sensors on smart garbage bins (SGBs), management of waste trucks and urban infrastructure; planning and optimization of waste truck routes; etc. The purpose of this paper is to provide a comprehensive overview of the existing research in the field of systems, applications, and approaches vis-à-vis the collection and processing of solid waste in SWM systems. To achieve this objective, we performed a systematic literature review. This study consists of 173 primary studies selected for analysis and data extraction from the 3,732 initially retrieved studies from 5 databases. We 1) identified the main approaches and services that are applied in the city and SGB-level SWM systems, 2) listed sensors and actuators and analyzed their application in various types of SWM systems, 3) listed the direct and indirect stakeholders of the SWM systems, 4) identified the types of data shared between the SWM systems and stakeholders, and 5) identified the main promising directions and research gaps in the field of SWM systems. Based on an analysis of the existing approaches, technologies, and services, we developed recommendations for the implementation of city-level and SGB-level SWM systems.

An Urban Water Infrastructure Management System Design with Storm Water Intervention for Smart Cities

An Urban Water Infrastructure Management System Design with Storm Water Intervention for Smart Cities

Smart City: A Mobility Technology Adoption Framework Incorporating Surface-Level Technical Analysis

Rising urban population, aging infrastructure, and increasing capital maintenance costs call for more efficient use of limited available resources. To address these concerns, the use of technology for urban infrastructure management and operational efficiency comes naturally with emerging technological advancements. Although there have been analyses on how to conceptually design a smart city from the ground up, they are often less applicable in transforming existing cities into smart ones. Retrofitting existing infrastructures requires integration and synergies with existing systems. Given the broad scope of smart cities, this paper equips planners with surface-level considerations in adopting smart mobility solutions. This provides an avenue to assess project feasibility, risk management, and investment requirement. The process is presented via a replicable framework with a use case with simplistic approaches that do not require complex constraints or modeling. The framework streamlines how to deduce a feasible set of user-centric smart solutions, which are then ranked according to their impacts for implementation priority. Middle East Technical University campus located in Ankara (Turkey) is considered for the use case. The main outcomes for the use case are deducing high-impact smart solutions based on the proposed framework. Preliminary system design analyses are showcased for three high-ranked solutions: electric vehicle charging station installation and investment optimization, autonomous electric shuttle system design, and bus network electrification strategies.

Framework for incorporating climate projections in the integrated planning and management of urban infrastructure

The impact of climate change on the management and planning of coastal urban areas has become a worldwide issue and has been the subject of several studies in recent decades. Climate change projection in the design phase of urban planning allows building capacity for urban adaptation and avoids future economic costs. Many coastal communities still keep reactive adaptation responses due to a lack of knowledge about how to incorporate this information in planning. In this sense, this paper presents a modelling framework to guide the incorporation of climate projections in the planning and management of coastal areas. The framework was applied in a coastal city of Brazil, where high environmental vulnerability and constantly coastal flooding are observed. The results indicate that there is no significant difference between the emission scenarios, in the short and medium-term, for precipitation projections and runoff peak flow. Extreme rainfall projections give differences up to 21% between the historical series and the projection for 2100. For the sea level projection, the extent of flooded areas induced by the worst emission scenario until the end of the century will reach 10% of the total municipally area (characterized by ecologically sensitive areas). In general, the framework provides preliminary information that can be used to improve spatial planning in a local context. Given the several simplified assumptions, the results should be used with caution, serving as a baseline for future planning efforts in the search for resilient cities.

Open-Source Application for Water Supply System Management: Implementation in a Water Transmission System in Southern Spain

Sustainable water use has become a critical issue for the future of the planet in face of highly probable climate change. The drinking water supply sector has made significant progress over the last 20 years, although improvements in the management of urban hydraulic infrastructures are still required. The proposed system, Integrated Tool for Water Supply Systems Management (ITWSM), built on three interconnected modules (QGIS database, Epanet hydraulic model, and Google My Maps app), was developed on open-source software. The core of ITWSM allows analyzing the behavior of water supply systems under several operation/failure scenarios. It facilitates decision making supported by the mobile application ITWSM-app. Information flows easily through the different decision levels involved in the management process, keeping updated the georeferenced database after system changes. ITWSM has been implemented in a real public water supply company and applied to manage breakdown repairs in water transmission systems. The use of the proposed methodology reduces the average cost of failure repair by 13.6%, mainly due to the optimal planning of the resources involved.

Identification of Interruptions in Urban Drainage Systems and Their Sustainable Solutions for Alleviating Flood Risk in Mumbai, an Indian Megacity

Indian cities, in recent times, have been grappling with pressing natural disasters such as urban flooding and the dichotomy of acute water shortage. 24x7 water supply is a distant dream for the authorities involved. With an ever-increasing population leading to greater water demands, stormwater is increasingly considered an ‘asset,’ a resource that needs to be harvested. Mumbai, India’s financial capital, being prone to chronic flooding, is a colossal problem for the country’s economy. This review paper aims to identify direct and indirect interruptions in the stormwater drainage system of Mumbai, which in turn is leading to chronic flooding issues. This research identifies causative factors for flooding and observes that dilapidated and old-fashioned drainage systems are major hindrances in flood-free Mumbai. The traditional approach to urban infrastructure management needs to be reviewed and re-imagined. The identified interruptions in the drainage system were, thus, studied in detail to finally suggest some sustainable solutions, based on analysis and learnings from international success stories of cities having similar problems.

Disaster vulnerability analysis of small towns in Brazil

Most of the global urban population lives in cities with less than one million people, whereas in Brazil, more than 45% of the population is estimated to live in cities with up to 100,000 people. As in both the global and Brazilian contexts, many of these people live in disaster-prone areas, there has been an increasing demand for research on small cities to subsidize actions contributing to disaster risk reduction. Thus, this study evaluates the vulnerability and capacities of small Brazilian municipalities to reduce risks, especially those related to landslides and floods. To this end, 234 municipalities located in the southern and southeast regions of the country were classified into two population classes, that is, cities from 20,000 to 50,000 people and those from 50,000 to 100,000 people. These municipalities are characterized by high levels of municipal human development and availability of municipal master plans. Statistical analysis of a set of quantitative and qualitative indicators revealed that the vulnerability of populations and municipal capacities is mainly related to the economic sectors, public policies, and city size. Municipal master plans and high levels of municipal human development guarantee neither better urban infrastructure nor specific risk management legislations. Although there are legal tools recommending disaster risk management policies, such tools are insufficient to reduce vulnerabilities and increase capacities in small Brazilian cities.