Application of Decision-Making Methods in Smart City Projects: A Systematic Literature Review

Hoang, Giang Tran Thi; Dupont, Laurent; Camargo, Mauricio

doi:10.3390/smartcities2030027

Cited by 55 publications

(28 citation statements)

References 110 publications

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“…Human experts often make the decision based on data coming to any application. Decision making in the context of smart cities became more challenging because of the information available and the involvement of multiple stakeholders [25].…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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Traditional Artificial Intelligence (AI) technologies used in developing smart cities solutions, Machine Learning (ML) and recently Deep Learning (DL), rely more on utilising best representative training datasets and features engineering and less on the available domain expertise. We argue that such an approach to solution development makes the outcome of solutions less explainable, i.e., it is often not possible to explain the results of the model. There is a growing concern among policymakers in cities with this lack of explainability of AI solutions, and this is considered a major hindrance in the wider acceptability and trust in such AI-based solutions. In this work, we survey the concept of ‘explainable deep learning’ as a subset of the ‘explainable AI’ problem and propose a new solution using Semantic Web technologies, demonstrated with a smart cities flood monitoring application in the context of a European Commission-funded project. Monitoring of gullies and drainage in crucial geographical areas susceptible to flooding issues is an important aspect of any flood monitoring solution. Typical solutions for this problem involve the use of cameras to capture images showing the affected areas in real-time with different objects such as leaves, plastic bottles etc., and building a DL-based classifier to detect such objects and classify blockages based on the presence and coverage of these objects in the images. In this work, we uniquely propose an Explainable AI solution using DL and Semantic Web technologies to build a hybrid classifier. In this hybrid classifier, the DL component detects object presence and coverage level and semantic rules designed with close consultation with experts carry out the classification. By using the expert knowledge in the flooding context, our hybrid classifier provides the flexibility on categorising the image using objects and their coverage relationships. The experimental results demonstrated with a real-world use case showed that this hybrid approach of image classification has on average 11% improvement (F-Measure) in image classification performance compared to DL-only classifier. It also has the distinct advantage of integrating experts’ knowledge on defining the decision-making rules to represent the complex circumstances and using such knowledge to explain the results.

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Section: Literature Reviewmentioning

confidence: 99%

Explainable Artificial Intelligence for Developing Smart Cities Solutions

et al. 2020

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“…As argued by Caragliu, Del Bo, and Nijkamp [17], these early initiatives were heterogeneous, unfocused, had limited effectiveness, and impacted a limited number of people. However, in the present-day scenario, technological advancement is seen as providing new ways for decision-makers and planners to tackle immediate urban issues [18], such as traffic congestion, the lack of affordable housing, inefficient utility services, and poor health care. In addition, recent SC studies also provide several frameworks to assess these smart solutions when responding to pressing urban issues [19,20].…”

Section: Review Of Smart City Definitions and Practicesmentioning

confidence: 99%

Understanding the What, Why, and How of Becoming a Smart City: Experiences from Kakinada and Kanpur

Gupta

Hall

2020

Smart Cities

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Rapid urbanization can result in challenges, such as overcrowding, congestion, and a lack of urban services. To address these challenges, an increasing number of communities are exploring the concept of a smart city (SC). Although rapid urbanization is a problem for cities around the world, its consequences can be severe for those located in developing nations. While previous studies have focused on SCs that were built from the ground up, there is a critical need for studies that focus on how to advance SC initiatives in developing regions faced with limited land and resources. This study identified two proposed SCs in India-Kakinada and Kanpur-which are currently implementing SC projects to explore their SC transformation. This case study aims to explore how "smartness" is understood in these cities and examines the local conditions shaping SC objectives by studying the existing issues in the cities, the proposed projects, and the perception of SC experts on a) what they understand by "smartness"; b) why cities want to become smart; and c) how they will become smart. The study findings indicate that although the high-level goals of the proposed SCs in India are similar to those of existing SCs in developed nations, the underlying objectives and strategies vary and are shaped by the urbanization challenges facing the Indian cities. This research also highlights the key questions a SC planning effort should address, especially in a developing nation context.

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“…Although the dynamic nature of stakeholders (including governments, industry, academia and communities) across different sectors and scales has for long been a decision-making challenge [115], the notion of multi-stakeholder engagement has recently gained increasing attention for bridging the science-policy gaps. Several best practices have emerged around the world for engaging local stakeholders to enable integrated resource management.…”

Section: Enabling Multi-stakeholder Engagement For Transboundary Cooperationmentioning

confidence: 99%

Role of Smart Cities in Optimizing Water-Energy-Food Nexus: Opportunities in Nagpur, India

et al. 2020

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The evolving concept of ‘Smart Cities’ (SC) is today gaining global momentum in scientific and policy arenas. With the rising potential for jobs and improved lifestyle, these fast-growing cities are witnessing an ever-increasing concentration of populations and economic activities. However, the core aspect of sustainability is often overshadowed in SC by the components of economic growth. In view of the rising water, energy, and food (collectively referred to as WEF) demands in cities and the interlinkages between WEF systems, this study aims to highlight the role of SC in enhancing WEF resource efficiency from a nexus perspective. To study the current trends of SC developments first, a specific case of proposed Nagpur smart city (India) is reviewed based on document analysis and preliminary discussions with local government officials. Thereafter, bibliometric analysis (based on Scopus data) is conducted to establish a comprehensive understanding of existing SC research in varying domains of interlinked WEF systems. While the proposed Nagpur smart city is observably focused on city-centric goals with a high emphasis on infrastructure development, this study underlines the need for environmental resource conservation at a transboundary level. The key opportunities for optimizing the WEF nexus in Nagpur are then highlighted in reference to the bibliometric analysis.

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Application of Decision-Making Methods in Smart City Projects: A Systematic Literature Review

Cited by 55 publications

References 110 publications

Explainable Artificial Intelligence for Developing Smart Cities Solutions

Explainable Artificial Intelligence for Developing Smart Cities Solutions

Understanding the What, Why, and How of Becoming a Smart City: Experiences from Kakinada and Kanpur

Role of Smart Cities in Optimizing Water-Energy-Food Nexus: Opportunities in Nagpur, India

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