Pub Date : 2023-09-20DOI: 10.3390/smartcities6050112
Mohammed Itair, Isam Shahrour, Ihab Hijazi
This paper strives to enhance the inclusivity of urban public spaces, which play a crucial role in providing essential services for all citizens, including community building, physical and mental well-being, social interaction, civic engagement, citizen participation, and economic vitality. Despite the importance of these spaces, as recognized by the UN’s 2030 sustainability goals, the 2023 UN sustainable development report and scholars have drawn attention to their low availability, particularly for low-income individuals, women, children, and people with disabilities. To improve the inclusivity of public spaces, this paper offers the following contributions. (i) The establishment of a comprehensive framework for assessing public space inclusivity. This framework incorporates eight indicators: spatial distribution, typology, facilities and services, green and humid areas, governance and management, safety, user categories, and user satisfaction. (ii) The utilization of the framework to assess the inclusivity of public spaces in Nablus, a major Palestinian city. This assessment confirms the observations made by the UN and scholars regarding the low inclusivity of public spaces; in particular, a lack of public space, poor spatial distribution, and user dissatisfaction with safety conditions and services. (iii) The introduction of the concept of smart public space, which involves citizens in the governance of this space and leverages smart technology for monitoring, providing real-time information and services to citizens, improving facility efficiency, and creating an eco-friendly environment that preserves resources and biodiversity. By addressing these aspects, this paper enhances inclusivity. It promotes the development of an urban public space that caters to the diverse needs of the community, fostering a sense of belonging and well-being for all.
{"title":"The Use of the Smart Technology for Creating an Inclusive Urban Public Space","authors":"Mohammed Itair, Isam Shahrour, Ihab Hijazi","doi":"10.3390/smartcities6050112","DOIUrl":"https://doi.org/10.3390/smartcities6050112","url":null,"abstract":"This paper strives to enhance the inclusivity of urban public spaces, which play a crucial role in providing essential services for all citizens, including community building, physical and mental well-being, social interaction, civic engagement, citizen participation, and economic vitality. Despite the importance of these spaces, as recognized by the UN’s 2030 sustainability goals, the 2023 UN sustainable development report and scholars have drawn attention to their low availability, particularly for low-income individuals, women, children, and people with disabilities. To improve the inclusivity of public spaces, this paper offers the following contributions. (i) The establishment of a comprehensive framework for assessing public space inclusivity. This framework incorporates eight indicators: spatial distribution, typology, facilities and services, green and humid areas, governance and management, safety, user categories, and user satisfaction. (ii) The utilization of the framework to assess the inclusivity of public spaces in Nablus, a major Palestinian city. This assessment confirms the observations made by the UN and scholars regarding the low inclusivity of public spaces; in particular, a lack of public space, poor spatial distribution, and user dissatisfaction with safety conditions and services. (iii) The introduction of the concept of smart public space, which involves citizens in the governance of this space and leverages smart technology for monitoring, providing real-time information and services to citizens, improving facility efficiency, and creating an eco-friendly environment that preserves resources and biodiversity. By addressing these aspects, this paper enhances inclusivity. It promotes the development of an urban public space that caters to the diverse needs of the community, fostering a sense of belonging and well-being for all.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136374207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-20DOI: 10.3390/smartcities6050111
Sumbal Malik, Manzoor Ahmed Khan, Hesham El-Sayed, M. Jalal Khan
A specialized version of collaborative driving is convoy driving. It is referred to as the practice of driving more than one vehicle consecutively in the same lane with a small inter-vehicle distance, maintaining the same speed. Extensive research has been conducted on convoys of heavy-duty trucks on the highway; however, limited research has studied convoy driving in an urban environment. The complex dynamics of an urban environment require short-lived collaboration with varying numbers of vehicles rather than collaborating over hours. The motivation of this research is to investigate how convoy driving can be realized to address the challenges of an urban environment and achieve the benefits of autonomous driving such as reduced fuel consumption, travel time, improved safety, and ride comfort. In this work, the best-fitted coalitional game framework is utilized to formulate the convoy driving problem as a coalition formation game in an urban environment. A hypothesis is formulated that traveling in a coalition is more beneficial for a vehicle than traveling alone. In connection with this, a coalitional game and an all-comprehensive utility function are designed, modeled, and implemented to facilitate the formation of autonomous vehicle coalitions for convoy driving. Multiple solution concepts, such as the Shapley allocation, the Nucleolus, and the Core, are implemented to solve and analyze the proposed convoy driving game. Furthermore, several coalition formation strategies such as traveling mode selection, selecting optimal coalitions, and making decisions about coalition merging are developed to analyze the behavior of the vehicles. In addition to this, extensive numerical experiments with different settings are conducted to evaluate and validate the performance of the proposed study. The experimental results proved the hypothesis that traveling in a convoy is significantly more beneficial than traveling alone. We conclude that traveling in a convoy is beneficial for coalition sizes of two to four vehicles with an inter-vehicle spacing of less than 4 m considering the limitations of an urban environment. Traveling in a coalition allows vehicles to save on fuel, minimize travel time and enhance safety and comfort. Furthermore, the findings of this research state that achieving the enormous benefits of traveling in a coalition requires finding the right balance between inter-vehicle distance and coalition size. In the future, we plan to extend this work by studying the evolving dynamics of the coalitions and the environment.
{"title":"Should Autonomous Vehicles Collaborate in a Complex Urban Environment or Not?","authors":"Sumbal Malik, Manzoor Ahmed Khan, Hesham El-Sayed, M. Jalal Khan","doi":"10.3390/smartcities6050111","DOIUrl":"https://doi.org/10.3390/smartcities6050111","url":null,"abstract":"A specialized version of collaborative driving is convoy driving. It is referred to as the practice of driving more than one vehicle consecutively in the same lane with a small inter-vehicle distance, maintaining the same speed. Extensive research has been conducted on convoys of heavy-duty trucks on the highway; however, limited research has studied convoy driving in an urban environment. The complex dynamics of an urban environment require short-lived collaboration with varying numbers of vehicles rather than collaborating over hours. The motivation of this research is to investigate how convoy driving can be realized to address the challenges of an urban environment and achieve the benefits of autonomous driving such as reduced fuel consumption, travel time, improved safety, and ride comfort. In this work, the best-fitted coalitional game framework is utilized to formulate the convoy driving problem as a coalition formation game in an urban environment. A hypothesis is formulated that traveling in a coalition is more beneficial for a vehicle than traveling alone. In connection with this, a coalitional game and an all-comprehensive utility function are designed, modeled, and implemented to facilitate the formation of autonomous vehicle coalitions for convoy driving. Multiple solution concepts, such as the Shapley allocation, the Nucleolus, and the Core, are implemented to solve and analyze the proposed convoy driving game. Furthermore, several coalition formation strategies such as traveling mode selection, selecting optimal coalitions, and making decisions about coalition merging are developed to analyze the behavior of the vehicles. In addition to this, extensive numerical experiments with different settings are conducted to evaluate and validate the performance of the proposed study. The experimental results proved the hypothesis that traveling in a convoy is significantly more beneficial than traveling alone. We conclude that traveling in a convoy is beneficial for coalition sizes of two to four vehicles with an inter-vehicle spacing of less than 4 m considering the limitations of an urban environment. Traveling in a coalition allows vehicles to save on fuel, minimize travel time and enhance safety and comfort. Furthermore, the findings of this research state that achieving the enormous benefits of traveling in a coalition requires finding the right balance between inter-vehicle distance and coalition size. In the future, we plan to extend this work by studying the evolving dynamics of the coalitions and the environment.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136313232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.3390/smartcities6050110
Yerassyl Olzhabay, Muhammad N. Hamidi, Dahaman Ishak, Arjuna Marzuki, Annie Ng, Ikechi A. Ukaegbu
Perovskite solar cells (PSCs) are emerging photovoltaics (PVs) with promising optoelectronic characteristics. PSCs can be semitransparent (ST), which is beneficial in many innovative applications, including building-integrated photovoltaics (BIPVs). While PSCs exhibit excellent performance potential, enhancements in their stability and scalable manufacturing are required before they can be widely deployed. This work evaluates the real-world effectiveness of using PSCs in BIPVs to accelerate the development progress toward practical implementation. Given the present constraints on PSC module size and efficiency, bus stop shelters are selected for investigation in this work, as they provide a suitably scaled application representing a realistic near-term test case for early-stage research and engineering. An energy-harvesting system for a bus stop shelter in Astana, Kazakhstan, demonstrates the potential performance evaluation platform that can be used for perovskite solar cell modules (PSCMs) in BIPVs. The system includes maximum power point tracking (MPPT) and charge controllers, which can supply PSCM energy to the electronic load. Based on our design, the bus stop shelter has non-transparent and ST PSCMs on the roof and sides, respectively. May (best-case) and December (worst-case) scenarios are considered. According to the results, the PSCMs-equipped bus stop shelter can generate sufficient daily energy for load even in a worst-case scenario.
{"title":"Performance Evaluation of Emerging Perovskite Photovoltaic Energy-Harvesting System for BIPV Applications","authors":"Yerassyl Olzhabay, Muhammad N. Hamidi, Dahaman Ishak, Arjuna Marzuki, Annie Ng, Ikechi A. Ukaegbu","doi":"10.3390/smartcities6050110","DOIUrl":"https://doi.org/10.3390/smartcities6050110","url":null,"abstract":"Perovskite solar cells (PSCs) are emerging photovoltaics (PVs) with promising optoelectronic characteristics. PSCs can be semitransparent (ST), which is beneficial in many innovative applications, including building-integrated photovoltaics (BIPVs). While PSCs exhibit excellent performance potential, enhancements in their stability and scalable manufacturing are required before they can be widely deployed. This work evaluates the real-world effectiveness of using PSCs in BIPVs to accelerate the development progress toward practical implementation. Given the present constraints on PSC module size and efficiency, bus stop shelters are selected for investigation in this work, as they provide a suitably scaled application representing a realistic near-term test case for early-stage research and engineering. An energy-harvesting system for a bus stop shelter in Astana, Kazakhstan, demonstrates the potential performance evaluation platform that can be used for perovskite solar cell modules (PSCMs) in BIPVs. The system includes maximum power point tracking (MPPT) and charge controllers, which can supply PSCM energy to the electronic load. Based on our design, the bus stop shelter has non-transparent and ST PSCMs on the roof and sides, respectively. May (best-case) and December (worst-case) scenarios are considered. According to the results, the PSCMs-equipped bus stop shelter can generate sufficient daily energy for load even in a worst-case scenario.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135782283","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.3390/smartcities6050109
Simon Elias Bibri, Senthil Kumar Jagatheesaperumal
The Metaverse represents an always-on 3D network of virtual spaces, designed to facilitate social interaction, learning, collaboration, and a wide range of activities. This emerging computing platform originates from the dynamic convergence of Extended Reality (XR), Artificial Intelligence of Things (AIoT), and platform-mediated everyday life experiences in smart cities. However, the research community faces a pressing challenge in addressing the limitations posed by the resource constraints associated with XR-enabled IoT applications within the Internet of City Things (IoCT). Additionally, there is a limited understanding of the synergies between XR and AIoT technologies in the Metaverse and their implications for IoT applications within this framework. Therefore, this study provides a detailed overview of the literature on the potential applications, opportunities, and challenges pertaining to the deployment of XR technologies in IoT applications within the broader framework of IoCT. The primary focus is on navigating the challenges pertaining to the IoT applications powered by VR and AR as key components of MR in the Metaverse. This study also explores the emerging computing paradigm of AIoT and its synergistic interplay with XR technologies in the Metaverse and in relation to future IoT applications in the realm of IoCT. This study’s contributions encompass a comprehensive literature overview of XR technologies in IoT and IoCT, providing a valuable resource for researchers and practitioners. It identifies challenges and resource constraints, identifying areas that require further investigation. It fosters interdisciplinary insights into XR, IoT, AIoT, smart cities, and IoCT, bridging the gap between them. Lastly, it offers innovation pathways for effective XR deployment in future IoT/AIoT applications within IoCT. These contributions collectively advance our understanding of synergistic opportunities and complementary strengths of cutting-edge technologies for advancing the emerging paradigms of urban development.
{"title":"Harnessing the Potential of the Metaverse and Artificial Intelligence for the Internet of City Things: Cost-Effective XReality and Synergistic AIoT Technologies","authors":"Simon Elias Bibri, Senthil Kumar Jagatheesaperumal","doi":"10.3390/smartcities6050109","DOIUrl":"https://doi.org/10.3390/smartcities6050109","url":null,"abstract":"The Metaverse represents an always-on 3D network of virtual spaces, designed to facilitate social interaction, learning, collaboration, and a wide range of activities. This emerging computing platform originates from the dynamic convergence of Extended Reality (XR), Artificial Intelligence of Things (AIoT), and platform-mediated everyday life experiences in smart cities. However, the research community faces a pressing challenge in addressing the limitations posed by the resource constraints associated with XR-enabled IoT applications within the Internet of City Things (IoCT). Additionally, there is a limited understanding of the synergies between XR and AIoT technologies in the Metaverse and their implications for IoT applications within this framework. Therefore, this study provides a detailed overview of the literature on the potential applications, opportunities, and challenges pertaining to the deployment of XR technologies in IoT applications within the broader framework of IoCT. The primary focus is on navigating the challenges pertaining to the IoT applications powered by VR and AR as key components of MR in the Metaverse. This study also explores the emerging computing paradigm of AIoT and its synergistic interplay with XR technologies in the Metaverse and in relation to future IoT applications in the realm of IoCT. This study’s contributions encompass a comprehensive literature overview of XR technologies in IoT and IoCT, providing a valuable resource for researchers and practitioners. It identifies challenges and resource constraints, identifying areas that require further investigation. It fosters interdisciplinary insights into XR, IoT, AIoT, smart cities, and IoCT, bridging the gap between them. Lastly, it offers innovation pathways for effective XR deployment in future IoT/AIoT applications within IoCT. These contributions collectively advance our understanding of synergistic opportunities and complementary strengths of cutting-edge technologies for advancing the emerging paradigms of urban development.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":"66 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134990258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-11DOI: 10.3390/smartcities6050108
Ao Xu, Ruinan Zhang, Jiahui Yu, Yu Dong
Carbon-neutral architectural design focuses on rationally utilizing the building’s surroundings to reduce its environmental impact. Resilient ventilation systems, developed according to the thermal comfort requirements of building energy-saving research, have few applications. We studied the Jin-an Shopping Mall in Harbin and established the middle point height (h), middle point horizontal location (d), roof angle (α), and exposure to floor ratio (k) as the morphological parameters of the atrium. Using computational fluid dynamics (CFD), the mean radiant temperature (MRT), and the universal thermal climate index calculations (UTCI), this program was set to switch off air conditioning when the resilient ventilation met the thermal comfort requirement to achieve energy savings. The energy-saving efficiency (U) was calculated based on the energy consumption of the original model, and U could reach 7.34–9.64% according to the simulation and prediction. This study provides methods and a theoretical basis for renovating other commercial complexes to improve comfort and control energy consumption.
{"title":"Energy Saving Optimization of Commercial Complex Atrium Roof with Resilient Ventilation Using Machine Learning","authors":"Ao Xu, Ruinan Zhang, Jiahui Yu, Yu Dong","doi":"10.3390/smartcities6050108","DOIUrl":"https://doi.org/10.3390/smartcities6050108","url":null,"abstract":"Carbon-neutral architectural design focuses on rationally utilizing the building’s surroundings to reduce its environmental impact. Resilient ventilation systems, developed according to the thermal comfort requirements of building energy-saving research, have few applications. We studied the Jin-an Shopping Mall in Harbin and established the middle point height (h), middle point horizontal location (d), roof angle (α), and exposure to floor ratio (k) as the morphological parameters of the atrium. Using computational fluid dynamics (CFD), the mean radiant temperature (MRT), and the universal thermal climate index calculations (UTCI), this program was set to switch off air conditioning when the resilient ventilation met the thermal comfort requirement to achieve energy savings. The energy-saving efficiency (U) was calculated based on the energy consumption of the original model, and U could reach 7.34–9.64% according to the simulation and prediction. This study provides methods and a theoretical basis for renovating other commercial complexes to improve comfort and control energy consumption.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135979277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-04DOI: 10.3390/smartcities6050107
Zichong Lyu, D. Pons, Gilbert Palliparampil, Yilei Zhang
The transport of freight involves numerous intermediate steps, such as freight consolidation, truck allocation, and routing, all of which exhibit high day-to-day variability. On the delivery side, drivers usually cover specific geographic regions, also known as clusters, to optimise operational efficiency. A crucial aspect of this process is the effective allocation of resources to match business requirements. The discrete-event simulation (DES) technique excels in replicating intricate real-world operations and can integrate a multitude of stochastic variables, thereby enhancing its utility for decision making. The objective of this study is to formulate a routing architecture that integrates with a DES model to capture the variability in freight operations. This integration is intended to provide robust support for informed decision-making processes. A two-tier hub-and-spoke (H&S) architecture was proposed to simulate stochastic routing for the truck fleet, which provided insights into travel distance and time for cluster-based delivery. Real industry data were employed in geographic information systems (GISs) to apply the density-based spatial clustering of applications with noise (DBSCAN) clustering method to identify customer clusters and establish a truck plan based on freight demand and truck capacity. This clustering analysis and simulation approach can serve as a planning tool for freight logistics companies and distributors to optimise their resource utilisation and operational efficiency, and the findings may be applied to develop plans for new regions with customer locations and freight demands. The original contribution of this study is the integration of variable last-mile routing and an operations model for freight decision making.
{"title":"Optimising Urban Freight Logistics Using Discrete-Event Simulation and Cluster Analysis: A Stochastic Two-Tier Hub-and-Spoke Architecture Approach","authors":"Zichong Lyu, D. Pons, Gilbert Palliparampil, Yilei Zhang","doi":"10.3390/smartcities6050107","DOIUrl":"https://doi.org/10.3390/smartcities6050107","url":null,"abstract":"The transport of freight involves numerous intermediate steps, such as freight consolidation, truck allocation, and routing, all of which exhibit high day-to-day variability. On the delivery side, drivers usually cover specific geographic regions, also known as clusters, to optimise operational efficiency. A crucial aspect of this process is the effective allocation of resources to match business requirements. The discrete-event simulation (DES) technique excels in replicating intricate real-world operations and can integrate a multitude of stochastic variables, thereby enhancing its utility for decision making. The objective of this study is to formulate a routing architecture that integrates with a DES model to capture the variability in freight operations. This integration is intended to provide robust support for informed decision-making processes. A two-tier hub-and-spoke (H&S) architecture was proposed to simulate stochastic routing for the truck fleet, which provided insights into travel distance and time for cluster-based delivery. Real industry data were employed in geographic information systems (GISs) to apply the density-based spatial clustering of applications with noise (DBSCAN) clustering method to identify customer clusters and establish a truck plan based on freight demand and truck capacity. This clustering analysis and simulation approach can serve as a planning tool for freight logistics companies and distributors to optimise their resource utilisation and operational efficiency, and the findings may be applied to develop plans for new regions with customer locations and freight demands. The original contribution of this study is the integration of variable last-mile routing and an operations model for freight decision making.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":" ","pages":""},"PeriodicalIF":6.4,"publicationDate":"2023-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48702632","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-01DOI: 10.3390/smartcities6050106
Vian Ahmed, Mohamed Faisal Khatri, Zied Bahroun, Najihath Basheer
Smart technologies have become increasingly prevalent in various industries due to their potential for energy cost reduction, productivity gains, and sustainability. Smart campuses, which are educational institutions that implement smart technologies, have emerged as a specific application of these technologies. However, implementing available smart technologies is often not feasible due to various limitations, such as funding and cultural restrictions. In response, this study develops a mathematical decision-making tool based on the evidential reasoning (ER) approach and implemented in Python. The tool aims to assist universities in prioritizing smart campus solutions tailored to their specific needs. The research combines a comprehensive literature review with insights from stakeholder surveys to identify six principal objectives and four foundational technologies underpinning smart campus solutions. Additionally, six critical success factors and nine functional clusters of smart campus solutions are pinpointed, and evaluated through the ER approach. The developed decision-support tool underwent validation through various statistical tests and was found to be highly reliable, making it a generalized tool for worldwide use with different alternatives and attributes. The proposed tool provides universities with rankings and utilities to determine necessary smart applications based on inputs such as implementation cost, operation cost, maintenance cost, implementation duration, resource availability, and stakeholders’ perceived benefit.
{"title":"Optimizing Smart Campus Solutions: An Evidential Reasoning Decision Support Tool","authors":"Vian Ahmed, Mohamed Faisal Khatri, Zied Bahroun, Najihath Basheer","doi":"10.3390/smartcities6050106","DOIUrl":"https://doi.org/10.3390/smartcities6050106","url":null,"abstract":"Smart technologies have become increasingly prevalent in various industries due to their potential for energy cost reduction, productivity gains, and sustainability. Smart campuses, which are educational institutions that implement smart technologies, have emerged as a specific application of these technologies. However, implementing available smart technologies is often not feasible due to various limitations, such as funding and cultural restrictions. In response, this study develops a mathematical decision-making tool based on the evidential reasoning (ER) approach and implemented in Python. The tool aims to assist universities in prioritizing smart campus solutions tailored to their specific needs. The research combines a comprehensive literature review with insights from stakeholder surveys to identify six principal objectives and four foundational technologies underpinning smart campus solutions. Additionally, six critical success factors and nine functional clusters of smart campus solutions are pinpointed, and evaluated through the ER approach. The developed decision-support tool underwent validation through various statistical tests and was found to be highly reliable, making it a generalized tool for worldwide use with different alternatives and attributes. The proposed tool provides universities with rankings and utilities to determine necessary smart applications based on inputs such as implementation cost, operation cost, maintenance cost, implementation duration, resource availability, and stakeholders’ perceived benefit.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":" ","pages":""},"PeriodicalIF":6.4,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46121941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-29DOI: 10.3390/smartcities6050104
Xinyi Yang, Hafiz Usman Ahemd, Ying Huang, Pan Lu
The contribution of autonomous vehicles to traffic is one of the key aspects of future ground transportation in smart cities. Autonomous vehicles are able to provide many benefits, but some benefits can only provide advantages if these vehicles comprise a large percent of on the road/driven vehicles, which may take decades. Until then, the robotic drivers in autonomous vehicles will share the same road system with human divers in a mixed-driver environment where the majority of road accidents for autonomous vehicles are associated with the operational inconsistency of human drivers. In this paper, a cumulatively anticipative car-following model (which considers cumulative influences from multiple preceding vehicles) is developed to potentially improve the safety of autonomous vehicles in mixed-driver environments that benefit from enhanced communication between the autonomous vehicles and other components in the transportation system. Through intensive simulations (200 simulations), this study comprehensively evaluates four models including the cumulative anticipative car-following model, the Wiedemann 99 model, adaptive cruise control, and the cooperative adaptive cruise control model. Across 10 scenarios and five speed limits (24.59–33.53 m/s), the cumulative anticipative car-following model consistently demonstrates superior conflict reduction, with average, maximum, and minimum conflict percentages ranging from 77.69% to 91.97% against the Wiedemann 99 model, 67.00% to 93.94% against the adaptive cruise control model, and 69.17% to 93.25% against the cooperative adaptive cruise control model. Notably, the cooperative adaptive cruise control model exhibits suboptimal performance, especially in mixed-driver settings. The cumulative anticipative car-following model also enhances vehicle mobility, reducing average stops by up to 93.54%, 91.74%, 92.04%, 88.60%, and 91.35% in comparison to the other three models at speeds of 24.59 m/s, 26.82 m/s, 29.06 m/s, 31.29 m/s, and 33.53 m/s. Overall, the cumulative anticipative car-following model holds significant potential for conflict reduction and traffic enhancement.
{"title":"Cumulatively Anticipative Car-Following Model with Enhanced Safety for Autonomous Vehicles in Mixed Driver Environments","authors":"Xinyi Yang, Hafiz Usman Ahemd, Ying Huang, Pan Lu","doi":"10.3390/smartcities6050104","DOIUrl":"https://doi.org/10.3390/smartcities6050104","url":null,"abstract":"The contribution of autonomous vehicles to traffic is one of the key aspects of future ground transportation in smart cities. Autonomous vehicles are able to provide many benefits, but some benefits can only provide advantages if these vehicles comprise a large percent of on the road/driven vehicles, which may take decades. Until then, the robotic drivers in autonomous vehicles will share the same road system with human divers in a mixed-driver environment where the majority of road accidents for autonomous vehicles are associated with the operational inconsistency of human drivers. In this paper, a cumulatively anticipative car-following model (which considers cumulative influences from multiple preceding vehicles) is developed to potentially improve the safety of autonomous vehicles in mixed-driver environments that benefit from enhanced communication between the autonomous vehicles and other components in the transportation system. Through intensive simulations (200 simulations), this study comprehensively evaluates four models including the cumulative anticipative car-following model, the Wiedemann 99 model, adaptive cruise control, and the cooperative adaptive cruise control model. Across 10 scenarios and five speed limits (24.59–33.53 m/s), the cumulative anticipative car-following model consistently demonstrates superior conflict reduction, with average, maximum, and minimum conflict percentages ranging from 77.69% to 91.97% against the Wiedemann 99 model, 67.00% to 93.94% against the adaptive cruise control model, and 69.17% to 93.25% against the cooperative adaptive cruise control model. Notably, the cooperative adaptive cruise control model exhibits suboptimal performance, especially in mixed-driver settings. The cumulative anticipative car-following model also enhances vehicle mobility, reducing average stops by up to 93.54%, 91.74%, 92.04%, 88.60%, and 91.35% in comparison to the other three models at speeds of 24.59 m/s, 26.82 m/s, 29.06 m/s, 31.29 m/s, and 33.53 m/s. Overall, the cumulative anticipative car-following model holds significant potential for conflict reduction and traffic enhancement.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":" ","pages":""},"PeriodicalIF":6.4,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46967034","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-29DOI: 10.3390/smartcities6050105
Hong Yang, Jiandong Peng, Yuanhang Zhang, Xue Luo, Xuexin Yan
As the backbone of passenger transportation in many large cities around the world, it is particularly important to explore the association between the built environment and metro ridership to promote the construction of smart cities. Although a large number of studies have explored the association between the built environment and metro ridership, they have rarely considered the spatial and temporal heterogeneity between metro ridership and the built environment. Based on metro smartcard data, this study used EM clustering to classify metro stations into five clusters based on the spatiotemporal travel characteristics of the ridership at metro stations. And the GBDT model in machine learning was used to explore the nonlinear association between the built environment and the ridership of different types of stations during four periods in a day (morning peak, noon, evening peak, and night). The results confirm the obvious spatial heterogeneity of the built environment’s impact on the ridership of different types of stations, as well as the obvious temporal heterogeneity of the impact on stations of the same type. In addition, almost all built environment factors have complex nonlinear effects on metro ridership and exhibit obvious threshold effects. It is worth noting that these findings will help the correct decisions be made in constructing land use measures that are compatible with metro functions in smart cities.
{"title":"Understanding the Spatiotemporal Impacts of the Built Environment on Different Types of Metro Ridership: A Case Study in Wuhan, China","authors":"Hong Yang, Jiandong Peng, Yuanhang Zhang, Xue Luo, Xuexin Yan","doi":"10.3390/smartcities6050105","DOIUrl":"https://doi.org/10.3390/smartcities6050105","url":null,"abstract":"As the backbone of passenger transportation in many large cities around the world, it is particularly important to explore the association between the built environment and metro ridership to promote the construction of smart cities. Although a large number of studies have explored the association between the built environment and metro ridership, they have rarely considered the spatial and temporal heterogeneity between metro ridership and the built environment. Based on metro smartcard data, this study used EM clustering to classify metro stations into five clusters based on the spatiotemporal travel characteristics of the ridership at metro stations. And the GBDT model in machine learning was used to explore the nonlinear association between the built environment and the ridership of different types of stations during four periods in a day (morning peak, noon, evening peak, and night). The results confirm the obvious spatial heterogeneity of the built environment’s impact on the ridership of different types of stations, as well as the obvious temporal heterogeneity of the impact on stations of the same type. In addition, almost all built environment factors have complex nonlinear effects on metro ridership and exhibit obvious threshold effects. It is worth noting that these findings will help the correct decisions be made in constructing land use measures that are compatible with metro functions in smart cities.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":" ","pages":""},"PeriodicalIF":6.4,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47698991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-08-28DOI: 10.3390/smartcities6050103
Muhammad Nadeem, Naqqash Dilshad, N. Alghamdi, L. Dang, Hyoung-Kyu Song, Junyoung Nam, Hyeonjoon Moon
The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection.
{"title":"Visual Intelligence in Smart Cities: A Lightweight Deep Learning Model for Fire Detection in an IoT Environment","authors":"Muhammad Nadeem, Naqqash Dilshad, N. Alghamdi, L. Dang, Hyoung-Kyu Song, Junyoung Nam, Hyeonjoon Moon","doi":"10.3390/smartcities6050103","DOIUrl":"https://doi.org/10.3390/smartcities6050103","url":null,"abstract":"The recognition of fire at its early stages and stopping it from causing socioeconomic and environmental disasters remains a demanding task. Despite the availability of convincing networks, there is a need to develop a lightweight network for resource-constraint devices rather than real-time fire detection in smart city contexts. To overcome this shortcoming, we presented a novel efficient lightweight network called FlameNet for fire detection in a smart city environment. Our proposed network works via two main steps: first, it detects the fire using the FlameNet; then, an alert is initiated and directed to the fire, medical, and rescue departments. Furthermore, we incorporate the MSA module to efficiently prioritize and enhance relevant fire-related prominent features for effective fire detection. The newly developed Ignited-Flames dataset is utilized to undertake a thorough analysis of several convolutional neural network (CNN) models. Additionally, the proposed FlameNet achieves 99.40% accuracy for fire detection. The empirical findings and analysis of multiple factors such as model accuracy, size, and processing time prove that the suggested model is suitable for fire detection.","PeriodicalId":34482,"journal":{"name":"Smart Cities","volume":" ","pages":""},"PeriodicalIF":6.4,"publicationDate":"2023-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49011763","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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