“…Infrastructure comprises durable assets that define the physical configuration of mobility systems [ 20 ]. Infrastructure provides a solution to the problems arising from massive urbanization [ 21 , 31 ] and ultimately determines the health of cities [ 22 ]. The reinforcement of infrastructure is essential to determining the level of readiness for smart city transformation [ 1 , 27 , 31 , 44 , 49 , 50 ].…”
Section: Methodsmentioning
confidence: 99%
“…Infrastructure provides a solution to the problems arising from massive urbanization [ 21 , 31 ] and ultimately determines the health of cities [ 22 ]. The reinforcement of infrastructure is essential to determining the level of readiness for smart city transformation [ 1 , 27 , 31 , 44 , 49 , 50 ]. The attractiveness of a city is linked to its ability to attract a creative population [ 51 ], a construct that encompasses a broad spectrum of elements [ 52 ].…”
Section: Methodsmentioning
confidence: 99%
“…In addition, smart cities can increase economic efficiency to a greater extent than those linked to ecological factors [ 15 , 25 ]. Elements that condition the transformation of cities include social impact [ 31 ]. Sustainable mobility plays a key role in achieving a sustainable urban environment, generating benefits for the well-being and public health of cities [ 12 , 15 ].…”
Section: Methodsmentioning
confidence: 99%
“…Turning cities into sustainable ones requires a transformative process, which considers the context and needs of the city, the local interest, the quality of life of citizens and the readiness of the city for change [ 3 ]. Readiness is a multidimensional construct that reflects the simultaneous presence of political, social, economic and environmental factors [ 27 , 31 , 49 ].…”
Section: Methodsmentioning
confidence: 99%
“…Mass urbanization and resource scarcity are global phenomena for which urban transformation towards a smart city is the most viable solution [ 31 ]. Sustainable innovation is the result of the combined effect of different elements [ 32 ].…”
The growing concentration of the population in urban areas presents great challenges for sustainability. Within this process, mobility emerges as one of the main generators of externalities that hinder the achievement of the Sustainable Development Goals. The transition of cities towards innovations in sustainable mobility requires progress in different dimensions, whose interaction requires research. Likewise, it is necessary to establish whether the experiences developed between cities with different contexts can be extrapolated. Therefore, the purpose of this study was to identify how the conditions that determine a city’s readiness to implement urban mobility innovations could be combined. For this, qualitative comparative analysis was applied to a model developed using the multi-level perspective, analyzing 60 cities from different geographical areas and with a different gross domestic product per capita. The R package Set Methods was used. The explanation of the readiness of cities to implement mobility innovations is different to the explanation of the readiness negation. While readiness is explained by two solutions, in which only regime elements appear, the negation of readiness is explained by five possible solutions, showing the interaction between the landscape and regimen elements and enacting the negation of innovations as a necessary condition. The cluster analysis shows us that the results can be extrapolated between cities with different contexts.
“…Infrastructure comprises durable assets that define the physical configuration of mobility systems [ 20 ]. Infrastructure provides a solution to the problems arising from massive urbanization [ 21 , 31 ] and ultimately determines the health of cities [ 22 ]. The reinforcement of infrastructure is essential to determining the level of readiness for smart city transformation [ 1 , 27 , 31 , 44 , 49 , 50 ].…”
Section: Methodsmentioning
confidence: 99%
“…Infrastructure provides a solution to the problems arising from massive urbanization [ 21 , 31 ] and ultimately determines the health of cities [ 22 ]. The reinforcement of infrastructure is essential to determining the level of readiness for smart city transformation [ 1 , 27 , 31 , 44 , 49 , 50 ]. The attractiveness of a city is linked to its ability to attract a creative population [ 51 ], a construct that encompasses a broad spectrum of elements [ 52 ].…”
Section: Methodsmentioning
confidence: 99%
“…In addition, smart cities can increase economic efficiency to a greater extent than those linked to ecological factors [ 15 , 25 ]. Elements that condition the transformation of cities include social impact [ 31 ]. Sustainable mobility plays a key role in achieving a sustainable urban environment, generating benefits for the well-being and public health of cities [ 12 , 15 ].…”
Section: Methodsmentioning
confidence: 99%
“…Turning cities into sustainable ones requires a transformative process, which considers the context and needs of the city, the local interest, the quality of life of citizens and the readiness of the city for change [ 3 ]. Readiness is a multidimensional construct that reflects the simultaneous presence of political, social, economic and environmental factors [ 27 , 31 , 49 ].…”
Section: Methodsmentioning
confidence: 99%
“…Mass urbanization and resource scarcity are global phenomena for which urban transformation towards a smart city is the most viable solution [ 31 ]. Sustainable innovation is the result of the combined effect of different elements [ 32 ].…”
The growing concentration of the population in urban areas presents great challenges for sustainability. Within this process, mobility emerges as one of the main generators of externalities that hinder the achievement of the Sustainable Development Goals. The transition of cities towards innovations in sustainable mobility requires progress in different dimensions, whose interaction requires research. Likewise, it is necessary to establish whether the experiences developed between cities with different contexts can be extrapolated. Therefore, the purpose of this study was to identify how the conditions that determine a city’s readiness to implement urban mobility innovations could be combined. For this, qualitative comparative analysis was applied to a model developed using the multi-level perspective, analyzing 60 cities from different geographical areas and with a different gross domestic product per capita. The R package Set Methods was used. The explanation of the readiness of cities to implement mobility innovations is different to the explanation of the readiness negation. While readiness is explained by two solutions, in which only regime elements appear, the negation of readiness is explained by five possible solutions, showing the interaction between the landscape and regimen elements and enacting the negation of innovations as a necessary condition. The cluster analysis shows us that the results can be extrapolated between cities with different contexts.
Recent advances in energy conversion, information technology, the internet, new types of web, and communication technologies have enabled the interconnection of all physical objects, including sensors and actuators. Web‐enabled smart objects have paved the way for smart homes by enabling innovative services. In this work, the idea of using time series analysis based on machine learning and forecasting considering the weather conditions is discussed, to enhance automation and improve intelligence. The proposed Prophet model is used to predict the future net energy consumption and generation for improving energy efficiency and enabling power backup. Energy efficiency is the need of the hour, wherein major utilization of energy is in the residential sector, it is gravely important to analyze current generation and consumption and act accordingly for the future predicted results. Moreover, smart homes are dependent on web technologies and telecommunication for the operation of every action, which makes it crucial to have necessary power backup. The Prophet forecasting model, after parameter tuning and logistic growth pattern with additional regressors, gives only 0.27 mean absolute error and 0.13 mean squared error for predicting future energy consumption as compared to other models.
The unsafe behavior of construction workers is the most direct reason of frequent construction site accidents. In order to improve the safety management of construction sites and figure out the causal relationship among the influencing factors in the field of construction workers’ unsafe behaviors, literature research, questionnaire survey, decision-making trial, and evaluation laboratory—interpretive structural modeling—crossimpact matrix multiplication applied to classification (DEMATEL-ISM-MICMAC) methods were used in combination in this thesis. The analysis of collected data was carried out in three dimensions: individual, organizational environment, and safety management. A framework about influencing factors of construction workers’ unsafe behaviors was constructed. DEMATEL-ISM was used to construct the explanatory structural model, which to analyze the influence relationships and hierarchical relationships among factors. MICMAC method was used to analyze the driving dependency. ISM model consists of six parts, the bottom of which includes three influencing factors: work environment, safety supervision, and concernment of superior. With characteristics of high drive and low dependence, the bottom layer are the root causes of construction workers’ unsafe behaviors. Work environment and concernment of superior are the core indicators of it. The intermediate layer with a low drive and low dependence covers six factors: psychological status, physical health, professional skills, organizational climate, work quota, and safety plan. It is the indirect factor to influence construction workers’ unsafe behaviors. The top layer is composed of safety awareness, safety education, and technical delivery. Safety awareness is the core of individual dimension, showing the characteristics of low drive and high dependence, is the direct factor to influence construction workers’ unsafe behaviors. Based on the DEMATEL-ISM-MICMAC method, the methodology to reduce construction workers’ unsafe behaviors was proposed.
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