IET Smart Cities最新文献

Scientific planning, digital management of construction and intelligent service are needed for a smart city. This paper focusses on city management based on the smart information system and digital technologies in China. The establishment of the smart intercommunication system of landscape, water conservancy, environmental protection and other industries is basic for a smart city. This paper introduced a framework of digital technologies for the construction of a smart city, which made full use of the city information modelling based on the geographic information system, building information modelling, internet of things, and integrated digital system and service platform such as satellite remote sensing, global navigation satellite system, mobile applications, cloud computing, visualisation technology etc. The study of the case implemented show that the framework is applicable to the smart city with digital technologies that includes the data model and system, integrating the urban basic geographic data, and data of infrastructure and other public facilities related to traffic, urban construction, ‘One Map’ of land and resources etc. The governance is more effective through data acquisition, data transmission, data analysis and processing based on the data platform and system.

More European countries seriously depend on oil supplies from Russia primarily via one pipeline, which makes energy security weaker. This energy balance brings a massive problem for the import intensity; therefore, e-mobility might be a potential solution for the trade deficits of many European countries. Battery Electric Vehicles and Plug-In Hybrid Electric vehicles have been introduced within the priorities of the EC but also by car manufacturing companies worldwide. By 2050, massive growth of Electric vehicles (EVs) is expected, and significant changes in favour of electric cars have to be observed in new car sales till 2030. The article's main objective is to investigate whether and to what extent new sales of e-cars bring lower oil imports to Slovakia. The authors use three scenarios (based on regression models) differentiating market force intensity and regulation stringency till 2030. The significant findings of the models provide an estimated number of EVs on Slovak roads in 2030 and significant oil import cuts stemming from oil import substitution. The conclusion suggests that by 2030, Slovak oil imports will only slightly decrease due to e-mobility penetration, even in the most optimistic scenario.

Traffic congestion and road intersection management have become a significant issue, mainly with the highly increasing number of vehicles in cities. There is a common belief from vehicle drivers that installing traffic lights with some consideration of traffic flows will be dominant in traffic movements. This article proposes a novel system for Urban Traffic Control (UTC) with a continuous dynamic environment adaptation to improve traffic flow on large cities' network roads. The proposed system introduces vehicle counting method, lane evaluation of the current status and controlling method considering the effect on the whole traffic network—not just the intersection itself—to provide an efficient traffic scheduling. The main objective of the authors’ system is to reduce traffic jam, by reducing waiting time and trip time for vehicles at intersections. Some indicators and models are introduced in this work to assign traffic flow schedules with minimum traffic congestion and vehicle waiting time. These indicators and models include a traffic jam indicator, vehicle priority and lane weight. A multi-agent urban traffic control system is proposed as the simulation environment using NetLogo simulator. (A total of 150) Vehicles are generated with random behaviour distributed over 25 intersections for 9 h duration to compare the normal fixed cycle traffic light and the authors’ smart traffic control. Results show a reduction in the total average waiting time of all vehicles for the simulation period by more than (29.98%). Hence, it is more suitable for the complexity of the current traffic condition with minimum changing infrastructure.

Japan has long embarked on the city transformation journey, from green city to eco city, ubiquitous city, sustainable city, and now zero-emission city. A smart city is not considered smart if it is not green, not sustainable, and not environmentally friendly. The journey is long but progressive, and the Japanese government has been supportive in its city transformational efforts. Japanese cities are marked by distinctive local cultures, habitat, people, food, beliefs, history etc. From Tokyo to Osaka, Nagoya, Fukuoka, Kobe and Yokohama, cities in Japan have advanced into high levels of urbanisation. The increase in population and traffic utilisation have resulted in higher energy demands and pollution of the environment. These concerns have motivated Japan to strive for cities with zero-emission and Tokyo, as the country's capital, will lead in this drive. The author outlines the current situation in Japan, the impact of global warming and climate change, presents the motivation behind the new strategy, and narrates and discusses Tokyo city’s zero-emission strategy and execution plans and how it works towards achieving net zero by 2050.

Fossil-fuelled vehicles are being replaced by electric vehicles (EVs) around the world due to environmental pollution and high fossil fuel price. On the one hand, the electrical grid is faced with some challenges when too many EVs are improperly integrated. On the other hand, using massive unexploited capacity of the battery storage in too many EVs makes these challenges to opportunities. This unused capacity can be employed for the grid ancillary services and trading peer-to-peer (P2P) energy. However, the preference of EV users is one of the most important factors, which has to be considered within the scheduling process of EVs. Therefore, this paper proposes a stochastic model for EVs bidirectional smart charging taking into account the preferences of EV users, P2P energy trading, and providing ancillary services of the grid based on blockchain mechanism. Considering the preferences of EV users makes the proposed scheduling model adaptive against changing operating conditions. The presented model is formulated as an optimisation problem aiming at optimal management of EV battery state of charge and energy placement of several services considering the provision of ancillary services and contributing to P2P transactions. To evaluate the proposed model, real-world data collected from Tehran city are used as input data of simulation. Numerical results demonstrate the efficacy of the presented model. Simulation results show that considering the preferences of EV users in the proposed model can enhance the total income provided by the EV energy-planning model such that it could balance the charging cost. Moreover, this advanced user-based smart charging model increases P2P energy transactions amongst EVs and raises the ancillary services facility to the grid.

Traffic speed prediction is an integral part of an Intelligent Transportation System (ITS) and the Internet of Vehicles (IoV). Advanced knowledge of average traffic speed can help take proactive preventive steps to avoid impending problems. There have been studies for traffic speed prediction in which data has been decomposed into components using various decomposition techniques such as empirical mode decomposition, wavelets, and seasonal decomposition. As far as the authors are aware, no research has used additively decomposed components as input features. In this study, we used additive decomposition on 21,843 samples of traffic speed data. We implemented two statistical techniques designed for double seasonality (i) Double Seasonal Holt-Winter, and (ii) Trigonometric seasonality, Box-Cox transformation, autoregressive integrated moving average errors, trend, and Seasonal components (TBATS), and five machine learning (ML) techniques, (i) Multi-Layer Perceptron, (ii) Convolutional-Neural Network, (iii) Long Short-Term Memory, (iv) Gated Recurrent Unit and (v) Convolutional-Neural Network-LSTM. Machine learning techniques are used in univariate mode with raw time series as features and then with decomposed components as features in multivariate mode. This study demonstrates that using decomposed components (trend, seasonal, and residual), as features, improves prediction results for multivariate ML techniques. This becomes a significant advantage when no other features are available.

Unmanned Aerial Vehicles (UAVs) are offering many global industry sectors the opportunity to adopt more sustainable business models. They offer innovative ways of managing resources and water and offer newer opportunities to address key challenges in many areas like border surveillance, precision agriculture and search and rescue missions. All these new applications areas tend to require the cooperation of groups, or “swarms” of UAVs to provide collaborative sensing and processing solutions. These new scenarios impose new requirements in terms of safety, coordination, and operation management. This paper provides an overview of some of the technical challenges that multirotor UAVs are still facing in terms of aerial coordination and interaction. In this regard, it focusses on recent developments available in the literature and presents some contributions realised during the past few years by the authors addressing UAV interaction to achieve collision-free flights and swarm-based missions. Based on the analysis provided in this work, the paper is able to provide insight into the challenges still open that need to be solved in order to enable effective UAV-based solutions to support sustainable aerial services.

An optimal day-ahead operation of a microgrid (MG) based on an energy hub (EH) that is an industrial building, is presented in this paper. The proposed EH includes wind turbine (WT), photovoltaic (PV), triple generation that is combined cooling, heat and power, and salt water desalination. The purpose of solving problem is to lessen the operational and pollution costs limited to several technical restrictions. The EH takes into account plug in electric vehicle (PEV) and an ice storage conditioner (ISC) and together with a thermal energy storage system that is a supplementary energy storage system (ESS). Particularly, the performance and efficacy of the EH operational and pollution costs are studied by considering a solar--powered compressed air energy storage (SPCAES) that is a novel rechargeable and developing ESS. The proposed model takes into account the uncertain behaviour of PV and WT generations together with the thermal, electrical, and cooling demands, which deal with a robust optimisation approach. The suggested robust mix integer linear problem model is figured out using the CPLEX solver in general algebraic modelling system software. The proposed framework is implemented on the industrial building located in the industrial city of Kaveh, Iran. The simulation results show that using ESSs including SPCAES, ISC, and PEVs reduce the total costs (operation and emission costs) by 2.42% in the day-ahead energy management.