Big data and smart cities: a public sector organizational learning perspective

Public sector organizations (city authorities) have begun to explore ways to exploit big data to provide smarter solutions for cities. The way organizations learn to use new forms of technology has been widely researched. However, many public sector organisations have found themselves in new territory in trying to deploy and integrate this new form of technology (big data) to another fast moving and relatively new concept (smart city). This paper is a cross-sectional scoping study—from two UK smart city initiatives—on the learning processes experienced by elite (top management) stakeholders in the advent and adoption of these two novel concepts. The findings are an experiential narrative account on learning to exploit big data to address issues by developing solutions through smart city initiatives. The findings revealed a set of moves in relation to the exploration and exploitation of big data through smart city initiatives: (a) knowledge finding; (b) knowledge reframing; (c) inter-organization collaborations and (d) ex-post evaluations. Even though this is a time-sensitive scoping study it gives an account on a current state-of-play on the use of big data in public sector organizations for creating smarter cities. This study has implications for practitioners in the smart city domain and contributes to academia by operationalizing and adapting Crossan et al’s (Acad Manag Rev 24(3): 522–537, 1999) 4I model on organizational learning.     

MIKADO: a smart city KPIs assessment modeling framework

Smart decision making plays a central role for smart city governance. It exploits data analytics approaches applied to collected data, for supporting smart cities stakeholders in understanding and effectively managing a smart city. Smart governance is performed through the management of key performance indicators (KPIs), reflecting the degree of smartness and sustainability of smart cities. Even though KPIs are gaining relevance, e.g., at European level, the existing tools for their calculation are still limited. They mainly consist in dashboards and online spreadsheets that are rigid, thus making the KPIs evolution and customization a tedious and error-prone process. In this paper, we exploit model-driven engineering (MDE) techniques, through metamodel-based domain-specific languages (DSLs), to build a framework called MIKADO for the automatic assessment of KPIs over smart cities. In particular, the approach provides support for both: (i) domain experts, by the definition of a textual DSL for an intuitive KPIs modeling process and (ii) smart cities stakeholders, by the definition of graphical editors for smart cities modeling. Moreover, dynamic dashboards are generated to support an intuitive visualization and interpretation of the KPIs assessed by our KPIs evaluation engine. We provide evaluation results by showing a demonstration case as well as studying the scalability of the KPIs evaluation engine and the general usability of the approach with encouraging results. Moreover, the approach is open and extensible to further manage comparison among smart cities, simulations, and KPIs interrelations.

     

Quality and the efficiency of data in “Smart-Cities”

As a new form of sustainable development, the concept “Smart Cities” knows a large expansion during the recent years. It represents an urban model, refers to all alternative approaches to metropolitan ICTs case to enhance quality and performance of urban service for better interaction between citizens and government. However, the smart cities based on distributed and autonomous information infrastructure contains millions of information sources that will be expected more than 50 billion devices connected by using IoT or other similar technologies in 2020. In Information Technology, we often need to process and reason with information coming from various sources (sensors, experts, models). Information is almost always tainted with various kinds of imperfection: imprecision, uncertainty, ambiguity, we need a theoretical framework general enough to allow for the representation, propagation and combination of all kinds of imperfect information. The theory of belief functions is one such Framework. Real-time data generated from autonomous and distributed sources can contain all sorts of imperfections regarding on the quality of data e.g. imprecision, uncertainty, ignorance and/or incompleteness. Any imperfection in data within smart city can have an adverse effect over the performance of urban services and decision making. In this context, we address in this article the problem of imperfection in smart city data. We will focus on handling imperfection during the process of information retrieval and data integration and we will create an evidential database by using the evidence theory in order to improve the efficiency of smart city. The expected outcomes from this paper are (1) to focus on handling imperfection during the process of information retrieval and data integration (2) to create an evidential database by using the evidence theory in order to improve the efficiency of smart city. As experimentation we present a special case of modeling imperfect data in the field of Healthcare. An evidential database will be built which will contain all the perfect and imperfect data. These data come from several Heterogeneous sources in a context of Smart Cities. Imperfect aspects in the evidential database expressed by the theory of beliefs that will present in this paper.     

CITIESData: a smart city data management framework

Smart city data come from heterogeneous sources including various types of the Internet of Things such as traffic, weather, pollution, noise, and portable devices. They are characterized with diverse quality issues and with different types of sensitive information. This makes data processing and publishing challenging. In this paper, we propose a framework to streamline smart city data management, including data collection, cleansing, anonymization, and publishing. The paper classifies smart city data in sensitive, quasi-sensitive, and open/public levels and then suggests different strategies to process and publish the data within these categories. The paper evaluates the framework using a real-world smart city data set, and the results verify its effectiveness and efficiency. The framework can be a generic solution to manage smart city data.     

Impacts of low citizen awareness and usage in smart city services: the case of London’s smart parking system

Smart city applications and services are increasingly considered as strategic means to cope with emerging global challenges such as climate change, pollution, the ageing population, and energy shortage. In particular, smart parking is a type of smart services used to improve traffic congestion and pollution within cities. Nevertheless, although smart city services are driven by advanced information technologies, their success is highly dependent on user engagement, which is historically problematic. This paper presents and discusses the results of a case study on the smart parking service in London. A questionnaire (involved a total of 212 local drivers) was adopted as the main data collection method. This was complemented by the collection and analysis of 470 online user comments left for the service. The results showed that London’s smart parking service may potentially help each driver to save an average of £68 (62.2 l) on petrol annually and reduce CO₂ emissions by 238.14 kg per car per year. At city level, a smart parking system could help London save £183.6 million worth of petrol per year and reduce its annual CO₂ emissions by 642,978 tons. However, public awareness, actual usage, and user satisfaction of this smart service are currently very low. These present substantial barriers to realise the potential economic and environmental benefits of the service. This paper concluded that further to the very technological efforts, local authorities and service providers need to make a stronger endeavour to enhance public engagement and user satisfaction towards smart services, in order to realise the promises of such solutions.     

Recommender systems for smart cities

Among other conceptualizations, smart cities have been defined as functional urban areas articulated by the use of Information and Communication Technologies (ICT) and modern infrastructures to face city problems in efficient and sustainable ways. Within ICT, recommender systems are strong tools that filter relevant information, upgrading the relations between stakeholders in the polity and civil society, and assisting in decision making tasks through technological platforms. There are scientific articles covering recommendation approaches in smart city applications, and there are recommendation solutions implemented in real world smart city initiatives. However, to the best of our knowledge, there is not a comprehensive review of the state of the art on recommender systems for smart cities. For this reason, in this paper we present a taxonomy of smart city features, dimensions, actions and goals, and, according to these variables, we survey the existing literature on recommender systems. As a result of our survey, we do not only identify and analyze main research trends, but also show current opportunities and challenges where personalized recommendations could be exploited as solutions for citizens, firms and public administrations.     

智慧城市系统协同数据共享模式研究

智慧城市的重要任务是实现城市中信息系统协同工作、数据信息共享和开放应用。目前,智慧城市协同的方案有集中数据中心、开放平台、物联网标识服务等。本文总结了这几种模式,并比较了其各自的优势和问题,探究了物联网标识服务作为基础设施结合其他方案更能保证智慧城市协同共享的效果。     

Citizen-centric data services for smarter cities

Smart Cities use Information and Communication Technologies (ICT) to manage more efficiently the resources and services offered by a city and to make them more approachable to all its stakeholders (citizens, companies and public administration). In contrast to the view of big corporations promoting holistic “smart city in a box” solutions, this work proposes that smarter cities can be achieved by combining already available infrastructure, i.e., Open Government Data and sensor networks deployed in cities, with the citizens’ active contributions towards city knowledge by means of their smartphones and the apps executed in them. In addition, this work introduces the main characteristics of the IES Cities platform, whose goal is to ease the generation of citizen-centric apps that exploit urban data in different domains. The proposed vision is achieved by providing a common access mechanism to the heterogeneous data sources offered by the city, which reduces the complexity of accessing the city’s data whilst bringing citizens closely to a prosumer (double consumer and producer) role and allowing to integrate legacy data into the cities’ data ecosystem.     

面向公共安全数据处理的浪涌模型研究应用

近年来,随着平安城市和智慧城市项目的建设和发展,视频监控系统已经成为公安机关治安管控、打击犯罪、预防应急突发事件的有效手段。随着网络通信技术的迅速发展以及移动智能终端(如智能手机、平板电脑等)的快速普及,智能终端已经普遍携带视频监控、音频、加速传感器等感知设备。部分高端智能终端所能携带的视频设备已经超过部分低端的视频监控设备。智能终端的大量普及使得构建以人为中心的感知与计算网络成为可能。对不同信息空间的信息进行有效融合,可以加强对于公共安全事件的有效感知与检测。针对公共安全事件多源信息的融合问题,提出了数据浪涌模型,并对该模型进行了定义。同时利用该模型对人证合一系统进行了实例验证。开发的人证合一系统已经应用于北京市的多个长途车站与火车站。     

Analysis and assessment of a knowledge based smart city architecture providing service APIs

The main technical issues regarding smart city solutions are related to data gathering, aggregation, reasoning, data analytics, access, and service delivering via Smart City APIs (Application Program Interfaces). Different kinds of Smart City APIs enable smart city services and applications, while their effectiveness depends on the architectural solutions to pass from data to services for city users and operators, exploiting data analytics, and presenting services via APIs.Therefore, there is a strong activity on defining smart city architectures to cope with this complexity, putting in place a significant range of different kinds of services and processes. In this paper, the work performed in the context of Sii-Mobility smart city project on defining a smart city architecture addressing a wide range of processes and data is presented. To this end, comparisons of the state of the art solutions of smart city architectures for data aggregation and for Smart City API are presented by putting in evidence the usage semantic ontologies and knowledge base in the data aggregation in the production of smart services. The solution proposed aggregate and re-conciliate data (open and private, static and real time) by using reasoning/smart algorithms for enabling sophisticated service delivering via Smart City API. The work presented has been developed in the context of the Sii-Mobility national smart city project on mobility and transport integrated with smart city services with the aim of reaching a more sustainable mobility and transport systems. Sii-Mobility is grounded on Km4City ontology and tools for smart city data aggregation, analytics support and service production exploiting smart city API. To this end, Sii-Mobility/Km4City APIs have been compared to the state of the art solutions. Moreover, the proposed architecture has been assessed in terms of performance, computational and network costs in terms of measures that can be easily performed on private cloud on premise. The computational costs and workloads of the data ingestion and data analytics processes have been assessed to identify suitable measures to estimate needed resources. Finally, the API consumption related data in the recent period are presented.     

New Paradigm of Data-Driven Smart Customisation through Digital Twin

Big data is one of the most important resources for the promotion of smart customisation. With access to data from multiple sources, manufacturers can provide on-demand and customised products. However, existing research of smart customisation has focused on data generated from the physical world, not virtual models. As physical data is constrained by what has already occurred, it is limited in the identification of new areas to improve customer satisfaction. A new technology called digital twin aims to achieve this integration of physical and virtual entities. Incorporation of digital twin into the paradigm of existing data-driven smart customisation will make the process more responsive, adaptable and predictive. This paper presents a new framework of data-driven smart customisation augmented by digital twin. The new framework aims to facilitate improved collaboration of all stakeholders in the customisation process. A case study of the elevator industry illustrates the efficacy of the proposed framework.     

智能交通动态调度优化模型与评价方法

为了降低大城市市民出行成本,缓解公交企业运力压力,提出一种智能交通出行OD(Origin Destination,出行地和目的地)的公交调度优化算法,以公交出行OD客流预测和计划排班发车时间间隔为出发点,运用公交出行OD客流推导理论,构建智能交通出行OD的公交调度优化模型。通过获取个人OD数据,利用单条线路公交OD方法,实现全市公交OD矩阵推算。根据全市公交出行OD推算结果,求解公交调度模型,解决智能交通调度多目标规划和公交线网优化问题。通过仿真模拟试验,分析智能公交排班计划评价指标,计算车辆营运效率占比:自动排班仿真数据为79%,实际运营数据为73%;统计车辆高峰时段与全天营运车次占比:自动排班仿真数据为36.75%,实际运营数据为37.37%,满足智能公交计划排班评价指标的要求,实例证明模型和算法具有实用性和可靠性。     

The social smart grid: Dealing with constrained energy resources through social coordination

The smart grid promises to improve the efficiency and reliability of tomorrow's energy supply. One of the biggest achievements of future smart grids will be their distributed mode of operation which effectively eliminates vulnerable nodes causing single points of failures in the grid. However, due to the lack of centralized energy production and control, the coordination of energy consumption becomes first priority. Because there do not exist technologies to store energy at large-scale yet, all energy that is required must be produced at the same time. The biggest challenge of energy producers is therefore to reliably predict and provide the right amount of required energy to avoid shortages and breakdowns. In this paper, we propose a novel way to let smart grid stakeholders, i.e., energy producers and consumers, coordinate their energy demands themselves. For that purpose we combine traditional social network models and service-oriented computing concepts with the smart grid to allow consumers to form communities according to their energy consumption behavior. These communities enable them to interact with other grid stakeholders to coordinate energy consumption plans and set up private energy sharing alliances. This way, the utility provider and industrial energy producers can rely on a better predictable and a smoother energy demand of customers. We introduce a software framework, making use of widely adopted standards, demonstrate its feasibility with an agent-based simulation, and discuss its overall applicability.     

Discovering the Hidden Community Structure of Public Transportation Networks

Advances in public transit modeling and smart card technologies can reveal detailed contact patterns of passengers. A natural way to represent such contact patterns is in the form of networks. In this paper we utilize known contact patterns from a public transit assignment model in a major metropolitan city, and propose the development of two novel network structures, each of which elucidate certain aspects of passenger travel behavior. We first propose the development of a transfer network, which can reveal passenger groups that travel together on a given day. Second, we propose the development of a community network, which is derived from the transfer network, and captures the similarity of travel patterns among passengers. We then explore the application of each of these network structures to identify the most frequently used travel paths, i.e., routes and transfers, in the public transit system, and model epidemic spreading risk among passengers of a public transit network, respectively. In the latter our conclusions reinforce previous observations, that routes crossing or connecting to the city center in the morning and afternoon peak hours are the most “dangerous” during an outbreak.

     

基于区块链的智慧城市边缘设备可信管理方法研究

由于物联网设备的资源受限,当前智慧城市相关应用系统,在抵御以物联网设备为目标的恶意攻击时存在局限性,难以提供安全可靠的服务.本文设计了基于区块链的智慧城市边缘设备管理架构,将区块链技术引入智慧城市建设研究中,利用区块链分布式架构和去中心化的思想,实现感知数据的可信收集和存储,并基于该架构提出了一种新的基于信誉的PoW共...     

Modelling peak-hour urban freight movements with limited data availability

Modelling the complexity of urban freight transport requires large amounts of data related to supply chain management, delivery practices, tour configuration, time windows, etc., but when all this detailed data is not available local authorities still need models that represent this type of transport and its contribution to congestion and environmental impacts. We present here an improvement on other recent works, consisting of a demand model for B2B and home deliveries during the morning peak hour that uses only very limited data to estimate the number of delivery vehicles entering and leaving each zone of the city. We then calculate the trip distribution using an entropy maximisation approach, and solve the resulting model using simulated annealing. We apply this model to a case study in the city of Seville, in Spain, and compare its results to those produced by a gravity model, and with actual traffic counts.     

Viscous Open Data: The Roles of Intermediaries in an Open Data Ecosystem

Open data have the potential to improve the governance of universities as public institutions. In addition, open data are likely to increase the quality, efficacy and efficiency of the research and analysis of higher education systems by providing a shared empirical base for critical interrogation and reinterpretation. Drawing on research conducted by the Emerging Impacts of Open Data in Developing Countries project, and using an ecosystems approach, this research paper considers the supply, demand and use of open data as well as the roles of intermediaries in the governance of South African public higher education. It shows that government's higher education database is a closed and isolated data source in the data ecosystem; and that the open data that are made available by government is inaccessible and rarely used. In contrast, government data made available by data intermediaries in the ecosystem are being used by key stakeholders. Intermediaries are found to play several important roles in the ecosystem: (i) they increase the accessibility and utility of data; (ii) they may assume the role of a “keystone species” in a data ecosystem; and (iii) they have the potential to democratize the impacts and use of open data. The article concludes that despite poor data provision by government, the public university governance open data ecosystem has evolved because intermediaries in the ecosystem have reduced the viscosity of government data. Further increasing the fluidity of government open data will improve access and ensure the sustainability of open data supply in the ecosystem.     

数据驱动的事件预测技术最新研究进展

"事件"(event)是指在特定时空发生的对人类社会和自然界产生较为明显影响的事情.社会动乱、暴恐事件、传染病大流行等例子是给国家和社会安全带来严重威胁的"事件".如果能够提前对这些事件的发生进行有效预测,将有助于做好应对准备,大大减少不必要的损失,因此事件预测技术在实际中具有重大社会应用价值,能够在社会安全、风险感知...     

Towards a Cloud Computing Paradigm for Big Data Analysis in Smart Cities

In this paper, we present a Big Data analysis paradigm related to smart cities using cloud computing infrastructures. The proposed architecture follows the MapReduce parallel model implemented using the Hadoop framework. We analyse two case studies: a quality-of-service assessment of public transportation system using historical bus location data, and a passenger-mobility estimation using ticket sales data from smartcards. Both case studies use real data from the transportation system of Montevideo, Uruguay. The experimental evaluation demonstrates that the proposed model allows processing large volumes of data efficiently.     

图形图像融合的海量建筑绘制

目的 城市3维模型数据海量且结构复杂,缺乏一个高效完善的可视化系统往往是影响数字城市应用的瓶颈之一。通常利用多层次细节（LOD）与调度算法减少每一帧绘制的数据量来提高绘制效率,当场景规模足够大时,即使采用复杂的优化算法也难以取得较好的效果。为此,本文在传统算法基础上,提出一种图形图像融合的海量建筑物场景绘制方法。方法 提出并采用视域分级绘制策略,将视椎体平行分割为感兴趣区域、次感兴趣区域和非感兴趣区域,感兴趣区域采用图形实时绘制方法,使用离屏渲染技术将次感兴趣和非感兴趣区域绘制在纹理图像中,每一帧绘制完成后将二者进行顾及深度信息的融合,实现完整场景渲染。结果 使用公开的纽约市区CityGML文件作为实验数据,数据包含了118 195个LOD1和LOD2级别的建筑物模型。分别构建多组不同建筑数量的场景进行帧率统计实验,绘制帧率都达到20帧/s以上。算法实现了视觉无损失的场景完整渲染,并与Cesium平台进行对比实验,证明算法有效且系统运行流畅。结论 图形图像融合的绘制方法,既保持了图形渲染的漫游连续性,同时也具有图像渲染的场景复杂度无关的优点。实验结果表明,针对大规模的低分辨率建筑模型场景,算法可以有效提高系统的渲染能力,在性能相对较低的硬件条件下也能实现海量建筑物实体模型的流畅漫游,并达到视觉无损失的场景完整绘制。