Introduction

The H2020 CIVITAS SUITS project was conceived by Professor Andree Woodcock and colleagues in 2014/15. It was scheduled to run between 2016 and 2020 but owing to the COVID-19 pandemic was extended to spring 2021. The aim of the project was to support capacity building of small–medium local authorities developing sustainable transport measures. This volume provides an account of the outputs of the project, in the form of chapters and recommendations for local authorities and consultants who are entrusted in delivering transport services which are inclusive, fit for purpose and enable accessibility for all. Although larger authorities are relatively well equipped to make these changes, smaller, more traditional local authorities may lack the knowledge, capacity and capability to plan, finance and implement sustainable transport measures at a time of great socio economic, technological and cultural change. Such authorities are also required to adopt new ways of working at the same time as designing and planning multimillion-euro transport projects which will support smart city developments and significantly improve the mobility of their citizens. At the heart of H2020 CIVITAS SUITS is a socio-technical approach, which recognises that capacity building is more than just providing training, and it is about empowering members of an organisation to be innovative. This volume has been written to inform designed to inform the daily practices of transport departments and stakeholder groups engaged in commissioning sustainable transport measures of working on Sustainable Urban Mobility Plans. © 2023, Transport for West Midlands.

Transport System of the Smart City Concept vs Pandemics: COVID-19 Case Study

In this article, we analysed the situation during the pandemics of COVID-19 virus in the Slovak Republic. We summarized measures within transport system, that Slovak Republic took in an attempt to soften the impact of the virus and to minimize its spread. We found out that these measures and the swiftness of their adoption had strong influence on flattening the curve of virus spreading. The main contribution of this article is in deeper look into possibilities of a smart transport system, aimed to identify, what more could the smart transport system offer to help in a fight of the country against spreading virus. For this purpose, we need to remind our previous work, where we described concept Smart City, concept Safe City, and their systems. One of these systems is system smart transport, and its description in previous work was the base ground for our design of additional solutions, improving safety in the time of pandemics. Therefore, this article will start with description of system Safe City and system smart transport, followed by examination of the case, evaluation of adopted measures, and proposal of additional measures. The focus of proposed measures will be given to the original design of mass transport system. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Italians and smart working: A technical study on the effects of smart working on the society

Due to the COVID-19 pandemic, "smart working” (hereafter SW) has become the norm for millions of workers around the world. A new way of working for most workers and in particular in Italy, a country where the use of SW was extremely rare before the pandemic. The aim of this paper, was to highlights whether smart working, adopted to face and survive global crises, could be really a suitable tool to generate benefits for companies, society, reduce environmental impacts and guarantee autonomy and flexibility for workers as well as a balance between private life. The analysis was conducted on a sample of 2753 individuals based in Italy during the period January and February 2021 using PLS-SEM model. The contribution of this study to research is identified in clarifying the potential of SW to create sustainable Smart Cities. © 2022 Elsevier Inc.

Sensing Technologies for Crowd Management, Adaptation, and Information Dissemination in Public Transportation Systems: A Review

Management of crowd information in public transportation (PT) systems is crucial, both to foster sustainable mobility, by increasing the user's comfort and satisfaction during normal operation, as well as to cope with emergency situations, such as pandemic crises, as recently experienced with coronavirus disease (COVID-19) limitations. This article presents a taxonomy and review of sensing technologies based on the Internet of Things (IoT) for real-time crowd analysis, which can be adopted in the different segments of the PT system (buses/trams/trains, railway/metro stations, and bus/tram stops). To discuss such technologies in a clear systematic perspective, we introduce a reference architecture for crowd management, which employs modern information and communication technologies (ICTs) in order to: 1) monitor and predict crowding events;2) implement crowd-aware policies for real-time and adaptive operation control in intelligent transportation systems (ITSs);and 3) inform in real time the users of the crowding status of the PT system, by means of electronic displays installed inside vehicles or at bus/tram stops/stations and/or by mobile transport applications. It is envisioned that the innovative crowd management functionalities enabled by ICT/IoT sensing technologies can be incrementally implemented as an add-on to state-of-the-art ITS platforms, which are already in use by major PT companies operating in urban areas. Moreover, it is argued that, in this new framework, additional services can be delivered to the passengers, such as online ticketing, vehicle access control and reservation in severely crowded situations, and evolved crowd-aware route planning. © 2001-2012 IEEE.

Evaluate Space after Covid-19: Smart City Strategies for Gamification

Smart cities carry the burden of utilizing technologies to support city life during and beyond the Covid-19 pandemic. More than ever, true smartness needs to address the broader health implications of the shared urban space. Especially highly populated cities tend to suffer more from the consequences of Covid-19 than rural areas. Without a doubt, the pandemic has revealed particular weaknesses of the existing urban environment, in urban space and population demographics, so evident of unprepared city infrastructure systems. The traditional design of public space has created inequalities, and such space design serves the needs of a commercialized urban context and enables public gatherings or private/commercial access. The sudden behavioral shift needed in cities means that smart solutions are also needed for the health and well-being of city populations. This paper examines the impact in the urban environments for London, Manchester, Newcastle, and Liverpool. This paper maps the implications of physical distancing due to Covid-19 using cases studies in three main areas: i) roads, ii) parks, and iii) retail. A matrix of urban, social, and health consequences is suggested, which will shape urban policy. It will focus on terms of access and use of urban space during the pandemic and beyond. The expected outcome of this research is to map some of the metropolitan area to demonstrate restrictions, changes in sharing behavior, and gamification opportunities of urban space. The expected outcomes will provide evidence-based scenarios for gamification technologies (for example, wayfinding, location, and character-based) of the challenged urban space in roads, parks, and retail to support change in future policy. The paper will discuss the implications of behavior change and consider so-called "gamification” practices in the urban space, using examples of social distancing, movement tracing, and techniques that add to a truly smart city. Overall, the aim is to demonstrate the spatial constraints of Covid-19;social distancing as the main challenge and to explore how the design of urban form and smart systems will provide for a healthy and resilient urban environment. This research addresses good urban health and a playful approach to the new way of urban living. © 2022 The Author(s). Published with license by Taylor & Francis Group, LLC.

The effect of the pandemic on European narratives on smart cities and surveillance

This article presents an analysis of European smart city narratives and how they evolved under the pressure of the COVID-19 pandemic. We start with Joss et al.'s observation that the smart-city discourse is presently in flux, engaged in intensive boundary-work and struggling to gain wider support. We approach this process from the critical perspective of surveillance capitalism, as proposed by Zuboff, to highlight the growing privacy concerns related to technological development. Our results are based on analysing 184 articles regarding smart-city solutions, published on social media by five European journals between 2017 and 2021. We adopted both human and machine coding processes for qualitative and quantitative analysis of our data. As a result, we identified the main actors and four dominant narratives: regulation of artificial intelligence and facial recognition, technological fight with the climate emergency, contact tracing apps and the potential of 5G technology to boost the digitalisation processes. Our analysis shows the growing number of positive narratives underlining the importance of technology in fighting the pandemic and mitigating the climate emergency, but the latter is often mentioned in a tokenistic fashion. Right to privacy considerations are central for two out of four discovered topics. We found that the main rationale for the development of surveillance technologies relates to the competitiveness of the EU in the global technological rivalry, while ambitions like increasing societal well-being or safeguarding the transparency of new policies are nearly non-existent. © Urban Studies Journal Limited 2023.

Effective use of smart cities in crisis cases: A systematic review of the literature

Smart cities have become an influential concept in urban development. Smart cities and their applications aim to maintain a high quality of life by using smart technologies and enhancing economic productivity. Previous systematic literature reviews have considered the development of smart cities and highlighted their applications and services. However, no prior studies have comprehensively investigated smart cities in relation to emergencies. To this end, the current paper aims to provide a research agenda reviewing the relevant literature that touches on smart technologies during emergencies like the ongoing COVID-19 pandemic. Based on a systematic methodology centred on text mining analysis, our research identified the following three themes: (a) emergency response, which covers emergency management, traffic and unmanned aerial vehicles, waste disposal, and contact tracing;(b) motivation and outcome, which includes such sub-themes as smart urbanism, quality of life and the economy;and (c) technology and data, which covers social media, machine learning, Internet of Things, data-driven applications, and object detection. We comprehensively discuss each theme and offer suggestions for future research. © 2023

Reconstruction of Human Trajectories Based on Anonymous Sensor Data for Estimating Infection Route

The COVID-19 Pandemic has increased the demands of governments for technologies to estimate the route of infection. In this paper, we propose a new smart city framework that collects anonymized passage information from deployed Bluetooth sensors and analyzes them to reconstruct the multiple trajectories of infected people. We formulate recovering multiple trajectories on the basis of anonymized passage information, including passage time and passage position, obtained by sensors in a smart city as a problem of multiple-trajectory reconstruction in general networks. We propose a new method for reconstructing multiple trajectories on the basis of anonymized passage information. Our method assumes that each trajectory follows a Markov process and estimates transit time for each edge in networks and the transition probability of the Markov process. On the basis of its estimation, our method can find multiple trajectories with maximum likelihood by solving a minimum cost flow problem. We evaluate the performance of our method in experiments using simulation data and actual human trajectory data. © 2022 IEEE.

Smart City Construction and Management by Digital Twins and BIM Big Data in COVID-19 Scenario

With the rapid development of information technology and the spread of Corona Virus Disease 2019 (COVID-19), the government and urban managers are looking for ways to use technology to make the city smarter and safer. Intelligent transportation can play a very important role in the joint prevention. This work expects to explore the building information modeling (BIM) big data (BD) processing method of digital twins (DTs) of Smart City, thus speeding up the construction of Smart City and improve the accuracy of data processing. During construction, DTs build the same digital copy of the smart city. On this basis, BIM designs the building's keel and structure, optimizing various resources and configurations of the building. Regarding the fast data growth in smart cities, a complex data fusion and efficient learning algorithm, namely Multi-Graphics Processing Unit (GPU), is proposed to process the multi-dimensional and complex BD based on the compositive rough set model. The Bayesian network solves the multi-label classification. Each label is regarded as a Bayesian network node. Then, the structural learning approach is adopted to learn the label Bayesian network's structure from data. On the P53-old and the P53-new datasets, the running time of Multi-GPU decreases as the number of GPUs increases, approaching the ideal linear speedup ratio. With the continuous increase of K value, the deterministic information input into the tag BN will be reduced, thus reducing the classification accuracy. When K = 3, MLBN can provide the best data analysis performance. On genbase dataset, the accuracy of MLBN is 0.982 +/- 0.013. Through experiments, the BIM BD processing algorithm based on Bayesian Network Structural Learning (BNSL) helps decision-makers use complex data in smart cities efficiently.