Mobility, Data, and Behavior

This chapter presents the social innovation project “TrafficO2”, a support system for decision-making in the field of transportation that tries to push commuters towards more sustainable mobility by providing concrete incentives for each responsible choice. After focusing on Palermo, Italy, the context of this case study, this chapter provides a detailed description of the TrafficO2 model. Specifically, the chapter deals with the analysis of a selected sample of users among Palermo University students who commute daily to their respective University departments on campus. Starting from the modal split of the actual situation (Status Quo scenario), another behavior scenario (Do your right mix) is designed and promoted to encourage users to create a better mix of existing mobility means and reduce the use of private vehicles powered by combustibles. The first test that was performed confirmed the reliability of the initiative.

Wireless Access Networks for Smart Cities

Smart cities are envisioned to enable a vast amount of services in urban environments, so as to improve mobility, health, resource management, and, generally speaking, citizens' quality of life. Most of these services rely on pervasive, seamless and real-time access to information by users on the move, as well as on continuous exchanges of data among millions of devices deployed throughout the urban surface. It is thus clear that communication networks will be the key to enabling smart city solutions, by providing their core support infrastructure. In particular, wireless technologies will represent the main tool leveraged by such an infrastructure, as they allow device mobility and do not have the deployment constraints of wired architectures. In this Chapter, we present different wireless access networks intended to empower future smart cities, and discuss their features, complementarity and interoperability.

Multi-Scale, Multi-Dimensional Modelling of Future Energy Systems

This chapter provides a discussion of current multi-scale energy systems expressed by a multitude of data and simulation models, and how these modelling approaches can be (re)designed or combined to improve the representation of such system. It aims to address the knowledge gap in energy system modelling in order to better understand its existing and future challenges. The frontiers between operational algorithms embedded in hardware and modelling control strategies are becoming fuzzier: therefore the paradigm of modelling intelligent urban energy systems for the future has to be constantly evolving. The chapter concludes on the need to build a holistic, multi-dimensional and multi-scale framework in order to address tomorrow's urban energy challenges. Advances in multi-scale methods applied to material science, chemistry, fluid dynamics, and biology have not been transferred to the full extend to power system engineering. New tools are therefore necessary to describe dynamics of coupled energy systems with optimal control.

Living Labs and Urban Smartness

Urban Living Labs are socio-digital innovation environments in realistic city life conditions based on multi-stakeholder partnerships that effectively involve citizens in the co-creation and co-production of new or reformed public services and infrastructures. This chapter explores the growing phenomenon of Urban Living Labs and analyses the nature of related innovations in the perspective of ‘City Smartness' – a mantra for local governments worldwide which are having to address increasingly complex problems with fast diminishing financial resources. It goes on to briefly overview the urban governance models emerging in such environments and finally focuses on the challenges posed by these models as result of integration between the ‘technology push' Smart City vision and the ‘human pull' Urban Living Lab concept and approach.

Crowdsensing in Smart Cities

The widespread availability of smartphones with on-board sensors has recently enabled the possibility of harvesting large quantities of monitoring data in urban areas, thus enabling so-called crowdsensing solutions, which make it possible to achieve very large-scale and fine-grained sensing by exploiting all personal resources and mobile activities in Smart Cities. In fact, the information gathered from people, systems, and things, including both social and technical data, is one of the most valuable resources available to a city's stakeholders, but its huge volume makes its integration and processing, especially in a real-time and scalable manner, very difficult. This chapter presents and discusses currently available crowdsensing and participatory solutions. After presenting the current state-of-the-art crowdsensing management infrastructures, by carefully considering the related and primary design guidelines/choices and implementation issues/opportunities, it provides an in-depth presentation of the related work in the field. Moreover, it presents some novel experimental results collected in the ParticipAct Crowdsensing Living Lab testbed, an ongoing experiment at the University of Bologna that involves 150 students for one year in a very large-scale crowdsensing campaign.

An Innovative Approach to Vehicle Electrification for Smart Cities

Vehicles, both personal and commercial, have become ubiquitous forms of transportation in the developed world. The auto industry is amidst a technological transformation in identifying alternative sources of energy to power vehicles due to two driving forces: environmental pollution prevention and depletion of fuel resources. This driver for developing “smarter” solutions to create a “smarter planet” is crucial to advancing the science behind electric vehicles (EVs). EVs have been in existence since the mid-19th century, and electric locomotion has been the commonplace in many other vehicle types such as trains. The focus of this chapter is to discuss the feasibility of EVs in smart cities. In particular, the chapter explores the types of EVs, advantages and challenges faced by EVs to penetrate the market, and to outline state-of-the-art research and technologies that are driving the creation of newer and better EVs for adoption in the smart cities of tomorrow.

Smart Cities

Smart City is a fuzzy concept that has not been clearly defined either in theoretical studies or in empirical projects. Smart Cities are based on Information and Communication Technologies (ICT), people (with their knowledge, habits, experiences, culture and behaviour) remain at the heart of concerns. In this chapter we are interested in the centrality of citizens (i.e. in the heart of the city) and of ICT in their environment. This leads us to take into account the tacit knowledge brought by citizens and the knowledge that may be divulged through ICT. We then present the concept of the Information and Knowledge System (IKS), and then we explain how it differs from that of the Digital Information System (DIS). We also point to the role of ICT in the DIS, and to their impact on improving the smartness of cities.

Smart Cities and the Internet

This chapter challenges recent mode 2 accounts of smart cities and in particular, the idea they are an index of the future internet. Adopting the triple helix model of knowledge production, it studies smart cities, not as the emergent technologies of economic transactions, but in terms of civil society's support for the integration of Web2.0-based information and communication platforms into their regional innovation systems. This reveals that no matter how technologically advanced such an internet-driven reinvention of cities may appear, being smart is something which reaches beyond this. Beyond this and towards policies, leadership qualities and corporate strategies that not only serve the knowledge economy, but which are also smart in allowing cities to cultivate the creativity of the internet as the information and communication technologies of regional innovation systems.

Energy Investment in Smart Cities Unlocking Financial Instruments in Europe

The intense pressures being brought to bear by the increasing diversity in European urban development patterns call for innovative funding mechanisms to promote smart sustainable urban development, most notably in the energy sector. Currently in Europe, various policy initiatives support sustainable urban development through financial engineering mechanisms operating at municipal and regional scales. The objective of this chapter is to review the main financial mechanisms focusing on energy, and in particular on urban investments committed to a highly energy-efficient, and low carbon, economy. Within this framework we assert that, in order to achieve the EU sustainable urban development outcomes, specific European financial instruments will need to be considered as viable key investment options. The structure and operational features of European Financial Instruments are explored here in the case of the Urban Development Fund implemented in London. We also discuss the importance of ESCOs and crowdfunding as essential funding sources for community energy projects, and suggest that European policy should recognise their importance.

From the Smart City to the People-Friendly City

This chapter addresses the smart city concept as a first step towards the formulation of a new socially-improved urban concept which may be defined as that of the “people-friendly city”. This new task involves the employment of IT tools, but using new methods and pursuing different goals other than mere numerical information. In terms of the urban environment, this means that cities should be designed for people, and planning practitioners should be able to understand citizens' needs, communicate with them and involve them in a collaborative process. Therefore, an overview of the implications of smart cities for urban planning is followed by a more detailed analysis of Planning Support Systems (PSS) as innovative tools for enhancing the process of delivering a more inclusive and people-friendly urban environment. The lessons learnt from the application of the PSS tool is then illustrated in order to define the potentialities and key points for the development of similar tools.