A Computational Architecture Based on RFID Sensors for Traceability in Smart Cities

Information Technology and Communications (ICT) is presented as the main element in order to achieve more efficient and sustainable city resource management, while making sure that the needs of the citizens to improve their quality of life are satisfied. A key element will be the creation of new systems that allow the acquisition of context information, automatically and transparently, in order to provide it to decision support systems. In this paper, we present a novel distributed system for obtaining, representing and providing the flow and movement of people in densely populated geographical areas. In order to accomplish these tasks, we propose the design of a smart sensor network based on RFID communication technologies, reliability patterns and integration techniques. Contrary to other proposals, this system represents a comprehensive solution that permits the acquisition of user information in a transparent and reliable way in a non-controlled and heterogeneous environment. This knowledge will be useful in moving towards the design of smart cities in which decision support on transport strategies, business evaluation or initiatives in the tourism sector will be supported by real relevant information. As a final result, a case study will be presented which will allow the validation of the proposal

Complex Urban Systems: Challenges and Integrated Solutions for the Sustainability and Resilience of Cities

For decades, from design theory to urban planning and management, from social sciences to urban environmental science, cities have been probed and analyzed from the partial perspective of single disciplines. The digital era, with its unprecedented data availability, is allowing for testing old theories and developing new ones, ultimately challenging relatively partial models. Our community has been in the last years providing more and more compelling evidence that cities are complex systems with emergent phenomena characterized by the collective behavior of their citizens who are themselves complex systems. However, more recently, it has also been shown that such multiscale complexity alone is not enough to describe some salient features of urban systems. Multilayer network modeling, accounting for both multiplexity of relationships and interdependencies among the city's subsystems, is indeed providing a novel integrated framework to study urban backbones, their resilience to unexpected perturbations due to internal or external factors, and their human flows. In this paper, we first offer an overview of the transdisciplinary efforts made to cope with the three dimensions of complexity of the city: the complexity of the urban environment, the complexity of human cognition about the city, and the complexity of city planning. In particular, we discuss how the most recent findings, for example, relating the health and wellbeing of communities to urban structure and function, from traffic congestion to distinct types of pollution, can be better understood considering a city as a multiscale and multilayer complex system. The new challenges posed by the postpandemic scenario give to this perspective an unprecedented relevance, with the necessity to address issues of reconstruction of the social fabric, recovery from prolonged psychological, social and economic stress with the ensuing mental health and wellbeing issues, and repurposing of urban organization as a consequence of new emerging practices such as massive remote working. By rethinking cities as large-scale active matter systems far from equilibrium which consume energy, process information, and adapt to the environment, we argue that enhancing social engagement, for example, involving citizens in codesigning the city and its changes in this critical postpandemic phase, can trigger widespread adoption of good practices leading to emergent effects with collective benefits which can be directly measured

Planning support science: Developments and challenges

In this paper, we provide an update of recent developments and forthcoming challenges in the field of planning support systems, following earlier reviews in 2003 and 2009. The rationale for this update is the rapid development of information and communication technologies and their impact on planning support systems. After a brief retrospective assessment of past planning support system developments, the paper presents a synthesis of the experiences and views of a worldwide sample of invited planning support system experts, whose innovative contributions comprise a new Handbook of Planning Support Science. The developments documented by the experts together substantiate our impression that a fundamental transformation is taking place – a paradigm shift – wherein the field of planning support systems is maturing into a planning support science. From this perspective, it is expected that planning support systems will become indispensable instruments in the planning process in the not too distant future. The signs of this maturation are already visible in research, education and practice

Resolving urban mobility networks from individual travel graphs using massive-scale mobile phone tracking data

Human movements and interactions with cities are characterized by urban mobility networks. Many studies that address urban mobility are inspired by complex networks. The models of complex networks require a large amount of empirical data. However, current works relied on traditional survey data and were unable to take full advantage of the capabilities offered by complex networks; thus, the possibility of quantifying urban mobility networks by considering individual travel patterns has not yet been addressed. This study presents a data-driven approach for characterizing urban mobility networks based on massive-scale mobile phone tracking data. Individual travel motifs are first extracted using a graph-based approach. The global urban mobility network (G-UMN) and the motif-dependent urban mobility subnetworks (MD-UMNs) are then constructed. Next, network properties, including statistical measures and scaling relations between the basic measures, are proposed for characterizing mobility networks. We have conducted experiments focusing on Shenzhen, China. The results demonstrated that (1) the individual travel motifs are structurally and spatially heterogeneous, (2) the G-UMN exhibits a evolutionary hierarchical structure, and (3) the MD-UMNs show many behavioral differences in their spatial and topological properties, reflecting the impacts of the heterogeneity of the individual travel motifs. These results bridge the gap between complex network properties and urban mobility patterns and provide crucial implications and policies for data-informed urban planning