Smart cities are complex, socio-technological systems built as a strongly connected System of Systems, whose functioning is driven by human–machine interactions and whose ultimate goals are the well-being of their inhabitants. Consequently, controlling a smart city is an objective that may be achieved by using a specific framework that integrates algorithmic control, intelligent control, cognitive control and especially human reasoning and communication. Among the many functions of a smart city, intelligent transportation is one of the most important, with specific restrictions and a high level of dynamics. This paper focuses on the application of a neuro-inspired control framework for urban traffic as a component of a complex system. It is a proof of concept for a systemic integrative approach to the global problem of smart city management and integrates a previously designed urban traffic control architecture (for the city of Bucharest) with the actual purpose of ensuring its proactivity by means of traffic flow prediction. Analyses of requirements and methods for prediction are performed in order to determine the best way for fulfilling the perception function of the architecture with respect to the traffic control problem definition. A parametric method and an AI-based method are discussed in order to predict the traffic flow, both in the short and long term, based on real data. A brief comparative analysis of the prediction performances is also presented.

The problem of defining, analyzing and reasoning behaviors, facts and uncertainties in complex systems asks for new modeling formal methods and software tools that can deal with managing such collections of heterogeneous and dynamical interacting entities. The purpose of this paper is to present in detail the implementation of a new software modeling platform which is based on the hLARM (Hybrid Logic-Algebraic Relational Modeling) formalism which aims to alleviate the process of designing and managing such systems. The development of the platform is thoroughly analyzed from its conceptual background and up to the algorithms which support all the inference procedures involved.

System performance for networks composed of interconnected subsystems can be increased if the traditionally separated subsystems are jointly optimized. Recently, parallel and distributed optimization methods have emerged as a powerful tool for solving estimation and control problems in large-scale networked systems. In this paper we review and analyze the optimization-theoretic concepts of parallel and distributed methods for solving coupled optimization problems and demonstrate how several estimation and control problems related to complex networked systems can be formulated in these settings. The paper presents a systematic framework for exploiting the potential of the decomposition structures as a way to obtain different parallel algorithms, each with a different tradeoff among convergence speed, message passing amount and distributed computation architecture. Several specific applications from estimation and process control are included to demonstrate the power of the approach.