An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities

Abstract

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.

Keywords

• urban traffic prediction
• smart city
• neuro-inspired control architecture
• complex systems
• parametric model