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.

Multi-Section Traffic Flow Prediction Based on MLR-LSTM Neural Network

“…The model-driven method [ 21 ] (2020) is used to build a model based on the understanding of the traffic model, but its accuracy and applicability are limited due to the complexity of the actual traffic environment. The data-driven method focuses on the mapping relationship between data and phenomena, but the demand for data is large, and the understanding and application depth of traffic mechanisms is insufficient.…”

Improving model predictive control's optimization for urban traffic

“…However, due to the complicated traffic management system, these approaches are unable to provide an acceptable response. As a result, the development of smart approaches can aid in the resolution and improvement of urban management issues [8], [12]- [14]. In the planning of transportation infrastructure, traffic modeling and simulations are important [11], [15], [16].…”

Driving Behavior Analysis of City Buses Based on Real-Time GNSS Traces and Road Information

“…The insufficient management and monitoring of buses have caused social issues, e.g., residents have gradually lost confidence in public transportation, social security systems and government programs. Consequently, a greater scientific basis and more statistical analyses are required to support intelligent transportation systems [8,9].…”