An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities
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 achiev...
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doaj-a808364b3df54f9585d7b86e332a361f2020-12-17T00:03:32ZengMDPI AGSensors1424-82202020-12-01207209720910.3390/s20247209An Hybrid Approach for Urban Traffic Prediction and Control in Smart CitiesJanetta Culita0Simona Iuliana Caramihai1Ioan Dumitrache2Mihnea Alexandru Moisescu3Ioan Stefan Sacala4Faculty of Automatic Control and Computers, Politehnica University of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, Politehnica University of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, Politehnica University of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, Politehnica University of Bucharest, 060042 Bucharest, RomaniaFaculty of Automatic Control and Computers, Politehnica University of Bucharest, 060042 Bucharest, RomaniaSmart 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.https://www.mdpi.com/1424-8220/20/24/7209urban traffic predictionsmart cityneuro-inspired control architecturecomplex systemsparametric model |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Janetta Culita Simona Iuliana Caramihai Ioan Dumitrache Mihnea Alexandru Moisescu Ioan Stefan Sacala |
spellingShingle |
Janetta Culita Simona Iuliana Caramihai Ioan Dumitrache Mihnea Alexandru Moisescu Ioan Stefan Sacala An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities Sensors urban traffic prediction smart city neuro-inspired control architecture complex systems parametric model |
author_facet |
Janetta Culita Simona Iuliana Caramihai Ioan Dumitrache Mihnea Alexandru Moisescu Ioan Stefan Sacala |
author_sort |
Janetta Culita |
title |
An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities |
title_short |
An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities |
title_full |
An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities |
title_fullStr |
An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities |
title_full_unstemmed |
An Hybrid Approach for Urban Traffic Prediction and Control in Smart Cities |
title_sort |
hybrid approach for urban traffic prediction and control in smart cities |
publisher |
MDPI AG |
series |
Sensors |
issn |
1424-8220 |
publishDate |
2020-12-01 |
description |
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. |
topic |
urban traffic prediction smart city neuro-inspired control architecture complex systems parametric model |
url |
https://www.mdpi.com/1424-8220/20/24/7209 |
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