Abstract
Intelligent traffic management is an important issue for smart cities. City councils try to implement the newest techniques and performant technologies in order to avoid traffic congestion, to optimize the use of traffic lights, to efficiently use car parking, etc. To find the best solution to this problem, Birmingham City Council decided to allow open-source predictive traffic forecasting by making the real-time datasets available. This paper proposes a multi-agent system (MAS) approach for intelligent urban traffic management in Birmingham using forecasting and classification techniques. The designed agents have the following tasks: forecast the occupancy rates for traffic flow, road junctions and car parking; classify the faults; control and monitor the entire process. The experimental results show that k-nearest neighbor forecasts with high accuracy rates for the traffic data and decision trees build the most accurate model for classifying the faults for their detection and repair in the shortest possible time. The whole learning process is coordinated by a monitoring agent in order to automate Birmingham city’s traffic management.
Highlights
Nowadays, intelligent data flow management has attracted a large amount of attention in the smart cities domain
The proposed architecture is designed to deal with real-time data and to notify a human expert if any anomalies appear regarding traffic flow occupancy rates, road junction occupancy rates and car parking occupancy rates in Birmingham city
The proposed multi-agent system (MAS) architecture was designed according to intelligent urban traffic management processes in Birmingham city and the agents were enhanced with intelligence after performing an experimental phase
Summary
Intelligent data flow management has attracted a large amount of attention in the smart cities domain. Learning the large amount of data collected with smart city infrastructure and technologies has become an important issue as it is necessary in order to make the best decisions in the minimum amount of time. The intelligent agents embedded forecasting or classification techniques in order to extract knowledge from a large amount of data collected from the sensors. The proposed architecture is designed to deal with real-time data and to notify a human expert if any anomalies appear regarding traffic flow occupancy rates, road junction occupancy rates and car parking occupancy rates in Birmingham city. Multi-agent systems (MAS) are used in smart city application in order to automate and monitor different processes. The proposed MAS architecture was designed according to intelligent urban traffic management processes in Birmingham city and the agents were enhanced with intelligence after performing an experimental phase. The paper has the following sections: multi-agent system architecture, experiments and results (dataset description, forecasting results, classification results and mass testing) discussion and conclusions
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