Abstract
Nowadays many studies are conducted to develop solutions for improving the performance of urban traffic networks. One of the main challenges is the necessary cooperation among different entities such as vehicles or infrastructure systems and exploit the information available through networks of sensors deployed as infrastructures for smart cities. In this work an algorithm for cooperative control of urban subsystems is applied in order to provide solutions for mobility related problems in cities. The interconnected traffic lights controllers (TLC) network adapts traffic lights cycles, based on traffic and air pollution information, in order to improve the performance of urban traffic networks. The presence of air pollution in cities is not only caused by road traffic but there are other pollution sources that contribute to increase or decrease of the pollution level. Then the problem becomes more complex. Due to the distributed and heterogeneous nature of the different components involved, a system of systems engineering approach has been followed as design method and a distributed consensus-based control algorithm has been applied. The applied control law contains a consensus-based component that uses the information shared in the network for reaching a consensus in the state of TLC network components. Furthermore, Discrete Event Systems Specification (DEVS) formalism is applied for modelling and simulation purpose. The proposed solution has been tested and validated in a simulated environment corroborating that the proposed solution is a powerful technique to deal with simultaneous responses to both pollution levels and traffic flows in urban traffic networks.
Highlights
Current smart cities research aims to integration of urban subsystems where the subsystems have to work together [1]
Owing to the behaviour of the system is highly dependent of traffic conditions, the scenario was simulated 50 times both open loop (TLCs work with a fixed timing or Δ u i = 0 ) and closed loop for subsequent comparison
In order to evaluate the performance of the control system, two key performance indicators (KPIs) were defined as the mean of the absolute value of vehicle queues at all intersections and the global pollution during the simulation time (2 h)
Summary
Current smart cities research aims to integration of urban subsystems where the subsystems have to work together [1]. From a system engineering standpoint, a city can be considered as a physical system composed by several coupled, physical, subsystems These subsystems usually have different nature, i.e., system with different domain or different timing aspects, such as pollution measurement systems or traffic monitoring and control systems. Nanayakkara et al [2], proposed consensus-based control as a SoS cooperative control paradigm for extract greater benefit from systems constituents of a SoS. This approach aims to make a set of systems to achieve their own objectives as well as their common goals using communications between them. The problem of traffic optimization in urban environments based on city pollution information can be engineered as a consensus between control agents [5]
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