Abstract:The work presented in this paper concerns a switching-based control formulation for multi-intersection and multi-phase traffic light systems. A macroscopic traffic flow modelling approach is first presented, which is instrumental to the development of a model-based and switching-based optimization method for traffic signal operation, in the framework of Adaptive Dynamic Programming (ADP). The main advantage of the switching-based formulation is its capability to determine both 'when' to switch and 'which' mode… Show more
“…Liu et al [48] presented a model-based and switching-based control formulation for multi-intersection and multiphase traffic signal operation. Based on their model, appropriate adaptive dynamic programming methods were used to seek the optimal traffic light policy.…”
“…Liu et al [48] presented a model-based and switching-based control formulation for multi-intersection and multiphase traffic signal operation. Based on their model, appropriate adaptive dynamic programming methods were used to seek the optimal traffic light policy.…”
“…Alternative switch-based ADP methods were considered in [13] and [14] for classes of switched systems, and in [15] for classes of piecewise-smooth systems. Applications include optimal switching in power converters [16], traffic networks [17], and networked control [18], [19].…”
Switch-based adaptive dynamic programming (ADP) is an optimal control problem in which a cost must be minimized by switching among a family of dynamical modes. When the system dimension increases, the solution to switch-based ADP is made prohibitive by the exponentially increasing structure of the value function approximator and by the exponentially increasing modes. This technical correspondence proposes a distributed computational method for solving switch-based ADP. The method relies on partitioning the system into agents, each one dealing with a lower dimensional state and a few local modes. Each agent aims to minimize a local version of the global cost while avoiding that its local switching strategy has conflicts with the switching strategies of the neighboring agents. A heuristic algorithm based on the consensus dynamics and Nash equilibrium is proposed to avoid such conflicts. The effectiveness of the proposed method is verified via traffic and building test cases.
“…Algorithms to control the traffic-light duration at urban intersection based on dynamic programming or by control theory have been proposed, for instance, in [1], [2], [3], [4], [5], [6]. See also [7], [8] for broad reviews.…”
We consider a mixed autonomy scenario where the traffic intersection controller decides whether the traffic light will be green or red at each lane for multiple trafficlight blocks. The objective of the traffic intersection controller is to minimize the queue length at each lane and maximize the outflow of vehicles over each block. We consider that the traffic intersection controller informs the autonomous vehicle (AV) whether the traffic light will be green or red for the future traffic-light block. Thus, the AV can adapt its dynamics by solving an optimal control problem. We model the decision process of the traffic intersection controller as a deterministic delay Markov decision process owing to the delayed action by the traffic controller. We propose Reinforcement-learning based algorithm to obtain the optimal policy. We show -empirically -that our algorithm converges and reduces the energy costs of AVs drastically as the traffic controller communicates with the AVs.
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