In this paper, we design a stochastic model predictive control (MPC)-based traffic signal control method for urban networks when the uncertainties of the traffic model parameters (including the exogenous traffic flows and the turning ratios of downstream traffic flows) are taken into account. Considering that the traffic model parameters are random variables with known expectations and variances, the traffic signal control and coordination problem is formulated as a quadratic program with linear and second-order cone constraints. In order to reduce computational complexity, we suggest a way to decompose the optimization problem corresponding to the whole network into multiple subproblems. By applying an Alternating Direction Method of Multipliers (ADMM) scheme, the optimal stochastic traffic signal splits are found in a distributed manner. The effectiveness of the designed control method is validated via some simulations using VISSIM and MATLAB.
Distributed Stochastic MPC Traffic Signal Control for Urban Networks
IEEE Transactions on Intelligent Transportation Systems ; 24 , 8 ; 8079-8096
2023-08-01
8073913 byte
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
Urban traffic signal control based on distributed constraint satisfaction
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