Unscented stochastic model predictive perimeter control of uncertain two-regions urban traffic

Harati, Ehsan; Timotheou, Stelios; Panayiotou, Christos G.

doi:10.1016/j.ifacol.2020.12.1978

Cited by 4 publications

(1 citation statement)

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“…The robust method is conservative in dealing with stochastic problems [26]. To address the uncertainties of MFDs, stochastic model predictive control (SMPC) has been developed for two‐region urban traffic systems [27]. In SMPC, a stochastic model is used to predict the future behavior of the system, an optimization problem is solved at each time step, and the goal of optimization is to minimize a cost function while satisfying the system constraints.…”

Section: Introductionmentioning

confidence: 99%

Risk‐averse perimeter control for alleviating the congestion of an urban traffic network system with uncertainties

Shi,

Zhang,

Yin

et al. 2023

IET Intelligent Trans Sys

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The perimeter control method is an effective way to alleviate traffic congestion that is based on Macroscopic Fundamental Diagrams (MFDs). However, the strategy may lead to congestion when it ignores the uncertainty of MFDs. To address this problem, this paper presents a risk‐averse perimeter control method. First, the urban traffic network system with uncertainty is modeled using a neural network and scenario tree. Then, this research quantifies the congestion risk caused by uncertainty using an average value‐at‐risk. The next step sees the design of a risk‐averse model predictive control (MPC) controller that takes the multi‐stage risk as the optimization objective and improves robustness by interpolating between the conventional stochastic and worst‐case MPC formulations. Finally, this paper analyzes the risk‐sensitive stability of an urban traffic network system and gives a solvable form of risk‐averse optimal control for this system. Finally, two simulations are conducted to verify the presented method's validity and superiority for an urban traffic network system with uncertainties. The simulation results show that the risk‐averse perimeter control method presented by this paper is superior because it reduces the total travel time by 12.98% compared to Stochastic MPC, by 15.96% compared to bang‐bang control, and by 14.54% compared to proportional‐integral.

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Section: Introductionmentioning

confidence: 99%