Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow

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We develop a model-free approach to optimally control stochastic, Markovian systems subject to a reach-avoid constraint. Specifically, the state trajectory must remain within a safe set while reaching a target set within a finite time horizon. Due to the t ...

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Advanced driver assistance systems such as adaptive cruise control (ACC) and lane keeping system (LKS) potentially contribute to reducing crash rates and traffic congestion. On-road studies based on early ACC systems operational at medium-high speeds only ...

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Management of road traffic in urban settings remains a challenging problem. Perimeter control schemes proposed to alleviate congestion in large-scale urban networks usually assume noise-free measurements of the traffic state, which is problematic since mea ...

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Currently most optimization methods for urban transport networks (i) are suited for networks with simplified dynamics that are far from real-sized networks or (ii) apply decentralized control, which is not appropriate for heterogeneously loaded networks or ...

It was recently observed from empirical traffic data that by aggregating the highly scattered plots of flow versus density from individual loop detectors for city regions with homogeneous spatial distribution of congestion, the scatter significantly decrea ...

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Ensuing from the deteriorating conditions of road networks, traffic forecasting techniques with mathematical and computer-theory methods have been employed to address the real-time prediction of traffic conditions and their dynamic control, with the optimu ...

This paper deals with the nite horizon stochastic optimal control problem with the expectation of the p-norm as the objective function and jointly Gaussian, although not necessarily independent, additive disturbance process. We develop an approximation str ...

Distributed data-driven predictive control for cooperatively smoothing mixed traffic flow

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