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This paper addresses the problem of controlling the speed of a number of automated vehicles before they enter a speed reduction zone on a freeway. We formulate the control problem and provide an analytical, closed-form solution that can be implemented in real time. The solution yields the optimal acceleration/deceleration of each vehicle under the hard safety constraint of rear-end collision avoidance. The effectiveness of the solution is evaluated through a microscopic simulation testbed and it is shown that the proposed approach significantly reduces both fuel consumption and travel time. In particular, for three different traffic volume levels, fuel consumption for each vehicle is reduced by 19–22% compared to the baseline scenario, in which human-driven vehicles are considered, by 12–17% compared to the variable speed limit algorithm, and by 18–34% compared to the vehicular-based speed harmonization (SPD-HARM) algorithm. Similarly, travel time is improved by 26–30% compared to the baseline scenario, by 3–19% compared to the VSL algorithm, and by 31–39% compared to the vehicular-based SPD-HARM algorithm. [ABSTRACT FROM AUTHOR]

We address the problem of optimally controlling connected and automated vehicles (CAVs) crossing an urban intersection without any explicit traffic signaling, so as to minimize energy consumption subject to a throughput maximization requirement. We show that the solution of the throughput maximization problem depends only on the hard safety constraints imposed on CAVs and its structure enables a decentralized optimal control problem formulation for energy minimization. We present a complete analytical solution of these decentralized problems and derive conditions under which feasible solutions satisfying all safety constraints always exist. The effectiveness of the proposed solution is illustrated through simulation which shows substantial dual benefits of the proposed decentralized framework by allowing CAVs to conserve momentum and fuel while also improving travel time. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) provide the most intriguing opportunity to improve energy efficiency, traffic flow, and safety. In earlier work, we addressed the constrained optimal coordination problem of CAVs at different traffic scenarios using Hamiltonian analysis. In this paper, we investigate the properties of the unconstrained problem and provide conditions under which different combination of the state and control constraints become active. We present a condition-based computational framework that improves on the standard iterative solution procedure of the constrained Hamiltonian analysis. Finally, we derive a closed-form analytical solution of the constrained optimal control problem and validate the proposed framework using numerical simulation. The solution can be derived without any recursive steps, and thus it is appropriate for real-time implementation on-board the CAVs. [ABSTRACT FROM AUTHOR]

Connected and automated vehicles (CAVs) provide the most intriguing opportunity to optimize energy consumption and travel time. Several approaches have been proposed in the literature that allow CAVs to coordinate in situations where there is a potential conflict, for example, in signalized intersections, merging at roadways and roundabouts, to reduce energy consumption and optimize traffic flow. In this paper, we consider the problem of coordinating CAVs in a corridor consisting of multiple traffic scenarios. We formulate a two-level optimization problem in which we maximize traffic throughput in the upper-level problem, and derive a closed-form analytical solution that yields the optimal control input for each CAV, in terms of fuel consumption, in the low-level problem. We validate the effectiveness of the solution through simulation under 100% CAV penetration rate. Fuel consumption and travel time for the vehicles are significantly reduced compared to a baseline scenario consisting of human-driven vehicles. [ABSTRACT FROM AUTHOR]