A novel paradigm for vehicular traffic in the era of connected and automated vehicles (CAVs) is proposed, which includes two combined principles: lane-free traffic and vehicle nudging; the latter implying that vehicles may be "pushing" (from a distance, using communication or sensors) other vehicles in front of them. This traffic paradigm features several advantages, including: smoother and safer driving; increase of roadway capacity; and no need for the anisotropy restriction. The proposed concept provides the possibility to actively design (rather than model or describe) the traffic flow characteristics in an optimal way, i.e. to engineer the future CAV traffic flow as an efficient artificial fluid. Options, features, related prior work, application domains and required research topics are discussed. Preliminary simulation results illustrate some basic features of the concept.

The paper investigates and provides results, including feedback control, for a nonlinear, hyperbolic, 1-D PDE system on a bounded domain. The considered model consists of two first-order PDEs with a dynamic boundary condition on the one end and actuation on the other. It is shown that, for all positive initial conditions, the system admits a globally defined, unique, classical solution that remains positive and bounded for all times; these properties are important, for example for traffic flow models. Moreover, it is shown that global stabilization can be achieved for arbitrary equilibria by means of an explicit boundary feedback law. The stabilizing feedback law depends only on collocated boundary measurements. The efficiency of the proposed boundary feedback law is demonstrated by means of a numerical example of traffic density regulation.

Eco-driving, a timely and well-known subject, aims at reducing fuel consumption by appropriately maneuvering a vehicle with a human or automated driver. In this work, the eco-driving problem is cast in an optimal control framework. State equations reflect the simple vehicle kinematics for position and speed, with the acceleration acting as a control input. Initial and final states (position and speed) are fixed. For the fuel consumption estimation, a number of alternatives are employed. To start with, a realistic, but nonlinear and non-smooth formula from the literature is considered. Simple smoothing procedures are then applied to enable the application of powerful numerical algorithms for the efficient solution of the resulting nonlinear optimal control problem. Furthermore, simpler quadratic approximations of the nonlinear formula are also considered, which enable analytical problem solutions. A comprehensive comparison on the basis of various driving scenarios demonstrates that the often utilized, but sometimes strongly questioned, square-of-acceleration term delivers excellent approximations for fuel minimizing trajectories in the present setting. A GLOSA (Green Light Optimal Speed Advisory) approach, based on the analytical solution of an optimal control problem is also presented.

The paper presents a movement strategy for Connected and Automated Vehicles (CAVs) in a lane-free traffic environment with vehicle nudging by use of an optimal control approach. State-dependent constraints on control inputs are considered to ensure that the vehicle moves within the road boundaries and to prevent collisions. An objective function, comprising various weighted sub-objectives, is designed, whose minimization leads to vehicle advancement at the desired speed, when possible, while avoiding obstacles. A nonlinear optimal control problem (OCP) is formulated for the minimization of the objective function subject to constraints for each vehicle. A computationally efficient Feasible Direction Algorithm (FDA) is called, on event-triggered basis, to compute in real-time the numerical solution for finite time-horizons within a Model Predictive Control (MPC) framework. The approach is applied to each vehicle on the road, while running simulations on a lane-free ring-road, for a wide range of vehicle densities and different types of vehicles. From the simulations, which create myriads of driving episodes for each involved vehicle, it is observed that the proposed approach is highly efficient in delivering safe, comfortable and efficient vehicle trajectories, as well as high traffic flow outcomes. The approach is under investigation for further use in various lane-free road infrastructures for CAV traffic.

Aiming at operating effectively future traffic systems, we propose here a novel methodology for integrated lane-changing and ramp metering control that exploits the presence of connected vehicles. In particular, we assume that a percentage of vehicles can receive and implement specific control tasks (e.g., lane-changing commands), while ramp metering is available via an infrastructure-based system or enabled by connected vehicles. The proposed approach is designed to robustly maximise the throughput at motorway bottlenecks employing a feedback controller, formulated as a Linear Quadratic Integral regulator, which is based on a simplified linear time invariant traffic flow model. We also present an extremum seeking algorithm to compute the optimal set-points used in the feedback controller, employing only the measurement of a cost that is representative of the achieved traffic conditions. The method is evaluated via simulation experiments, performed on a first-order, multi-lane, macroscopic traffic flow model, also featuring the capacity drop phenomenon, which allows to demonstrate the effectiveness of the developed methodology and to highlight the improvement in terms of the generated congestion.