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We develop reinforcement learning (RL) boundary controllers to mitigate stop-and-go traffic congestion on a freeway segment. The traffic dynamics of the freeway segment are governed by a macroscopic Aw–Rascle–Zhang (ARZ) model, consisting of 2 $\times $ 2 quasi-linear partial differential equations (PDEs) for traffic density and velocity. The boundary stabilization of the linearized ARZ PDE model has been solved by PDE backstepping, guaranteeing spatial $L^{2}$ norm regulation of the traffic state to uniform density and velocity and ensuring that traffic oscillations are suppressed. Collocated proportional (P) and proportional–integral (PI) controllers also provide stability guarantees for allowable control gains and are always applicable as model-free control options through gain tuning by trial and error, or by model-free optimization. Although these approaches are mathematically elegant, the stabilization result only holds locally and is usually affected by the change of model parameters. Therefore, we reformulate the PDE boundary control problem as an RL problem that pursues stabilization without knowing the system dynamics, simply by observing the state values. The proximal policy optimization (PPO), a neural network-based policy gradient algorithm, is employed to obtain RL controllers by interacting with a numerical simulator of the ARZ PDE. Being stabilization-inspired, the RL state-feedback boundary controllers are compared and evaluated against the rigorously stabilizing controllers in two cases: 1) in a system with perfect knowledge of the traffic flow dynamics and then 2) in one with only partial knowledge. We obtain RL controllers that nearly recover the performance of the backstepping, P, and PI controllers with perfect knowledge and outperform them in some cases with partial knowledge. It must be noted, however, that the RL controllers are obtained by conducting about one thousand episodes of iterative training on a simulation model. This training cannot be performed in a collision-free fashion in real traffic, nor convergence guaranteed when training. Thus, we demonstrate that the RL approach has learning (i.e., adaptation) potential for traffic PDE systems under uncertain and changing conditions, but RL is neither simple nor a fully safe substitute for model-based control in real traffic systems. [ABSTRACT FROM AUTHOR]

Smart city technologies can provide substantial benefits that improve people's daily lives. Here, the authors investigate how ubiquitous traffic sensing technologies and techniques can be incorporated with conventional traffic control and monitoring practices in the city of Oulu, Finland. In collaboration with the city's traffic control center and traffic planners, they exploit inductive magnetic sensing and Wi-Fi scanning across the city center, and develop tools to assist traffic operators in their tasks. [ABSTRACT FROM PUBLISHER]

Vehicular communications networks are envisioned for the access to drive-thru Internet and IP-based infotainment applications. These services are supported by roadside access routers (ARs) that connect vehicular ad hoc networks (VANETs) to external IP networks. However, VANETs suffer from asymmetric links due to variable transmission ranges caused by mobility, obstacles, and dissimilar transmission power, which make it difficult to maintain the bidirectional connections and to provide the IP mobility required by most IP applications. Moreover, vehicular mobility results in short-lived connections to the AR, affecting the availability of IP services in VANETs. In this paper, we study the secure and timely handover of IP services in an asymmetric VANET and propose a multihop-authenticated Proxy Mobile IP (MA-PMIP) scheme. MA-PMIP provides an enhanced IP mobility scheme over infrastructure-to-vehicle-to-vehicle (I2V2V) communications that uses location and road traffic information. The MA-PMIP also reacts, depending on the bidirectionality of links, to improve availability of IP services. Moreover, our scheme ensures that the handover signaling is authenticated when V2V paths are employed to reach the infrastructure so that possible attacks are mitigated without affecting the performance of the ongoing sessions. Both analysis and extensive simulations in OMNeT++ are conducted, and the results demonstrate that the MA-PMIP improves service availability and provides secure seamless access to IP applications in asymmetric VANETs. [ABSTRACT FROM AUTHOR]

Robust road-boundary extraction/tracking is one of the main problems in autonomous roadway navigation. Although the road boundary can be defined by various means including lane markings, curbs, and borders of vegetation, this paper focuses on road-boundary tracking using curbs. A vehicle-mounted (downward tilted) 2-D laser-measurement system is utilized to detect the curbs. The tracking problem is difficult because both the vehicle is moving and the target is disappearing, reappearing, and maneuvering in clutter. The interacting-multiple-model probabilistic-data-association filter (IMMPDAF) is proposed to solve the problems after detailed analysis. Track initiation, confirmation, and deletion are performed using the sequential-probability-ratio test. Extensive simulations followed by experiments in a campus environment show that the road-boundary tracking utilizing curbs is possible and robust through IMMPDAF. [ABSTRACT FROM AUTHOR]

In this paper, an iterative learning approach for the freeway density control under ramp metering and speed regulation is developed in a macroscopic level traffic environment. Rigorous analyses show that the proposed learning control schemes guarantee the asymptotic convergence of the traffic density to the desired one. The two major features of the learning-based density control are 1) less prior modeling knowledge required in the control system design and 2) the ability to reject exogenous traffic perturbations. The control schemes are applied to a freeway model, and simulation results confirm the efficacy of the proposed approach. [ABSTRACT FROM AUTHOR]

The article provides information on the methods used by young artificial intelligence (AI) researchers to improve the urban traffic flow with the use of an artificial intelligence (AI)-based traffic control. Carlos Gershenson, a postdoctoral student at the New England Complex Systems Institute and Vrije Universiteit Brussel, devised a rule-based AI program to improve the city's traffic light system. Marco Wiering, assistant professor of AI, works at Utrecht University to devise a system which will help drivers to communicate their destination through a computer link to the city's traffic light grid. Professors Kurt Dresner and Peter Stone at the University of Texas invented a system which lets driven vehicles have a reserve space within an intersection.