Urban Air Mobility (UAM) offers a solution to current traffic congestion by using electric Vertical Takeoff and Landing (eVTOL) vehicles to provide on-demand air mobility in urban areas. Effective traffic management is crucial for efficient operation of UAM systems, especially for high-demand scenarios. In this paper, we present a centralized framework for conflict-free takeoff scheduling of eVTOLs in on-demand UAM systems. Specifically, we provide a scheduling policy, called VertiSync, which jointly schedules UAM vehicles for servicing trip requests and rebalancing, subject to safety margins and energy requirements. We characterize the system-level throughput of VertiSync, which determines the demand threshold at which the average waiting time transitions from being stable to being increasing over time. We show that the proposed policy maximizes throughput for sufficiently large fleet size and if the UAM network has a certain symmetry property. We demonstrate the performance of VertiSync through a case study for the city of Los Angeles, and show that it significantly reduces average passenger waiting time compared to a first-come first-serve scheduling policy.

Intersections are major sources of traffic delays and accidents. Unlike traffic light controlled intersections, signal free intersections coordinate safe vehicle crossings individually, offering greater flexibility and the potential to eliminate unnecessary stops. Moreover, the high cost of installing and maintaining physical traffic signals motivates the adoption of virtual infrastructure enabled by connectivity and fast computation. This paper proposes a centralized intersection controller for real-time coordination of the dynamic traffic demands of Connected and Autonomous Vehicles (CAVs) at a signal free intersection. The proposed method consists of a centralized intersection controller that, in real time, assigns each CAV a safe reference path and desired speed based on its location, current speed, onboard vehicle controller transient response, and origin–destination pair, rather than transmitting direct acceleration commands to individual vehicles. Due to errors in measuring acceleration and susceptibility to delays, tracking a reference speed is a more robust and practical approach as it takes into account the ability of onboard vehicle controllers to track speeds in a more accurate way than accelerations. Moreover, the proposed approach allows vehicles to be in any lane when making left and right turns, which has been shown to improve efficiency. The proposed method was evaluated via multi-agent simulations in the open-source CARLA environment. It was compared against four benchmark methods: fixed- and variable cycle time traffic light controlled intersections (with variable cycle and phase plan optimized using Webster’s method) and two state-of-the-art signal free coordination methods. Results show that the proposed approach reduces average delays and increases the average lane flow rate compared to these benchmarks. As expected, the magnitude of improvement depends on the demand.

The recent emergence of new technologies and systems such as connected and automated vehicles (CAVs), novel incentive and routing platforms, and shared mobility services is making a significant impact on traffic flow in road networks. The rapid development of these innovations, powered by new capabilities in data collection, communication, and vehicle autonomy raises both great opportunities and new challenges for managing and controlling the transportation network efficiently. It is thus imperative to integrate the emerging systems into a dynamic transportation network analysis, and to develop new methodologies, which coherently integrate dynamic traffic models with increasingly available data, and methods for large-scale computation. Consequently, they call for new theories, models, computational methods, and application scenarios to study dynamic transportation networks with the emerging technologies as essential components.

Increasing traffic volume with respect to physical space motivates explicit consideration of space constraint in traffic system analysis. We study dynamics of a system of homogeneous vehicles executing safe vehicle following on a closed single lane ring road. Dynamics of each vehicle is governed by a standard second order model and a two mode vehicle following controller. One mode is cruise control and the other is a constant time headway control for safety; switching between modes is determined by a linear combination of relative distance and speed. We show that there exists a threshold value for the number of vehicles at which the equilibria for inter-vehicle configurations transition from being infinite to being unique. We explicitly characterize the unique equilibrium in the latter case as well as the threshold value for transition in terms of system parameters (road length, constant time headway and free flow speed). We also show that, starting from any initial condition, the inter-vehicle configuration converges to an equilibrium. The threshold value for the number of vehicles is also shown to define the boundary of when the transfer function from external disturbance to error in relative spacing changes.

Formal methods for task management for human operators are gathering increasing attention to improve efficiency of human-in-the-loop systems. In this paper, we consider a novel dynamical queue approach to intelligent task management for human operators. We consider a model of a dynamical queue, where the service time depends on the server utilization history. The proposed queueing model is motivated by, but not restricted to, widely accepted empirical laws describing human performance as a function of mental arousal. The focus of the paper is to characterize the throughput of the dynamical queue and design corresponding maximally stabilizing task release control policies, assuming deterministic arrivals. We focus extensively on threshold policies that release a task to the server only when the server state is less than a certain threshold. When every task brings in the same deterministic amount of work, we give an exact characterization of the throughput and show that an appropriate threshold policy is maximally stabilizing. The technical approach exploits the optimality of the one-task equilibria class associated with the server dynamics. When the amount of work associated with the tasks is an independent identically distributed (i.i.d.) random variable with finite support, we show that the maximum throughput increases in comparison to the case where the tasks have the same deterministic amount of work. Finally, we provide preliminary empirical evidence in support of the applicability of the proposed approach to systems with human operators.

Recent years have witnessed great advancements in the science and technology of autonomy, robotics, and networking. This paper surveys recent concepts and algorithms for dynamic vehicle routing (DVR), that is, for the automatic planning of optimal multivehicle routes to perform tasks that are generated over time by an exogenous process. We consider a rich variety of scenarios relevant for robotic applications. We begin by reviewing the basic DVR problem: demands for service arrive at random locations at random times and a vehicle travels to provide on-site service while minimizing the expected wait time of the demands. Next, we treat different multivehicle scenarios based on different models for demands (e.g., demands with different priority levels and impatient demands), vehicles (e.g., motion constraints, communication, and sensing capabilities), and tasks. The performance criterion used in these scenarios is either the expected wait time of the demands or the fraction of demands serviced successfully. In each specific DVR scenario, we adopt a rigorous technical approach that relies upon methods from queueing theory, combinatorial optimization, and stochastic geometry. First, we establish fundamental limits on the achievable performance, including limits on stability and quality of service. Second, we design algorithms, and provide provable guarantees on their performance with respect to the fundamental limits.