Transportation contributes a substantial fraction of all greenhouse gas emissions. One approach for reducing such emissions is to modify vehicles’ route choices to minimize their fuel consumption or emission, which is known as eco-routing. Most eco-routing is based on vehicles choosing routes that minimize their individual fuel consumption or emissions. The Braess paradox demonstrates that when vehicles choose routes to minimize their individual goals, the aggregate effect may paradoxically result in the opposite net effect due to changes in congestion patterns. We construct a multiclass user equilibrium model in which some vehicles use eco-routing and others seek to minimize their individual travel times. Using this model, we show that the Braess paradox exists for eco-routing. If a large number of vehicles is trying to minimize their fuel consumption or emissions, the total fuel consumption or emissions may increase. We then solve the multiclass user equilibrium on publicly available city network data, and find that eco-routing results in increases in fuel consumption and emissions on some city networks as well.

Emerging vehicle sensing and communication technologies allow for real-time information exchange between connected vehicles (CVs) and intelligent infrastructure. This presents a unique opportunity for predicting traffic states such as speed and density. A promising application of traffic prediction is eco-driving speed control of CVs, which requires future traffic information along the look-ahead time horizon. However, it is challenging to obtain accurate real-time traffic prediction for the next 10-15 s, particularly for mixed traffic involving both CVs and human-driven vehicles (HVs), complicated further by the presence of lane changing maneuvers. In this article, we address this pressing problem by integrating a macroscopic traffic flow model for prediction with a microscopic vehicle model for speed control. Specifically, we modify the well-known second-order Payne-Whitham (PW) model to account for the impacts of lane changing on traffic state evolution, based on which we develop a traffic prediction framework capable of handling mixed traffic. CVs provide partial measurements of traffic states, while the unknown states are estimated using an unscented Kalman filter (UKF). Consequently, future traffic states are obtained by propagating the PW model forward in time, and optimal eco-driving speed controls are obtained for electric vehicles (EVs) using the prediction results. The proposed approach is evaluated using ample traffic data collected from Simulation of Urban MObility (SUMO). The results show an average energy benefit of 6.6% for the ego vehicle considering all the simulated scenarios, among which the maximum energy benefit is about 16.18%.

In this work we present a traffic model to simulate network-level traffic evolution under the impact of controlled autonomous vehicles acting as moving bottlenecks. We first extend the Newell-Daganzo method to track the trajectories of moving bottlenecks and calculate the cumulative number of vehicles passing each moving bottleneck. By integrating the solutions to the cumulative number of vehicles passing moving bottlenecks and link nodes as boundary conditions in the link-transmission model, we can incorporate the impact of moving bottlenecks into the flow of traffic at a network scale. We present numerical simulation results that illustrate the effectiveness of the developed model to track the trajectories of the moving bottlenecks and simulate their impact on freeway traffic. Lastly, we present control applications of the developed model to trajectory optimization. The reduced fuel consumption associated with the careful control of AV trajectories in the moving bottleneck framework indicates the potential to considerably improve the flow of traffic by controlling the AVs in a mixed human and autonomous environment.

Shared autonomous vehicles (SAVs) provide on demand point-to-point transportation for passengers. This service has been extensively studied using dispatch heuristics and agent based simulations of large urban areas. However, these approaches make no mathematical guarantees of the passenger throughput for the SAV network. This study builds on the dynamic queuing model design of Kang and Levin which provides a maximum stability dispatch policy for SAVs. This model is extended to include rebalancing of empty vehicles to regions of high demand. The modified dispatch policy is proven to maximize throughput. Simulation results show that this dispatch policy reduces waiting times (between vehicle dispatch and passenger pickup) compared to the original formulation. However, vehicle time traveling empty increases in some scenarios. Simulation results also show that rebalancing often reduces passenger waiting times, but not when too many vehicles rebalance at once and are not available for dispatch.

This article proposes a new approach for computing a semiexplicit form of the solution to a class of traffic flow problems encoded by a Hamilton–Jacobi (HJ) partial differential equation (PDE), with time-switched Hamiltonian. Using a characterization of the problem derived from viability theory, we show that the solution associated with the problem can be formulated as a minimization problem involving the trajectory of an auxiliary dynamical system. A generalized Lax–Hopf formula for the switched Hamiltonian problem is derived, which enables us to compute the solution associated with affine initial or boundary conditions as a linear program involving the control function of the auxiliary dynamical system. This formulation allows us to compute the solution to the original problem exactly, unlike dynamic programming methods. In addition, this method allows one to very efficiently recompute the boundary conditions associated with an initial condition problem, allowing large-scale variable speed limit traffic control problems to be solved.Note to Practitioners—Most dynamic speed limit control techniques used to manage traffic flow on highways rely on discretizations of partial differential equations, which require one to compute the solution on a computational grid. This article focuses on an alternate solution method that does not require the solution to be found on all grid points, potentially saving computational time on large-scale problems.

Uniform traffic flow has been shown to be unstable in certain flow regimes due to collective behaviors of human drivers, resulting in the well-observed stop-and-go waves. These traffic waves can arise even in the absence of merges, bottlenecks, or lane changing, and may lead to higher vehicle fuel consumption and emissions. In this article, we aim to smooth unstable traffic flow via optimal control of autonomous vehicles (AVs) in a predominantly human-driven traffic flow. These controlled AVs act as mobile actuators in the traffic without changing the way human-driven vehicles (HVs) normally operate. We develop a dynamic model to describe mixed traffic flow in the presence of both HVs and AVs, whose dynamics follow general nonlinear car-following principles. Based on this general framework, we formulate an optimal control problem with the objective of minimizing vehicle speed perturbation, and prove the existence of optimal AV control policy. Following the necessary conditions of optimality prescribed by the well-known Pontryagin’s minimum principle, we present a computational algorithm to determine the optimal AV control strategy and prove its convergence. The mathematical model is further illustrated using the intelligent driver model (IDM) and optimal velocity with relative velocity (OVRV) model for HVs and AVs, respectively. Numerical results are presented to show the effectiveness of the proposed approach on traffic smoothing, as well as the improvement on vehicle fuel economy.

Tile-based reservation intersection control for autonomous vehicles has the potential to reduce intersection delays beyond optimized traffic signals. A major question in implementing reservations is the underdetermined problem of resolving conflicting reservation requests. Previous work studied prioritizing requests by first come first served or holding auctions at intersections, but the possibilities are infinite. Furthermore, although selfish routing behavior could affect the benefits of the reservation prioritization, reservation control has not been studied with user equilibrium routing due to its microsimulation definition. This paper addresses these issues by presenting an integer program formulation of the conflict point simplification of reservations. The feasible region is transformed, resulting in a more tractable integer program on conflict regions for dynamic traffic assignment. Because the integer program is NP-hard we present a polynomial-time heuristic. Finally, we demonstrate the potential utility of this heuristic by demonstrating objective functions that reduce travel time and energy consumption on a city network.