13,707 research outputs found
Pricing, Investment, and Network Equilibrium
Despite rapidly emerging innovative road pricing and investment principles, the development of a long run network dynamics model for necessary policy evaluation is still lagging. This research endeavors to fill this gap and models the impacts of road financing policies throughout the network equilibration process. The manner in which pricing and investment jointly shape network equilibrium is particularly important and explored in this study. The interactions among travel demand, road supply, revenue mechanisms and investment rules are modeled at the link level in a network growth simulator. After assessing several measures of effectiveness, the proposed network growth model is able to evaluate the short- and long-run impacts of a broad spectrum of road pricing and investment policies on large-scale road networks, which can provide valuable information to decision-makers such as the implications of various policy scenarios on social welfare, financial situation of road authorities and potential implementation problems. Some issues hard to address in theoretical analysis can be examined in the agent-based simulation model. As a demonstration, we apply the network growth model to assess marginal and average pricing scenarios on a sample network. Even this relatively simple application provides new insights into issues around road pricing that have not previously been seriously considered. For instance, the results disclose a potential problem of over-investment when the marginal cost pricing scheme is adopted in conjunction with a myopic profit-neutral investment policy.Transportation network equilibrium; Road growth; Pricing; Congestion toll; Investment; Transport policy analysis.
A Real-time Nonlinear Model Predictive Controller for Yaw Motion Optimization of Distributed Drive Electric Vehicles
This paper proposes a real-time nonlinear model
predictive control (NMPC) strategy for direct yaw moment control
(DYC) of distributed drive electric vehicles (DDEVs). The NMPC
strategy is based on a control-oriented model built by integrating
a single track vehicle model with the Magic Formula (MF) tire
model. To mitigate the NMPC computational cost, the
continuation/generalized minimal residual (C/GMRES) algorithm
is employed and modified for real-time optimization. Since the
traditional C/GMRES algorithm cannot directly solve the
inequality constraint problem, the external penalty method is
introduced to transform inequality constraints into an
equivalently unconstrained optimization problem. Based on the
Pontryagin’s minimum principle (PMP), the existence and
uniqueness for solution of the proposed C/GMRES algorithm are
proven. Additionally, to achieve fast initialization in C/GMRES
algorithm, the varying predictive duration is adopted so that the
analytic expressions of optimally initial solutions in C/GMRES
algorithm can be derived and gained. A Karush-Kuhn-Tucker
(KKT) condition based control allocation method distributes the
desired traction and yaw moment among four independent
motors. Numerical simulations are carried out by combining
CarSim and Matlab/Simulink to evaluate the effectiveness of the
proposed strategy. Results demonstrate that the real-time NMPC
strategy can achieve superior vehicle stability performance,
guarantee the given safety constraints, and significantly reduce the
computational efforts
Structural sensitivity analysis: Methods, applications, and needs
Some innovative techniques applicable to sensitivity analysis of discretized structural systems are reviewed. These techniques include a finite-difference step-size selection algorithm, a method for derivatives of iterative solutions, a Green's function technique for derivatives of transient response, a simultaneous calculation of temperatures and their derivatives, derivatives with respect to shape, and derivatives of optimum designs with respect to problem parameters. Computerized implementations of sensitivity analysis and applications of sensitivity derivatives are also discussed. Finally, some of the critical needs in the structural sensitivity area are indicated along with Langley plans for dealing with some of these needs
Implicit Cooperative Positioning in Vehicular Networks
Absolute positioning of vehicles is based on Global Navigation Satellite
Systems (GNSS) combined with on-board sensors and high-resolution maps. In
Cooperative Intelligent Transportation Systems (C-ITS), the positioning
performance can be augmented by means of vehicular networks that enable
vehicles to share location-related information. This paper presents an Implicit
Cooperative Positioning (ICP) algorithm that exploits the Vehicle-to-Vehicle
(V2V) connectivity in an innovative manner, avoiding the use of explicit V2V
measurements such as ranging. In the ICP approach, vehicles jointly localize
non-cooperative physical features (such as people, traffic lights or inactive
cars) in the surrounding areas, and use them as common noisy reference points
to refine their location estimates. Information on sensed features are fused
through V2V links by a consensus procedure, nested within a message passing
algorithm, to enhance the vehicle localization accuracy. As positioning does
not rely on explicit ranging information between vehicles, the proposed ICP
method is amenable to implementation with off-the-shelf vehicular communication
hardware. The localization algorithm is validated in different traffic
scenarios, including a crossroad area with heterogeneous conditions in terms of
feature density and V2V connectivity, as well as a real urban area by using
Simulation of Urban MObility (SUMO) for traffic data generation. Performance
results show that the proposed ICP method can significantly improve the vehicle
location accuracy compared to the stand-alone GNSS, especially in harsh
environments, such as in urban canyons, where the GNSS signal is highly
degraded or denied.Comment: 15 pages, 10 figures, in review, 201
Binary Search Algorithm for Mixed Integer Optimization: Application to energy management in a microgrid
This paper presents a binary search algorithm
to deal with binary variables in mixed integer optimization
problems. One example of this kind of problem is the optimal
operation of hydrogen storage and energy sale and purchase
into a microgrids context. In this work was studied a system
composed by a microgrid that has a connection with the
external electrical network and a charging station for electric
cars. The system modeling was carried out by the Energy
Hubs methodology. The proposed algorithm transforms the
MIQP (Mixed Integer Quadratic Program) problem into a QP
(Quadratic Program) that is easier to solve. In this way the
overall control task is carried out the electricity purchase and
sale to the power grid, maximizes the use of renewable energy
sources, manages the use of energy storages and supplies the
charge of the parked vehicles.Ministerio de EconomĂa y Competitividad DPI2013-46912-C2-1-RUniversidad de Sevilla CNPq401126/2014-5Universidad de Sevilla CNPq303702/2011-
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