Dynamic User Equilibrium (DUE)

The quantitative analysis of road network traffic performed through static assignment models yields the transport demand-supply equilibrium under the assumption of within-day stationarity. This implies that the relevant variables of the system (i.e. user flows, travel times, costs) are assumed to be constant over time within the reference period. Although static assignment models satisfactorily reproduce congestion effects on traffic flow and cost patterns, they do not allow to represent the variation over time of the demand flows (i.e. around the rush hour) and of the network performances (i.e. in presence of time varying tolls, lane usage, signal plans, link usage permission); most importantly, they cannot reproduce some important dynamic phenomena, such as the formation and dispersion of vehicle queues due to the temporary over-saturation of road sections, and the spillback, that is queues propagation towards upstream roads

calibration of the demand structure for dynamic traffic assignment using flow and speed data exploiting the advantage of distributed computing in derivative free optimization algorithms

Abstract Stochastic optimization algorithms have been used in the recent literature as a preferred way for calibrating Dynamic Traffic Assignment (DTA) models, as the computation of explicit gradients is numerically too cumbersome on real networks. However, early experiences based on the Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm have shown performance issues when the number of variables becomes large. This suggests to focus on structural demand variables rather than to consider all components of origin-destination (O-D) matrices. Moreover, with the possibility of distributed computing, many algorithms that where not efficient in a standard configuration (i.e. sequential objective function evaluations within each iteration) can become a viable alternative to SPSA. For example, parallelization can be especially beneficial for genetic algorithms, which require a large number of independent function evaluations per iteration. In this paper we examine several optimization algorithms applied to dynamic demand calibration using flow and speed field measurements. The problem is to minimize the distance between results of a dynamic network loading and traffic data observed on road links. This approach is investigated in the context of laboratory experiments, where known O-D matrices are perturbed after its dynamic assignment on the network, to prove the effectiveness of the proposed methodology

No Fermionic Wigs for BPS Attractors in 5 Dimensions

We analyze the fermionic wigging of 1/2-BPS (electric) extremal black hole attractors in N=2, D=5 ungauged Maxwell-Einstein supergravity theories, by exploiting anti-Killing spinors supersymmetry transformations. Regardless of the specific data of the real special geometry of the manifold defining the scalars of the vector multiplets, and differently from the D=4 case, we find that there are no corrections for the near--horizon attractor value of the scalar fields; an analogous result also holds for 1/2-BPS (magnetic) extremal black string. Thus, the attractor mechanism receives no fermionic corrections in D=5 (at least in the BPS sector).Comment: 24 pages, LaTeX2

Fast estimation of point-to-point travel times for real-time vehicle routing

Abstract To provide the optimal allocation of requests to the available fleet vehicles, routing algorithms typically assume the availability of complete and correct information about point-to-point travel times. Actually, in real applications non-recurrent events and traffic conditions make the estimation and the prediction of such travel times a difficult task, further complicated in real-time applications by the dynamicity of the information and the number of needed estimates. In this paper we present a complete methodology to achieve a computation of point-to-point travel times on a large network which proves to be both extremely fast and consistent with dynamically updated traffic information

Resonance absorption enhancement in laser-generated plasma ablating Cu treated surfaces

AbstractResonant absorption effects for 1.064 µm infrared laser pulse radiations are investigated by using different techniques producing micrometric surface structures with dimensions comparable to the wavelength value. The laser absorption is controlled through measurement of the Cu ion acceleration using time-of-flight approach. Surface treatments include low energy laser etching in air, deposition of microspheres obtained ablating Cu targets in water, pulse laser deposition of microstructures precursor of thin homogeneous film, chemical etching with HNO3 acid, Ar+ ion sputtering and rolling burnishing surface of thin Cu foils. Results indicate that the best resonance effect is obtained with the rolling burnishing, ion sputtering and microsphere deposition processes which enhance the Cu ion energy and the yield emission