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

A Scalable Approach for Short-Term Predictions of Link Traffic Flow by Online Association of Clustering Profiles

Short-term prediction of traffic flows is an important topic for any traffic management control room. The large availability of real-time data raises not only the expectations for high accuracy of the forecast methodology, but also the requirements for fast computing performances. The proposed approach is based on a real-time association of the latest data received from a sensor to the representative daily profile of one among the clusters that are built offline based on an historical data set using Affinity Propagation algorithm. High scalability is achieved ignoring spatial correlations among different sensors, and for each of them an independent model is built-up. Therefore, each sensor has its own clusters of profiles with their representatives; during the short-term forecast operation the most similar representative is selected by looking at the last data received in a specified time window and the proposed forecast corresponds to the values of the cluster representative

A demand model with departure time choice for within-day dynamic traffic assignment

A within-clay dynamic demand model is formulated, embodying, in addition to the classic generation, distribution and modal split stages, an actual demand model taking into account departure time choice. The work focuses on this last stage, represented through an extension of the discrete choice framework to a continuous choice set. The dynamic multimodal supply and equilibrium model based on implicit path enumeration, which have been developed in previous work are outlined here, to define within-day dynamic elastic demand stochastic multimodal equilibrium as a fixed point problem on users flows and transit line frequencies. A MSA algorithm capable, in the case of Logit route choice models, of supplying equilibrium flows and frequencies on real dimension networks, is presented, as well as the specific procedures implementing the departure time choice and actual demand models. Finally, the results obtained on a test network are presented and conclusions are drawn. (c) 2005 Elsevier B.V. All rights reserved

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