Agent Based Individual Trafficguidance

When working with traffic planning or guidance it is common practice to view the vehicles as a combined mass. From this models are employed to specify the vehicle supply and demand for each region. As the models are complex and the calculations are equally demanding the regions and the detail of the road network is aggregated. As a result the calculations reveal only what the mass of vehicles are doing and not what a single vehicle is doing. This is the crucial difference to Agent Based Individual Traffic guidance (ABIT). ABIT is based on the fact that information on the destination of each vehicle can be obtained. This information can then be used to provide individual traffic guidance as opposed to the mass information systems of today – dynamic road signs and traffic radio. The goal is to achieve better usage of the road and time. The main topic of this paper is the current development in both practical and theoretical fields concerning the realization of ABIT

Agent Based Individual Trafficguidance

When working with traffic planning or guidance it is common practice to view the vehicles as a combined mass. From this models are employed to specify the vehicle supply and demand for each region. As the models are complex and the calculations are equally demanding the regions and the detail of the road network is aggregated. As a result the calculations reveal only what the mass of vehicles are doing and not what a single vehicle is doing. This is the crucial difference to Agent Based Individual Traffic guidance (ABIT). ABIT is based on the fact that information on the destination of each vehicle can be obtained. This information can then be used to provide individual traffic guidance as opposed to the mass information systems of today – dynamic road signs and traffic radio. The goal is to achieve better usage of the road and time. The main topic of this paper is the current development in both practical and theoretical fields concerning the realization of ABIT

Simulation and optimization in transportation

During the last two decades, the use of simulation tools in transportation engineering has become inevitable. Many methodologies developed by the research community during the late 90s have made their way to commercial softwares, used daily by practitioners. These tools are convenient for so-called scenario based analysis, that is the derivation of indicators of performance under various design scenarios. Still, there are several pitfalls to be aware of when using them. It is also highly desirable to use them in a more systematic way, and to include them in a optimization framework. In this lecture, we discuss the pitfalls of simulation, and review the issues related to simulation-based optimization. We also report some recent developments in the field of transportation

Disaggregate path flow estimation in an iterated DTA microsimulation

This text describes the first application of a novel path flow and origin/destination (OD) matrix estimator for iterated dynamic traffic assignment (DTA) microsimulations. The presented approach, which operates on a trip-based demand representation, is derived from an agent-based DTA calibration methodology that relies on an activity-based demand model (Flötteröd et al., 2011a). The objective of this work is to demonstrate the transferability of the agent-based approach to the more widely used OD matrix-based demand representation. The calibration (i) operates at the same disaggregate level as the microsimulation and (ii) has drastic computational advantages over conventional OD matrix estimators in that the demand adjustments are conducted within the iterative loop of the DTA microsimulation, which results in a running time of the calibration that is in the same order of magnitude as a plain simulation. We describe an application of this methodology to the trip-based DRACULA microsimulation and present an illustrative example that clarifies its capabilities

Disaggregate path flow estimation in an iterated DTA microsimulation

This text describes the first application of a novel path flow and origin/destination (OD) matrix estimator for iterated dynamic traffic assignment (DTA) microsimulations. The presented approach, which operates on a trip-based demand representation, is derived from an agent-based DTA calibration methodology that relies on an activity-based demand model. The objective of this work is to demonstrate the transferability of the agent-based approach to the more widely used OD matrixbased demand representation. The calibration (i) operates at the same disaggregate level as the microsimulation and (ii) has drastic computational advantages over usual OD matrix estimators in that the demand adjustments are conducted within the iterative loop of the DTA microsimulation, which results in a running time of the calibration that is in the same order of magnitude as a plain simulation. We describe an application of this methodology to the trip-based DRACULA microsimulation and present an illustrative example that clarifies its capabilities

Simulation and optimization in transportation: a short review

This review discusses some issues related to the use of simulation in transportation analysis. Potential pitfalls are identified and discussed. An overview of some methods relevant to the use of an advanced simulation tool in an optimization context is also provided

Bayesian calibration of dynamic traffic simulations

We present an operational framework for the calibration of demand models for dynamic traffic simulations. Our focus is on disaggregate simulators that represent every traveler individually. We calibrate, at a likewise individual level, arbitrary choice dimensions within a Bayesian framework, where the analyst's prior knowledge is represented by the dynamic traffic simulator itself and the measurements are comprised of sensor data such as traffic counts. The approach is equally applicable to an equilibrium-based planning model and to a telematics model of spontaneous and imperfectly informed drivers. It is based on consistent mathematical arguments, yet applicable in a purely simulation-based environment, and, as our experimental results show, capable of estimating practically relevant scenarios in real-time

Solving noisy large scale fixed point problems and systems of nonlinear equations

Many complex transportation models can be formulated as fixed-point problems. Typical examples are the equilibrium-like models, motivated by the need to capture the interaction between the transport supply (that is, the infrastructure) and transport demand (that is, travelers behavior) in various ways. In this paper, we propose a new approach for solving fixed-point problems and, equivalently, systems of nonlinear equations. It is a generalization of secant methods, and uses several previous iterates to generate a linear approximation of the nonlinear function. Although it belongs to the quasi-Newton family of methods, our algorithm is matrix free, allowing it to solve large-scale systems of equations without derivative and without any particular assumption about the structure of the problem or its Jacobian. Computational experiments on standard problems show that this algorithm outperforms classical, large-scale quasi-Newton methods in terms of efficiency and robustness. Its numerical performances are similar to Newton- Krylov methods currently considered as the best algorithms to solve large-scale nonlinear systems of equations. Furthermore, our approach, using multiple previous iterates to build a linear approximation of the nonlinear function, exhibits robust behavior in the presence of noise in the equations, which makes it particularly well adapted to transportation problems. We run our algorithm on a simple origin-destination (OD) matrix estimation problem and on some instances of the consistent anticipatory route guidance (CARG) problem, to illustrate the applicability of the proposed method and to show its significant superiority to the classical method of successive averages (MSA). Regarding size, we were able to solve a CARG problem with more than 120,000 variables. We were also able to solve classical problems with up to two million variables