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The evermore widespread use of microscopic traffic simulation in the analysis of road systems has refocused attention on submodels, including car-following models. The difficulties of microscopic-level simulation models in the accurate reproduction of real traffic phenomena stem not only from the complexity of calibration and validation operations but also from the structural inadequacies of the submodels themselves. Both of these drawbacks originate from the scant information available on real phenomena because of the difficulty with the gathering of accurate field data. In this study, the use of kinematic differential Global Positioning System instruments allowed the trajectories of four vehicles in a platoon to be accurately monitored under real traffic conditions on both urban and extraurban roads. Some of these data were used to analyze the behaviors of four microscopic traffic flow models that differed greatly in both approach and complexity. The effect of the choice of performance measures on the model calibration results was first investigated, and intervehicle spacing was shown to be the most reliable measure. Model calibrations showed results similar to those obtained in other studies that used test track data. Instead, validations resulted in higher deviations compared with those from previous studies (with peaks in cross validations between urban and extraurban experiments). This confirms the need for real traffic data. On comparison of the models, all models showed similar performances (i.e., similar deviations in validation). Surprisingly, however, the simplest model performed on average better than the others, but the most complex one was the most robust, never reaching particularly high deviations.

SAE_NA 2003-01-057

This paper proposes a simulation-based optimization methodology for the efficient calibration of microscopic traffic flow models (i.e., car-following models) of large-scale stochastic network simulators. The approach is a metamodel simulation-based optimization (SO) method. To improve computational efficiency of the SO algorithm, problem-specific and simulator-specific structural information is embedded into a metamodel. As a closed-form expression is sought, we propose adopting the steady-state solution of the car-following model as an approximation of its simulation-based input-output mapping. This general approach is applied for the calibration of the Gipps car-following model embedded in a microscopic traffic network simulator, on a large network. To this end, a novel formulation for the traffic stream models corresponding to the Gipps car-following law is provided. The proposed approach identifies points with good performance within few simulation runs. Comparing its performances to that of a traditional approach, which does not take advantage of the structural information, the objective function is improved by two orders of magnitude in most experiments. Moreover, this is achieved within tight computational budgets, i.e., few simulation runs. The solutions identified improve the fit to the field measurements by one order of magnitude, on average. The structural information provided to the metamodel is shown to enable the SO algorithm to become robust to both the quality of the initial points and the simulator stochasticity., U.S. National Science Foundationunder Grant No. 1334304

Recently the growing demand in railway transportation has raised the need for practitioners to improve the design of railway systems. This means to identify a configuration of the infrastructure components (e.g. number of rail tracks, type and layout of the signalling system, layout of station tracks) and the operational schedule (e.g. train headways, scheduled dwell times) that improves given measures of performance such as the level of capacity, the punctuality of the service and the energy saving.Planners and designers involved in this process have the hard task of determining sound design solutions in order to achieve certain levels of network performances in a cost-effective way, especially when investment funds are limited. To this aim, a sensitivity analysis can support early decisional phases in order to better understand dependencies between performances and design variables and drive the decisional process towards effective solutions.In this paper, the Sobol variance-based method is applied to this purpose. A practical application has been carried out for a mass transit line in the city of Naples. Such study has investigated how train delays and energy consumption are affected by variations in design variables relative to the operational plan, the signalling system and factors related to the layout of station platforms. Results highlight the ability of this analysis in explaining the effects of different design solutions from a statistical point of view and finding the most influential factors for a given performance. This aspect suggests to practitioners the usefulness of this approach in addressing decisions towards cost-effective interventions. �� 2013 Elsevier Ltd.

Car-following models are widely used by researchers and practitioners for road traffic studies. Although dozens of them have been presented so far, the model proposed by P.G. Gipps in 1981 is still one of the most extensively used. However, many features of the model are still rather unknown or neglected in common applications. In this context, the present paper wishes i) to summarize and analyze the main findings available in the scientific literature for the Gipps??? car-following model and ii) to introduce some novel features of the same model that may improve its capability to reproduce real trajectory data. In particular, the structure of the acceleration component of the model is analytically investigated, in particular for what concerns the meaning of some parameters that in the common practice are usually kept to some empirically derived fixed values. Different possible versions of the Gipps??? model are presented and their performances to reproduce real vehicle trajectories are evaluated and compared. The results achieved show the necessity for these parameters to be calibrated in order to improve the predictive capabilities of the model.

This paper examines a metamodel-based technique for model sensitivity analysis and applies it to the Aimsun mesoscopic model. The paper argues that sensitivity analysis is crucial for the best use of the traffic simulation models while also acknowledging that the main obstacle to an extensive use of the most sophisticated techniques is the high number of model runs they usually require. To get around this problem, the paper considers the possibility of performing sensitivity analysis not on a model but on its metamodel approximation. Important issues arising when estimating a metamodel have been investigated and commented on in the specific application to the Aimsun model. Among these issues is the importance of selecting a proper sampling strategy based on low discrepancy random number sequences and the importance of selecting a class of metamodels able to reproduce the inputs-ouputs relationship in a robust and reliable way. Sobol sequences and Gaussian process metamodels have been recognized as the appropriate choices. The paper assesses the proposed methodology by comparing the results of the application of variance- based sensitivity analysis techniques to the simulation model and to a metamodel estimated with 512 model runs, on a variety of traffic scenarios and model outputs. Results confirm the powerfulness of the proposed methodology and also open up to a more extensive application of sensitivity analysis techniques to complex traffic simulation models., JRC.F.8-Sustainable Transport

This study adopted a metamodel-based technique for model sensitivity analysis and applied it to the AIMSUN mesoscopic model. The application of sensitivity analysis is crucial for the true comprehension and correct use of the traffic simulation model, although the main obstacle to an extensive use of the most sophisticated techniques is the high number of model runs such techniques usually require. For this reason, the possibility of performing a sensitivity analysis was tested not on a model but on its metamodel approximation. Important issues concerning metamodel estimation were investigated and commented on in the specific application to the AIMSUN model. Among these issues are the importance of selecting a proper sampling strategy based on low-discrepancy random number sequences and the importance of selecting a class of metamodels able to reproduce the inputs–outputs relationship in a robust and reliable way. Sobol sequences and Gaussian process metamodels were recognized as the appropriate choices. The proposed methodology was assessed by comparing the results of the application of variance-based sensitivity analysis techniques with the simulation model and with a metamodel estimated with 512 model runs for a variety of traffic scenarios and model outputs. Results confirmed the power of the proposed methodology and also made a more extensive application of sensitivity analysis techniques available for complex traffic simulation models.

In the field of traffic simulation, the calibration of uncertain inputs against real data is usually taken to cover the epistemic uncertainty regarding the un-modeled details of the phenomena and the aleatory not predicted by the models. For this reason, model parameters are usually indirectly derived by means of an optimization framework, which tries to maximize the fit between real and simulated measures of the traffic system. This is the case, for example, of the calibration of car-following models’ parameters against vehicle trajectory data. Only recently, it has been proven that the capability of the optimization framework to provide the parameters’ values that allow the car-following model reproducing real trajectories at its best is strictly connected to the setup of the optimization framework itself. This, in particular, entails the necessity to carefully choose an appropriate combination of optimization algorithm and measure of goodness of fit (GOF). In this study, the authors focus the attention on this latter issue. Specifically, it is claimed here that the commonly used GOFs are not able to capture the dynamics of the time-series which calibration is performed against. Therefore, a spectral analysis based approach to evaluate the overall performance of the simulation model in the objective function is proposed. The new measure of goodness of fit is tested in the calibration of the Intelligent Driver Model against synthetic trajectory data. Results confirm that the resulting optimization framework is always able to find the global optimum of the optimization problem.

Prediction of service availability in railway systems requires an increasing attention by designers and operators in order to satisfy acceptable service quality levels offered to passengers. For this reason it is necessary to reach high availability standards, relying on high-dependable system components or identifying effective operational strategies addressed to mitigate failure effects. To this purpose, in this paper an innovative architecture is proposed to simulate railway operation in order to conduct different kinds of analysis. This architecture encompasses a set of components considering, in an integrated way, several system features. Finally an application to a first case study demonstrates the impact on quality of service and service availability of different recovery strategies. Complexity of a railway system requires a heterogeneous working group composed of experts in transport and in computer science areas, with the support of industry.

In this work a learning machine model is proposed in order to develop an Adaptive Cruise Control (ACC) system with human-like driving capabilities. The system is based on a neural network approach and is intended to assist the drivers in safe car-following conditions. The proposed approach allows for an extreme flexibility of the ACC that can be continuously trained by drivers in order to accommodate their actual driving preferences as these changes among drivers and over time. The model has been calibrated against accurate experimental data consisting in trajectories of vehicle platoons gathered on urban roads. Its performances have been compared with those of a conventional car-following model.

The paper presents some results of a project, led by the Società Autostrade Meridionali S.P.A. (Autostrade per l???Italia Group) and partially funded by the MIUR (Italian Ministry of Education, University and Research), aimed at the experimentation of advanced technologies and management patterns on motorway E45 (Napoli-Pompei-Salerno). The main focus of the paper is the implementation and calibration of a micro-simulation model and its use in order to assess the effects of several control strategies based on ramp-metering techniques.