Montanino, Marcello (2014) Uncertainty Management in Traffic Simulation: Methodology and Applications. [Tesi di dottorato]

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In the last years, the expanding range of new technologies in the field of traffic management and control called for accurate modeling of traffic flows in order to evaluate their potential impact on society and environmental decision-making. The inner complexity of these applications sought for detailed stochastic traffic simulation tools which could enable their analysis, design and evaluation. In this view, microscopic traffic flow simulation models are increasingly used as cost-effective tools to support these tasks. However, despite their importance, the use of these tools is far from being trivial. Indeed, the “goodness” of a simulation study does not depend only on the expertise of the analyst/modeler but (mostly) on the “correct” use of such models which, conversely, can be challenging even for specialists. This could be due to a number of reasons, including model indeterminacy, over-parameterization, asymmetry in the importance of parametric inputs, and so on. In other words, different sources of parametric and non-parametric uncertainty may affect the performances of simulation models, and thus if not properly assessed (and possibly reduced), would inevitably undermine the reliability of results. Despite the importance of uncertainty management in scientific modeling, it is a very under investigated issue in the field of traffic flow simulation modeling. Common symptoms of neglecting the management of uncertainty in traffic flow simulation modeling may be the (un)repeatability of experiments, the (un)reliability of predictions, and the vulnerability to instrumental or otherwise unethical use of models. Above all, this turns out in the lack of effectiveness, credibility, and transparency of simulation results. Therefore, the objective of this dissertation thesis is to propose and apply a common methodological framework for the quantitative management of uncertainty in microscopic traffic flow simulation modeling. In particular, the research focused on driver behavioral models only, although generalization to other traffic flow models, or even to more general transportation systems models (e.g. public transportation models, pedestrian simulation models), might be possible with reasonable easiness. Several contributions were put forward with regards to the critical phases of i) vehicle trajectory data analysis, ii) driver behavioral model calibration, iii) model sensitivity analysis, and iv) aggregate traffic micro-simulation. Achieved results have the ambition of enabling the exploitation of the full potential of microscopic traffic flow simulation models in traffic forecasting.

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