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

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Item Type: Tesi di dottorato
Lingua: English
Title: Uncertainty Management in Traffic Simulation: Methodology and Applications
Creators:
CreatorsEmail
Montanino, Marcellom_montanino@msn.com
Date: 31 March 2014
Number of Pages: 293
Institution: Università degli Studi di Napoli Federico II
Department: Ingegneria Civile, Edile e Ambientale
Scuola di dottorato: Ingegneria civile
Dottorato: Ingegneria dei sistemi idraulici, di trasporto e territoriali
Ciclo di dottorato: 26
Coordinatore del Corso di dottorato:
nomeemail
Petroncelli, Elvirapelvira@unina.it
Tutor:
nomeemail
Punzo, VincenzoUNSPECIFIED
Date: 31 March 2014
Number of Pages: 293
Uncontrolled Keywords: traffic simulation, sensitivity analysis, calibration
Settori scientifico-disciplinari del MIUR: Area 08 - Ingegneria civile e Architettura > ICAR/05 - Trasporti
Area 01 - Scienze matematiche e informatiche > INF/01 - Informatica
Area 01 - Scienze matematiche e informatiche > MAT/06 - Probabilità e statistica matematica
Area 01 - Scienze matematiche e informatiche > MAT/08 - Analisi numerica
Area 01 - Scienze matematiche e informatiche > MAT/09 - Ricerca operativa
Aree tematiche (7° programma Quadro): TECNOLOGIE DELL'INFORMAZIONE E DELLA COMUNICAZIONE > Ambiente, energia e trasporti
Date Deposited: 07 Apr 2014 08:44
Last Modified: 28 Jan 2015 09:33
URI: http://www.fedoa.unina.it/id/eprint/9735

Abstract

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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