Probability in traffic: a challenge for modelling
conference paper
In the past decade an increase in research regarding stochasticity and probability in traffic modelling
has occurred. The realisation has grown that simple presumptions and basic stochastic elements are
insufficient to give accurate modelling results in many cases. This paper puts forward a strong
argument for the further development and application of probabilistic models and argues that a
realisation must arise of the detrimental effects of blindly applying non-probabilistic models to traffic
where probability is rife. This is performed by the demonstration that deterministic and simple
stochastic models will, in many cases, produce substantially biased results where variability is
present in traffic. Prior to this demonstration, recent developments in probabilistic modelling are
discussed.
While the case for probabilistic modelling is strong in theory, the application of such modelling
approaches is only possible with sufficiently developed models. However there are still certain
challenges to be addressed in probabilistic modelling before a widespread implementation is likely.
Remaining challenges for probabilistic approaches are therefore discussed and it is shown that
computational efficiency, correlations between variables, and data gathering and processing all
remain difficulties that have yet to be fully overcome.
has occurred. The realisation has grown that simple presumptions and basic stochastic elements are
insufficient to give accurate modelling results in many cases. This paper puts forward a strong
argument for the further development and application of probabilistic models and argues that a
realisation must arise of the detrimental effects of blindly applying non-probabilistic models to traffic
where probability is rife. This is performed by the demonstration that deterministic and simple
stochastic models will, in many cases, produce substantially biased results where variability is
present in traffic. Prior to this demonstration, recent developments in probabilistic modelling are
discussed.
While the case for probabilistic modelling is strong in theory, the application of such modelling
approaches is only possible with sufficiently developed models. However there are still certain
challenges to be addressed in probabilistic modelling before a widespread implementation is likely.
Remaining challenges for probabilistic approaches are therefore discussed and it is shown that
computational efficiency, correlations between variables, and data gathering and processing all
remain difficulties that have yet to be fully overcome.
TNO Identifier
489682
Source title
4th International Symposium on Dynamic Traffic Assignment, Massachusetts, USA, June 2012
Pages
1-15
Files
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