Probability in traffic: a challenge for modelling

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