Highlights The necessity for improvements in Monte Carlo simulation in traffic models is argued. Advanced sampling techniques can reduce computation effort in traffic modelling. The effectiveness of their application in practice is demonstrated in three test cases. Random sequencing is shown to be most effective in the considered cases.

    Abstract In probabilistic traffic models, consideration of stochasticity in the dynamics of traffic gives a closer representation of a traffic system in comparison to that of a deterministic approach. Monte Carlo simulation is a broadly accepted method to consider variations in traffic within modelling. In this contribution, the possibility of increasing the efficiency of probabilistic traffic flow models using Monte Carlo simulation is analysed using variance reduction techniques and sequencing, for varied capacity and traffic demand values. The techniques of Importance Sampling, Latin Hypercube Sampling and Quasi-Random Sequencing are compared in a dynamic macroscopic traffic model to demonstrate the effectiveness of these techniques for reduction of the computational load when considering multiple input variations. Demonstration of their efficiency in traffic modelling is expected to lead to a wider application of the methods in practice.


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    Title :

    Application of advanced sampling for efficient probabilistic traffic modelling


    Contributors:


    Publication date :

    2014-10-29


    Size :

    16 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English





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