Introducing the Core Probability Framework and Discrete-Element Core Probability Model for efficient stochastic macroscopic modelling

Abstract

In this contribution the Core Probability Framework (CPF) is introduced with the application of the Discrete-Element Core Probability Model (DE-CPM) as a new DNL for dynamic macroscopic modelling of stochastic traffic flow. The model is demonstrated for validation in a test case and for computational efficiency on two simple networks. The CPF extends a base model, such as the Cell Transmission Model (CTM), by considering each traffic variable as a discrete stochastic variable denoted as a probability distribution of values for each traffic variable in time and space. Traffic is propagated along a link using the base model and through a larger network with the application of probability merging algorithms at the nodes. Due to the incorporation of probability in the core of traffic propagation, the necessity for multiple acts as an internalisation of the Monte Carlo routine in the CPF for fast and efficient calculation of uncertainty. Initial tests cases show that the DE-CPM has the potential to reduce computation time multi-tenfold compared to regular Monte Carlo simulation. Such developments allow the application of stochastic dynamics traffic models to be more readily applied in practice.