Abstract
ABSTRACT In this paper, continuous timed Petri nets (CTPN) are used to develop a hybrid traffic model, where the network is modelled as a macroscopic model and calibrated by microscopic models. The concept of CTPN is used to build a modular model, where first the highway traffic system is decomposed into several systems, based on structural entities (highway segment, on- and off-ramp links), which are coalesced into a complete model. The result is a light, versatile and easily scalable stochastic model for traffic flow. The calibration and validation of the traffic model is performed through the comparison of basic traffic parameters (flow rate, density, and mean speed) obtained through the traffic model implemented and the commercial micro-modelling software, Aimsun, for part of Portugal’s highway network. The results show that the proposed methodology results in a good trade-off between accuracy, simplicity, and computational cost.
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