This project proposes the development of evidence-based parameter estimation methods to improve Main Roads Western Australia’s roundabout modelling practice and operational guidelines by accounting for various local conditions such as geometry, topography, location type (residential, industrial, rural etc.), traffic mix, and seasonality, as well as driving behaviour. The data will be used to develop dedicated roundabout models for Aimsun at micro-, meso- and macroscopic levels.
Models play a vital role in supporting decision-making at both strategic and operational levels in the transport industry. In this project, we focus on roundabouts, where significant delays on arterial roads occur. Designers rely on traffic models to test design performance, so the quality of model predications directly affects the quality of roundabout design. Data is the foundation of modelling but conventional manual traffic surveys are deficient in both quality and quantity.
Although a wide range of sophisticated software tools for traffic modelling have been developed over the years, the lack of abundant high-quality data hinders model calibration, validation, and continuous development to account for changing driving behaviour and local conditions.
This project addresses both quality and quantity problems in traffic data by applying the latest drone video analytics technology developed by University of Western Australia (UWA) researchers to inform and improve roundabout modelling.
Find more information on iMOVE website: Link