A data-driven approach for network-wide modelling and control of urban network
This research aims to model a large-scale urban network, consisted of thousands of short links and traffic signals, using a limited set of data collected from a combination of fixed and mobile traffic sensors. The required data for this study will be provided by the AMA Eco-drive trajectory data and Edmonton citywide Dynameq model. A general framework for describing a large traffic network will be developed using machine learning/artificial intelligence tools. Based on the developed traffic model, a congestion pricing scheme to optimize tolling prices and zones will be developed. Finally, a scenario analysis of the different tolling and congestion levels will be conducted. This study enhances computational efficiency and realism aspects of traffic modelling by describing large traffic networks using a limited set of links and vehicle trajectories.
Voir la description complète du projetLina Kattan
Alberta Motor Association
Engineering
Administrative and support, waste management and remediation services; Finance and Insurance; Other services (except public administration)
University of Calgary
Elevate
