Smart Pedestrian Networks: Modelling, Monitoring and Control

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This project aims to advance the theory of pedestrian traffic flow and develop new mathematical and computational methodologies to model large-scale pedestrian networks, taking advantage of advanced sensing technologies and geotagged social media data to monitor the pedestrian activities in a city. The project develops new techniques to model pedestrian movements in an urban network while accounting for sidewalk crowding, crossing delay, anisotropic pedestrian motion and other realistic properties of pedestrian networks. The outcomes of the project will enable more efficient management and planning of transport infrastructure in cities.

We are seeking highly motivated applicants contributing to our growing multidisciplinary research in traffic and transportation engineering and technology at the Research Centre for Integrated Transport Innovation (rCITI), School of Civil and Environmental Engineering in collaboration with the School of Computer Science and Engineering at UNSW Sydney. Applicants should hold a bachelor or master's degree in Civil Engineering, Electrical Engineering, Computer Science, Operations Research, Applied Mathematics, or Physics and should be interested in transportation systems research. Preferred background and experience include traffic modelling, transportation network modelling, stochastic analysis, optimisation, machine learning and MATLAB and Python programming. The project requires extensive computer programming and applied mathematics.

Supervisory team
Meead
Saberi

Engineering
Research Centre for Integrated Transport Innovation (rCITI)
S. Travis
Waller

Engineering
Research Centre for Integrated Transport Innovation (rCITI)
Wei
Liu

Engineering
Research Centre for Integrated Transport Innovation (rCITI)
meead.saberi@unsw.edu.au