Area of Research
We are seeking
highly motivated and talented applicants contributing to our growing
multidisciplinary research in transportation engineering.
Eligibility
Applicants
should hold a Master's degree in Civil Engineering, Electrical
Engineering, Computer Science, Information Technology, Operations
Research, Applied Mathematics, or Physics and should be interested in
transportation systems research. Preferred background and experience
include traffic modeling, transportation network modeling, stochastic
analysis, optimization, machine learning, and big data. A summary of the
project description is provided below.
How to apply
Potential applicants are
encouraged to contact Dr. Meead Saberi, submitting their complete CV and
contact information for three references.
Deadline
The position is available
immediately. The call for applications will remain open until the
position is filled. The PhD scholarship is provided by NICTA.
Project Summary
Urbanization and recent wide-spread advancements of information and communication technologies have transformed cities into pools of big data, providing new opportunities to unravel hidden patterns in urban life. However, using big data to generate "smarter cities" relies on the methodological capacity to render the masses of data into meaningful and, most importantly, useful information. Government organisations that manage and operate transport networks have a critical need for reliable and accurate traffic estimation and prediction tools. This project aims to develop a framework to integrate multiple data sources including traffic, weather, crashes, work zones, special events, and social media for a better predictive modelling of traffic congestion at the network level. The new modelling approach takes advantage of machine learning and big data analytics considering spatial and temporal interdependencies of traffic condition in a large-scale network. Results are expected to help government agencies to better predict traffic congestion and thus, manage the transport network more effectively.
Monash Supervisors
Dr. Meead Saberi (Faculty of Engineering, Monash University)
Dr. Mohsen Ramezani (Faculty of Engineering, Monash University)
NICTA Supervisor
Dr. Goce Ristanoski
Project Summary
Urbanization and recent wide-spread advancements of information and communication technologies have transformed cities into pools of big data, providing new opportunities to unravel hidden patterns in urban life. However, using big data to generate "smarter cities" relies on the methodological capacity to render the masses of data into meaningful and, most importantly, useful information. Government organisations that manage and operate transport networks have a critical need for reliable and accurate traffic estimation and prediction tools. This project aims to develop a framework to integrate multiple data sources including traffic, weather, crashes, work zones, special events, and social media for a better predictive modelling of traffic congestion at the network level. The new modelling approach takes advantage of machine learning and big data analytics considering spatial and temporal interdependencies of traffic condition in a large-scale network. Results are expected to help government agencies to better predict traffic congestion and thus, manage the transport network more effectively.
Monash Supervisors
Dr. Meead Saberi (Faculty of Engineering, Monash University)
Dr. Mohsen Ramezani (Faculty of Engineering, Monash University)
NICTA Supervisor
Dr. Goce Ristanoski
Dr. Meead Saberi
Institute of Transport Studies
Department of Civil Engineering
Monash University
Melbourne, Australia
Email: meead.saberi@monash.edu
Phone: +61 3 9905 0236
Web: http://monash.edu/research/
Institute of Transport Studies
Department of Civil Engineering
Monash University
Melbourne, Australia
Email: meead.saberi@monash.edu
Phone: +61 3 9905 0236
Web: http://monash.edu/research/
About the university
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