PhD scholarship on Data-Driven Smart Cities: Application of Machine Learning and Big Data Analytics in Urban Network Traffic Estimation and Prediction
We are seeking highly motivated and talented applicants contributing to our growing multidisciplinary research in transportation engineering. 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. Potential applicants are encouraged to contact Dr. Meead Saberi, submitting their complete CV and contact information for three references. The position is available immediately. The call for applications will remain open until the position is filled. The PhD scholarship is provided by NICTA.
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.
Dr. Meead Saberi (Faculty of Engineering, Monash University)
Dr. Mohsen Ramezani (Faculty of Engineering, Monash University)
Dr. Goce Ristanoski