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Network Econometrics and the Evolution of Transport Systems

Network Econometrics is a new set of methodologies to exploit network information when undertaking econometric analysis. It has uses in both short and long-term traffic prediction. more...

Supervisor(s): Levinson, David (Professor)

Technological Change and the Future of Cities

Many transport analysts envision a forward-looking, ambitious and disruptive cloud commuting-based transport system for future smart cities based on emerging connected, autonomous v more...

Supervisor(s): Levinson, David (Professor)

Behavioural Route Choice

Route Choice models have long been based on the assumption that travelers choose the shortest travel time path between their origin and destination, despite increasing evidence that more...

Supervisor(s): Levinson, David (Professor)

A Benefit/Cost Analysis of Benefit/Cost Analysis

Transportat projects of significant size and scope often must be subjected to fairly rigorous project evaluation processes.  Benefit-cost analysis (BCA) is a project evaluation more...

Supervisor(s): Levinson, David (Professor)

Characterizing Transport Networks

In an increasingly urbanized world, people remain connected by a complex nexus of roads, rails, paths, and sidewalks that form urban transportation systems and shape travel demand. more...

Supervisor(s): Levinson, David (Professor)

Simulating Transport for Realistic Engineering Education and Training

Understanding and predicting travel demand and travel patterns is one of the most important
but also most challenging tasks of transport
engineers, planners, and geographers. Tr more...

Supervisor(s): Levinson, David (Professor), Ramezani Ghalenoei, Mohsen (Dr)

Historical and Prospective Adoption Rates of Transport Technologies

Traditional transportation forecasts do not consider changes in technology. Yet we know technologies change, and those changes consequently affects underlying behaviours. Providing more...

Supervisor(s): Levinson, David (Professor)

Semi-parametric estimation of Autoregressive Conditional Duration (ACD) models

Method of Maximum Likelihood (ML) is used to estimate ACD model parameters. Estimating Function (EF) is a popular semi-parametric approach in time series modelling. This project dev more...

Supervisor(s): Peiris, Shelton (Associate Professor)

Non-linear cointegrating regression: Theory and Applications

This project will develop estimation and inference theory in non-linear cointegrating regression.  Empirical applications in money demand, trading data and various real time se more...

Supervisor(s): Wang, Qiying (Professor)

Dynamic prediction of longitudinal markers and time to disease progression in metastatic melanoma patients

This project aims to develop individualised dynamic event prediction models, combining simultaneously longitudinal biomarker data and time-to-event data. more...

Supervisor(s): Lo, Serigne (Dr), Long, Georgina (Professor)