Higher Degree Research opportunities and training

1. Effect of cyclic loading on the stability of bedded or sediment infilled jointed rock with special reference to rail tunnels
2. Application of vertical drains to alleviate undrained instability of soft soil subgrade under transport embankment
3. Heavy Haul Railroad Design Based on Fundamental Track Dynamic
4. Internal instability of granular compacted capping layers in railways
5. Stability of pile-supported railway embankment under train loading
6. Seismic behaviour of pile-supported railway embankment
7. Use of marginal wastes for rail and road embankment
8. The use of ground improvement methods for transport infrastructure
9. Constitutive modelling of soft clays including long term deformation
10. Behaviour of granular media with applications to railways

1. Big data analytics for asset management (e.g. railway track, road, bridge) to provide insights for problem inspections. Machine learning is used to detect regions of interests for suspicious defects in an efficient and accurate manner. This allows operators to extend the range of inspection that previously overlooked due to limited resources, assist inspectors in the decision-making process, and improve the efficiency of the inspection process while maintaining high maintaining standard. 
2. Develop track/road predictive maintenance models based on big-data analysis to monitor and predict the probability and types of instability mechanisms in terms of space and time as well as long-term trend of their performance;  
3. Data analytics for incident management to capture the crucial data to enhance the initiatives and proactively manage the incidents; When a delay is captured, we will real-time predict the delay effect with delay propagation. After massive delays happen, it can trace back the primary delay, which helps the rail managers have a better understanding of the root cause of the incident.   
4. Data analytics for network performance (e.g. train performance prediction). The analytics will be built on innovative machine learning techniques and aggregate different types of data sources in an agile manner. It will be designed to predict the performance for actionable insights in different network granularities. With temporal evolvement, it can monitor the infrastructure network interfaces and improve the connections between vital infrastructure components. 
5. Integrate data analytics with traffic simulation. The outcomes will support enhanced passenger mobility, efficient freight movement and advanced infrastructure networks, meanwhile minimising transport infrastructure upgrading costs. 
6. Integrate big data analytics with conventional civil engineering discipline, enabling the transport asset managers and practical engineers to establish and optimize their predictive maintenance plan and make better decisions.

1. Shared Space Design and Implementation: Placemaking road infrastructure.
2. Dynamic adaptive stochastic traffic assignment methods for disrupted network modelling.
3. Policy development for disruptive technologies: Autonomous Vehicles and the next transport revolution
4. Hybrid of acoustic emission/guided wave-based rail track monitoring
5. Data-driven bridge condition assessment based on deep learning;
6. Substructure condition assessment approach for transport infrastructure safety.

1. Characterising Landfill Properties for Construction of Transport Infrastructure
2.  Reliability Analysis for Low Embankment Strategy Considering Soft Soil Creep
3. Three Dimensional Discrete Element Modelling of Open-Ended Tubular Pile Penetration in Weak Rocks
4. Experimental Study on the Small Strain Shear Modulus of Unsaturated Soils
5. Post and re-liquefaction Characteristics of Lightly Cemented Sands
6. Enhanced Analysis of Load Transfer Mechanism and Deformation Estimation for Ground Improvement Using Concrete Injected Columns

The relevant topics for this program deal with concerns of a historical nature, with regards to how the value of time (VoT) commonly used in Transportation studies was derived, particularly with respect to how the VoT for employee business trips came to be. 

1. The value of time: Myth or Reality
   The value of time (VoT) represents one of, if not the, most important numbers in transport economics. The VoT is derived from neo-classical micro-economic theory, which assumes travellers are willing to trade off time for money, or at least act as if they do when choosing which mode to travel by, or which route to take. This research topic seeks to investigate whether the VoT actually represents travellers true underlying behaviour or whether alternative economic theories, such as those derived from behavioural economics, might better describe the choices made by travellers.
2. How stable is the value of time?
   The value of time (VoT) has traditionally been derived from either stated preference or revealed preference studies using discrete choice models estimated under the assumption of random utility theory, and has been shown to vary by mode, by time of day, and route. Worryingly, even for the same data set, the VoT may vary depending on the specific econometric model estimated, and is often observed to be higher for models estimated on stated preference data than for revealed preference data. This project will seek to determine how stable the VoT actually is in reality by collecting and analysing data obtained from a longitudinal panel.
3. Is the value of time what everyone thinks it is?
   The theory of the Value of Time (VoT) in transportation has its origins in micro-economic theory, being originally derived from a goods/leisure trade-off perspective. Under this framework, it is assumed that individual travellers are trading off travel time for time spent undertaking leisure activities. This theory suggests that VoT is a function of both time and income. Most recent studies ignore the role of income when estimating the VoT, whilst most studies further fail to acknowledge that the VoT should be larger for longer trips given that longer trips imply less leisure time. It is important to note that the above effects may be problematic if the VoT is to be used in other wider models (such as network models) as the generalised cost functions in such models may have difficulty in allowing for changing marginal utility for time based on trip length, and will likely need to resort to using average income levels rather than allow for income distributions.
4. Electric vehicle micro-simulation model
   This research will seek to develop a micro-simulation model to forecast electric vehicle (EV) demand and usage in Australia. The aim of the project is to derive a model that will allow for an exploration of the impact of various policies to promote EV uptake in Australia. The expected outcome of this project will be a microsimulation of the Australian Households that models the system of household vehicle choices, allowing for an exploration of the impact of different policies on both EV demand and usage (e.g., a per km charge or surcharge on electricity bills).
5. Long-term effect of the pandemic on travel behaviour
   Covid-19 has tremendously changed the travel behaviour of many Australian residents, who have adapted their lifestyle in response to state policies and private businesses guidelines. Working from home has become the new normal for millions of Australians, and it has profound implications for the future. However, uncertainty is linked to the long-term effects of Covid-19 on individual travel behaviour. Considering the extended public transport infrastructure in many big Australian capital cities, the expected goal of this project is to monitor transport patterns in order to establish whether and how Australians have changed their daily preferences in terms of transportation.