Discovery Early Career Researcher Award - Grant ID: DE160100007
Funder
Australian Research Council
Funding Amount
$303,000.00
Summary
The Future of Urban Routing and Navigation. This project aims to develop new efficient techniques for mixed-initiative routing in large transportation networks. Current state-of-the-art techniques for real-world journey planning take user requirements as input and generate a few proposed journeys as output. However, the most useful decision-support systems are mixed-initiative: the Information Technology (IT) system and user work together to find the best decisions. In the context of journey pla ....The Future of Urban Routing and Navigation. This project aims to develop new efficient techniques for mixed-initiative routing in large transportation networks. Current state-of-the-art techniques for real-world journey planning take user requirements as input and generate a few proposed journeys as output. However, the most useful decision-support systems are mixed-initiative: the Information Technology (IT) system and user work together to find the best decisions. In the context of journey planning, interaction with the user is needed to find the best combination of private, public and active transportation; understand trade-offs between cost, starting time, journey time, convenience and reliability; and react to delays and disruptions. This project aims to develop dynamic decision-support systems that will help travellers reach their destinations cheaper, faster and more conveniently.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100205
Funder
Australian Research Council
Funding Amount
$375,000.00
Summary
Optimisation of transit priority in a transportation network. This project is aimed at developing an optimised approach to combine various types of public transport priority in an urban network which can be used by transport planners to increase the efficiency of traffic movements and reduce traffic congestion. The case study is the network of Brisbane including all arterial and local roads.
Integrating network modelling with observed choice data for multi-criteria optimisation of complex car share systems: cost, mobility and transit usage. This project will develop methods to determine an efficient car share system, which includes optimal location, one-way car sharing, and how carshare influences the broader transport system. By adopting such new comprehensive methods, the overall transport system will benefit through potential improvements in public transit usage.
Discovery Early Career Researcher Award - Grant ID: DE160100103
Funder
Australian Research Council
Funding Amount
$373,506.00
Summary
Understanding the automobility decisions of Australian millennials. The aim of this project is to understand the decision-making of young Australians regarding driver licensing and car travel. After decades of growth in car use, young adults are now becoming less likely to get a licence and drive cars. This reduction in car dependence provides an opportunity to reduce road deaths and injuries, road congestion and greenhouse gas emissions. Understanding how and why young adults make decisions abo ....Understanding the automobility decisions of Australian millennials. The aim of this project is to understand the decision-making of young Australians regarding driver licensing and car travel. After decades of growth in car use, young adults are now becoming less likely to get a licence and drive cars. This reduction in car dependence provides an opportunity to reduce road deaths and injuries, road congestion and greenhouse gas emissions. Understanding how and why young adults make decisions about their current and future car mobility could support this societal transformation and enhance sustainability and well-being.Read moreRead less
Valuation of service reliability and crowding under risk and uncertainty: neglected drivers of demand for public transport. The reliability of public transport services, and the amount of crowding at stations and also on trains and on buses, have come under strong criticism. This study identifies the role that improved service reliability and reduced crowding play in influencing the switch from car to public transport for the commute.
Integrating Attribute Decision Heuristics into Travel Choice Models that accommodate Risk Attitude and Perceptual Conditioning. This proposal has the specific objective of integrating two disconnected literatures that are having a major influence on the behavioural and statistical performance of discrete choice models in travel choice modelling. These fields are attribute processing strategies and the conditioning of the marginal utility of attributes by risk attitude and perceptual conditioning ....Integrating Attribute Decision Heuristics into Travel Choice Models that accommodate Risk Attitude and Perceptual Conditioning. This proposal has the specific objective of integrating two disconnected literatures that are having a major influence on the behavioural and statistical performance of discrete choice models in travel choice modelling. These fields are attribute processing strategies and the conditioning of the marginal utility of attributes by risk attitude and perceptual conditioning. These two major developments have not been jointly integrated into a behaviourally richer representation of choice making. Given the encouraging evidence from both literatures, the research will determine more precisely the benefits in terms of improved estimates of willingness to pay for specific attributes and also increased predictive power. Read moreRead less
Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to deve ....Inferring driver behaviours, intent and risk in complex traffic scenarios. This project intends to develop methods to evaluate risk during driving. The next generation of vehicles will be fitted with sophisticated perception and egocentric information. This will be combined with inter-vehicle communication enabling cooperative safety, used in conjunction with intelligent infrastructure. This technology is expected to be mandated in the United States starting from 2017. This project plans to develop unsupervised learning algorithms to infer high-level driver behaviours, intent and contextual information to automatically evaluate levels of risk under complex driving scenarios. It plans to validate the results using naturalistic driving datasets taken in large-scale deployments around the world. This innovation may improve automotive safety and facilitate the deployment of autonomous vehicles.Read moreRead less