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
Discovery Early Career Researcher Award - Grant ID: DE200101793
Funder
Australian Research Council
Funding Amount
$423,038.00
Summary
Quantifying and managing the network impacts of transport sharing services. This project aims to address the challenge of effectively modelling multiple transport sharing services (e.g., ridesharing and parking sharing) in a multimodal network, and efficiently operating these services, and incentivising people to use them. The project expects to generate new knowledge in shared transport by developing an innovative approach to systematically reproducing and optimising network impacts of sharing ....Quantifying and managing the network impacts of transport sharing services. This project aims to address the challenge of effectively modelling multiple transport sharing services (e.g., ridesharing and parking sharing) in a multimodal network, and efficiently operating these services, and incentivising people to use them. The project expects to generate new knowledge in shared transport by developing an innovative approach to systematically reproducing and optimising network impacts of sharing services on travel choices, sharing demand-supply matching patterns, movement trajectory features and traffic dynamics. Expected outcomes include new models and strategies to improve decision support for transport planners and operators. This should provide significant benefits for human mobility and city sustainability.Read moreRead less