Economic Behaviour Of Interdependent Road Freight Stakeholders Under Variable Road User Charges: Advanced Stated Choice Analysis
Sean M. Puckett
Road freight transport and distribution is an important contributor to the efficiency of an urban economy, and the economy at large. Despite this, research on road freight stakeholders is under-represented in the transport literature, due largely to the complexity in modelling the interdependencies of these stakeholders, but also because of the pre-occupation with passenger transport. This shortcoming in the literature is especially prominent in the case of research into road user charging measures intended to reduce the negative consequences of traffic congestion in urban areas. Extant methodological frameworks for quantifying the preferences and influence of interdependent stakeholders are ill-suited for applications involving road freight stakeholders, and hence little is known about the likely behaviour of road freight stakeholders under a road user charging system. Furthermore, the state of practice in the modelling of preferences, whether in independent or interdependent decision-making settings, involves an assumption of passive bounded rationality (i.e., all decision makers attend to all information presented to them in the same manner). This assumption may obscure what behavioural inference is available within the literature, due to biases that the assumption may impose within econometric analyses of choice.
This thesis addresses these shortcomings by presenting new methods and empirical findings of a two-stage stated choice study of road freight transport providers (transporters) and their customers (shippers) in the Sydney Metropolitan Area. The study utilises a new technique, called minimum information group inference, to estimate measures of willingness-to-pay and relative influence for interdependent road freight stakeholders under a hypothetical variable road user charging system. Information was captured within an advanced computer aided personal survey instrument (CAPI) to condition model estimates on the manner in which respondents processed the information presented to them (i.e., their attribute processing strategies), to test the impacts of the assumption of passive bounded rationality and to demonstrate the inferential power of removing this assumption. The results support the feasibility of extending the state of the art and practice to allow for more complex representations of agent interactivity and attribute processing behaviour within empirical applications in freight distribution in particular and more generally in choice analysis.