In this section we present the final error component logit model, arrived at after extensive investigation of alternative specifications.9

An important point to be aware of when evaluating the findings is that the catchment area was defined to ensure that the metro was able to be evaluated as a physically available alternative, with a time-cost profile that was deemed sensible in satisfying in-scope eligibility. Hence the sample has selectivity bias in that the choices are conditional on metro being deemed a considered alternative in the choice set. In contrast, all other means of transport (apart from the mode actually chosen in the recently sampled trip for an in-scope interview), may or may not be in the choice set, depending of the responses of respondents. Consequently it will come as no surprise that the current modal share is not a reproduction of the wider modal share of the Sydney metropolitan area or of those who live and travel from a location within the catchment area.

The pre-defined catchment was assumed to be the locality where eligible trips would commence; however many trips in the catchment area would not have a destination that would allow the Metro to be a candidate alternative; hence the use of population data such as that provided by the NSW Transport Data Centre for exogenous weighting turned out to be of little value since the data assumes that everyone who is a ‘Residents of occupied private dwellings in the CATCHMENT’ (defined by a list of postcodes in Fig. 4) was a candidate potential users of Metro.10 Given that it is unlikely that one would be able to extract the relevant sub-sample from the HTS survey (given the physical catchment area), we have chosen to focus on the unweighted models, which provide parameter estimates that are a true reflection of the preferences of the sampled respondents for whom the Metro is a genuinely relevant alternative.

A descriptive profile of the data used in model estimation is given in Table 10.

The final model is presented in Table 11.

This was selected from a large number of models. The overall goodness-of-fit on accepted criteria is impressive (rho2 of 0.6075). Some parameter estimates are alternative-specific (e.g., car costs and times), and some are generic to a subset of modes (e.g., train and metro or all public transport modes). There are no fully generic parameter estimates (i.e., across all modes), suggesting that there are mode-specific sensitivities on many influencing attributes. For example, the mean parameter estimates (or marginal disutilities) for public transport fares are −0.4345 for bus, and −0.3994 for train and metro, and all are statistically significant (lowest t-value is −5.21).

The study has focussed on obtaining new empirical evidence on the factors influencing the choice of mode of transport for commuting trips in Sydney, and especially the contribution to the relatively small literature on valuation of crowding.

The primary focus is on establishing the role of a number of trip attributes in the definition of the modal preference expression for the proposed metro in the presence of existing modes of transport for access, main and egress stages of door-to-door travel activity.

The evidence on the relative marginal disutility (or utility) associated with the influencing attributes, essentially components of service level (time, frequency, transfers), costs and crowding as well as the metro-specific constant that captures served influences associated with the metro option, enable us to derive estimates of willingness to pay for each component.

The application modelling system can import the various Willingness to Pay estimates and use them to build up the empirical expressions of relative utility associated with the competing modes of transport. This can be achieved in a generalised time or cost specification.

A number of comments are appropriate as a way of reinforcing some important assumptions that underlie (or condition) the behavioural research herein:

1. The estimated model has been developed in a context where the metro is deemed to be a ‘feasible’ alternative. Essentially, the catchment origin-destination set has been premised on the metro being included in each respondent’s choice set as if they were asked, in contrast to imposing it on them.

2. The application system within which the model can be applied must recognise that the parameter estimates and associated Willingness to Pay (WTP) estimates are applicable to a specific context in Sydney, and as such, their incorporation in a Sydney-wide modelling system must recognise this, and be prepared to assume that the parameters are transferable to the wider context.

3. On the reasonable assumption that the key outputs are portable to the application model system, the only constant that can be ported is the metro-specific constant. The numerical estimates for all segments are intuitively plausible relative to the car, train and bus and so we can be somewhat confident that we have identified the relative magnitude of the mean unobserved influences associated with the metro.

4. All modelling was undertaken as if the entire metro network will be in place. Consequently any applications that focus on a staged introduction must be mindful of the context in which all parameter estimates are derived.