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Hi all,

I'm currently calibrating the scoring function parameters set out in Section 3.4 for use in a road pricing study, and as such am looking to implement a marginal utility of money (Bm) that is dependent on income.

I am wondering if anyone would have advice on whether this is better achieved by (1) adopting a single Bm that is then divided by an individual's income (i.e. single regression using income as a parameter), or (2) using different values for Bm for income groupings (i.e. multiple regressions for data disaggregated by income ranges).

The (dis)utility functions would look something like the following:

1)  Bm = 100 where S = c + (Bm / Income_i) . Cost + etc
for Individual daily income 'i' (100, 125, 160)

or

2)  Bm,i = 1, 0.8, 0.6 for income groups i (low, medium, high)
where S = c + Bm,i  . Cost + etc

At the moment I am leaning towards approach 1 as it ensures Bm is the same coefficient as that used in the mode choice model that was used to estimate the mode parameters (i.e. marginal utility of travelling).  

Any advice from those that may have tackled this issue before would be greatly appreciated.

Thanks & regards,
James

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