First off and in advance, thank you for providing this resourceful platform for researchers.

I am making an SP experiment using Ngene to study the Value of Time perception of truck drivers. I have decided to go with the ML design and have already conducted my pilot using a MNL design, thus I have the prior parameter information for my D-efficient design. I am in process of finalizing and distributing the design but I wanted to have your thoughts on its specification prior to its survey distribution. So feel free to give me your comments/advises whatsoever.

I attach my current syntax and will explain a few details about it and finally finish this post with a few questions that I could not find any legitimate answer by going over literature.

- Code: Select all
`Design`

;alts = Route A, Route B

;alg = mfederov

;rows = 12

;eff = (rppanel, d)

;rep = 1000

;rdraws = Halton (500)

;reject:

Route A.TT + Route A.TTV >= Route B.TT + Route B.TTV, #The study area shows that the toll route's travel time and its potential delay is always lower than the free route's

Route A.Dist > 0 and Route B.Dist > 0 #Since the situation where both routes have "extra" distance does not make sense, I always have to keep one of them zero

;model:

U(Route A) = b1[n,-0.04051,0.00837] * TT[60,80,100,120] #Travel Time

+ b2[n,-0.03067,0.00570] * TC[50,100,150] #Toll Cost

+ b3[n,-0.02740,0.00823] * TTV[0,10,20,30] #Potential_Delay

+ b4[n,-0.02315,0.01293] * Dist[0,15,30] / #Extra_Distance

U(Route B) = b1 * TT + b3 * TTV + b4 * Dist $

Questions:

1. Do you know any good source on RP Distributional Assumptions? Can we postpone this specification to the model estimation stage or should be the same for both the DCE and the Estimating Model?

1.1. All of my parameters are intuitively negative, Should I use lognormal distribution instead of normal? and if I do, should I reverse the signage of attributes (i.e. TT: -60, -80 etc.) or substitute the + sign in utility function with a - sign?

2. Considering the fact that giving prior information to all of my parameters would result in more realistic choice situations, how should I decide to have them all random or keep some of them fixed and the others random as it decreases model complexity?

3. When I start to introduce a bayesian approach to this current model, I receive an Undefined D-error, what is the source of this problem?

4. How should I decide the Standard Deviation of the Bayesian version as I only have the mean values?

5. Due to the nature of my constraints, I cannot use if statements as it only works with the other algorithm (RSC). Can you think of any equivalent version with the other algorithm? Does it really make any difference to use the other algorithm?

6. Do you know any good source for choosing the sampling method? Which one do you suggest?

7. I started to play around with the candidate set size and figured out that every time it only gives one valid design and there is no meaningful relationship between the candidate set size and my constrained size (1710). What should I set as my candidate set size?

Your inputs are much appreciated.

Cheers,

Yashar