- Very high reliability: 4/5 times on time, 1/5 times 2 hours late
- High reliability: 3/5 times on time, 2/5 times 3 hours late
- Medium reliability: 2/5 times on time, 3/5 times 3 hours late
- Low reliability: 2/5 times on time, 3/5 times 4 hours late
- Very Low reliability: 1/5 times on time, 4/5 times 5 hours late
Code: Select all
design
? mode choice of shippers with HOMOGENOUS DESIGN CODING
;alts=truck, train, etrain
;rows=24
;block=3
;eff=(mnl,d)
;con
;model: ?model using DESIGN CODING
U(truck)= b1.dummy[0|0|0|0] * COST1[0,1,2,3,4]
+ b2.dummy[0|0|0|0] * TIME1[0,1,2,3,4]
+ b3.dummy[0|0] * TTR1[0,1,2,3,4]
+ b4.dummy[0] * risk[1,0]
/
U(train)= con_train
+ b5.dummy[0|0] * COST2[0,1,2]
+ b6.dummy[0|0|0] * TIME2[0,1,2,3]
+ b3.dummy * TTR1
+ b7.dummy[0|0] * frequency[0,1,2]
/
U(etrain)= con_etrain
+ b5.dummy * COST2
+ b8.dummy[0|0] * TIME3[0,1,2]
+ b9.dummy[0] * TTR2[0,1]
+ b7.dummy * frequency
$Since both high and medium reliability have the same standard deviations (although they differ in number of on time arrivals), can I breakdown the reliability attribute into two separate attributes like:
- Option 1: Probability of on-time arrivals (in fractions or percents) and magnitude of delay (in hours)
- Option 2: Standard deviation of travel time and a categorical variable to differentiate between first two levels of reliability (Very high, High) and last three levels of reliability (Medium, Low, Very Low) for alternatives truck and train since etrain only has first two levels of reliability