V1 = INTERCEPT1 + B_COST * log(cost1) + B_DURATION1 * log(duration1)
V2 = INTERCEPT2 + B_COST * log(cost2) + B_DURATION2 * log(duration2)
V3 = INTERCEPT3 + B_COST * log(cost3) + B_DURATION3 * log(duration3)
Obviously, a cost increase or a longer transit time is expected to reduce the probability to choose the impacted mode. Hence, B_COST and B_DURATIONx should be negative.
The strange thing is that if B_COST is defined as:
B_COST = Beta('b_cost', 0, None, None, 0)
the estimator has the expected negative sign, but if I define it with a constraint on the upper value:
B_COST = Beta('b_cost', 0, None, 0, 0)
it sometimes happens that I obtain an "active bound" in the solution, with b_cost = 0
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