Cross-classification (category analysis): Introduction :
Cross-classification (category analysis): Introduction Trip production:
p = trip purpose
i = zone
h = household type grouping
ai(h) = number of households of type h in zone i
tp(h) = trip rate for trip of type p for households of type h
Cross-classification (category analysis): Example :
Cross-classification (category analysis): Example Situation: Zone 23 characteristics are as follows:
Home based work (HBW) trip production data are as follows:
Cross-classification (category analysis): Steps to create table :
Cross-classification (category analysis): Steps to create table Establish household groupings
Assign households to the groupings
Total, for each grouping the observed trips [Tp(h)]
p is the trip purpose
h is the grouping
Total, for each grouping the observed households [H(h)]
H is the number of households observed
h is the grouping
Calculate the trip rates by grouping [tp(h) = Tp(h)/ H(h)]
Cross-classification (category analysis) :
Cross-classification (category analysis) Advantages of cross-classification
Independent of zone system
No regression related assumptions necessary
Disadvantages
No extrapolation
No trip rate for cells with no observations
Difficult to add additional stratifying variables
Difficult to choose household groups
Matching Production and Attractions :
Matching Production and Attractions trip production models are more reliable than trip attraction
RESULT: force total trip attractions to equal total trip productions
Pi = trips produced by zone i
Ai = total trips attracted by zone i
Matching Generations and Attractions (cont.) :
Matching Generations and Attractions (cont.) The adjusting factor to adjust the attractions
Trip Attraction Adjustment Example :
Trip Attraction Adjustment Example