Direct Effects of City Logistics Measures and Urban Freight Demand Models - Sustainable Urban Logistics: Concepts, Methods and Information Systems - page 218

Table 4 Shop type choice model: parameter estimation for clothing
Parameter Unit Alternative Value t-st value p-value

Woman 0/1 Small outlet 0.44 1.80 0.07

ASA 0/1 Small outlet 0.43 1.63 0.10

Early afternoon 0/1 Large outlet 0.17 1.91 0.07

Time spent on purchases Minutes Large outlet 0.01 2.46 0.01

Sunday 0/1 Large outlet 1.03 2.22 0.03

q 2

0.15

Table 5 shows the results for other types of goods. The results confirm the
tendency of women to buy in a shop, and of large groups to travel to large retail
outlets on workdays (i.e. from Monday to Friday) and if considerable time for
shopping is available. Furthermore, this result shows the inclination of many
people to travel to large retail outlets for shopping together or for recreation.

4.5 Study Cases

The above modeling system was implemented by the authors to study the effects of
strategies for urban freight activity location upon transport costs in the medium-
size urban area of Padua in northern Italy (Nuzzolo et al. 2013 ). The strategy of
clustering warehouses, distribution centers and large retail outlets in the first ring
can have impacts in terms of reducing both freight distribution and shopping travel
distances. Indeed, with respect to the other land-use scenario, this solution entails a
reduction in freight distribution vehicle—km and a small increment in the number
of car shopping trips which is offset by a considerable reduction in car shopping
trip vehicle—km.

Table 5 Shop type choice model: parameter estimation for other products
Parameter Unit Alternative Value t-st value p-value

Woman 0/1 Small outlet 1.18 2.66 0.00

ASA 0/1 Small outlet 1.74 4.30 0.00

Size of group Large outlet 0.24 1.41 0.15

Monday/Friday 0/1 Large outlet 1.19 1.32 0.18

Housewife 0/1 Large outlet 1.48 1.41 0.15

Time spent on purchases Minutes Large outlet 0.03 5.02 0.00

q 2

0.23