Comprehensive Freight Demand Data Collection Framework for Large Urban Areas - Sustainable Urban Logistics: Concepts, Methods and Information Systems - page 104

Table 7 Data collection framework

Data Objective Target population Data to be collected Data collection approach Output

Freight generation
data

Support the
development of
models to
express freight
production and
consumption as a
function of
economic
characteristics

Primary: Businesses in
freight related sectors.
Secondary: Businesses
in non-freight related
sectors that may need
o produce freight in a
sporadic fashion

Comapany attributes;
frequency of
deliveries; amount of
cargo received;
commodities most
frequently received/
shipped; time of
deliveries, among
others a

Computer aided telephone
interviews (CATI)

A dataset with estimates
of number of
deliveries, amount of
cargo (tons), by
commodity type, and
company attributes

Delivery tour data Development of

econometric
models of
describe the
geographic
patterns of
commodity
flows, vehicle-
trips, sequences
of stops ana

Private and common
carriers in the study
area.

Company characteristics;
tonnage; commodity
types; vehicl-trips;
tours and delivery
sequence; amounts
delivered and picked
up; and time of travel a

Travel diaries
complemented with
Global Positioning
System (GPS) data
loggers

Dataset containing an
expanded sample of
tonnage transported,
tours, vehicle trips,
that could be used to
produce origin-
destination matrices

Cordon survey Obtain travel

patterns of
internal-external,
external-internal,
and external-
external trips

Freight traffic entering the
study area within the
sampling period

The same characteristics
of the internal survey
for the external trips

Roadside interviews or
postcard surveys to be
mailed back or
answered through the
internet handed
out at toll booths

Dataset containing a
sample of tonnage
transported, tours, and
vehicle trips, used to
produce origin-
destination matrices
(continued)