Comprehensive Freight Demand Data Collection Framework for Large Urban Areas - Sustainable Urban Logistics: Concepts, Methods and Information Systems - page 98

weakening the behavioral considerations captured by the resulting demand model.
This happens because most synthesis techniques require the use of simplifying
behavioral assumptions. At the other end, a massive amount of data collection
would cover all modeling needs and necessitates a minimal number of assumptions
and almost no freight demand synthesis. This leads to a situation in which, the
more resources spent in data collection, the lower the data error and the better the
foundation for the modeling effort. However, data collection costs could become
astronomical.
In between, there is a myriad of possibilities that represent different combi-
nations of data error versus data collection costs, or freight demand synthesis
versus actual data. Most freight demand models are found in between the end
positions discussed above. The best approach is the one that best satisfies the needs
and constraints of the user. For that reason, the planning organizations' staff should
ponder what is the balance of data error and data cost that is most appropriate for
them.

The following section briefly discusses the different data collection

procedures.

4 Review of Data Collection Procedures

In order to supplement the data sources currently available, it is necessary to
collect data. This section describes the key findings from a comprehensive review
of freight data collection approaches. As previously discussed, there are some
issues involved in freight transportation that affect the efforts of conducting freight
transportation surveys and the different means of collecting data. These issues are:
(1) multiplicity of metrics to define/measure freight; (2) multiplicity of factors
determine freight/freight trip generation, distribution and the other factors that
determine demand; (3) multiplicity of economic agents involved; and (4) agents
that only have a partial view of the freight system. All these aspects complicate
tremendously the data collection process. Consequently, it seems clear that a
comprehensive approach to collect freight data is the best, and to fully describe
what happens in the system a combination of methods may be required.
In general, the different data collection techniques or surveys could be grouped
depending on how the sampling frame is defined (i.e., on the basis of the estab-
lishments at the origin or the destination of the shipment, the truck traffic, cargo
tour). This translates into collection procedures that focus on the origin or desti-
nation of the cargo, en-route as in a truck intercept survey, or along the supply
chain following the shipment as it flows from shippers to receivers. Table 5
summarizes the different data collection methodologies depending on their sam-
pling frame. For each case, the table discusses its application and the type and
collection method generally used together with their strengths and limitations. An
indication of the level of detail provided by each unit or sampling frame is shown
in Table 6 . As discussed before, no single sampling frame can provide a complete
representation of all the data categories required for freight demand modeling.