Environmental Engineering Reference
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high-occupancy road segments. Loop detectors involve embedding one or more
loops of wire in the pavement, which are very useful under all weather conditions,
and are mainly used as permanent recorders at locations where counts are required
for a longer time duration (Beagan et al.
2007
).
4.1 Role of Global Positioning Systems (GPS) on Data
Collection
In recent times, there has been a great deal of interest in the use of Global
Positioning Systems (GPS) for freight demand modeling. Among other benefits,
these data are: very accurate, increasingly common as the number of companies
using GPS devices is increasing, and free as they are the byproduct of vehicle
tracking and navigation systems. However, a fundamental limitation that has not
been overcome is that GPS cannot collect key data that traditional surveys provide
(e.g., commodity type, shipment size, trip purpose). For these reasons, GPS has
had a limited role in freight demand modeling. There are a number of issues. First,
there is no guarantee that the data are representative of the region under study, as
in most cases, the data are biased toward medium and large firms. As a result, the
data lack observations for the small companies that transport the bulk of the freight
in urban areas. Second, although delivery tours can be estimated from the data,
shipment sizes and the purpose of the stop are unknown. These are important
implications that severely hamper the use of GPS for freight demand modeling.
As a result, the maximum utility of GPS is realized when it is combined with
other data collection methods. For example, origin, destination and routing
information received from GPS receivers can be used to validate and improve the
information provided by truck drivers in manually completed travel diaries. Also,
combining GPS truck trip information with Geographic Information System land
use data can yield useful information on truck activity characteristics at trip ends
(Beagan et al.
2007
).
5 Identification of Data Collection Framework
This section discusses key components of a freight data collection framework,
which are summarized in Table
7
. The table shows the objectives for each data
category, defines target population and data to be collected, the data collection
approach suggested, as well as the output that would be produced with the data.
The framework makes a distinction, in terms of the freight production and con-
sumption patterns, between freight or non-freight related industry segments.
Freight-related industries include: agriculture, forestry, and fisheries; mineral
industries; construction industries; manufacturing; transportation, communication,
and utilities; wholesale trade; retail trade; and, food. In essence, these industries are
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