Comprehensive Freight Demand Data Collection Framework for Large Urban Areas - Sustainable Urban Logistics: Concepts, Methods and Information Systems - page 102

Table 6 Sampling frame of different data collection procedures

Level of detail
Excellent
Good
Some
Low
Only general

Unit/ Sampling
Frame

Shipper

Es ta blis h-
ment

Carrier

Receiver

Trip inte rc e pts

Vehicle

Tour

Note: Blank cells indicate ''No information provided.''

Table 6 shows that the possible sampling frames do not provide data about
freight traffic volumes, which are also needed, for instance, to assess the impact of
freight volume on traffic congestion. Such traffic collection data is typically per-
formed with Automatic Vehicle Classifier (AVC) or manual counting. Manual
counting involves a trained observer collecting vehicle classification counts at a
location based on direct observation of vehicles. Alternatively, this can be done
using videography, which involves collecting vehicle classification counts using
video tape recorders and tallying them manually by observing vehicles on the
video with the ability to stop time and review data, if necessary (Beagan et al.
2007 ). On the other hand, AVC is usually based on techniques such as Weight-
In-Motion (WIM), consisting of loop detectors, video cameras, or other types of
detectors to automatically classify vehicles and collect freight volume
(Sharma et al. 1998 ). The full installation of WIM, however, may be expensive and
is only deployed at limited locations. The other AVC methods include: pneumatic
tubes, loop detectors (or other types of magnetic detectors), and video cameras.
Pneumatic tubes are easily portable and need just to be placed across travel lanes
for automatic recording of vehicles. However, the classification accuracy degrades
where there is simultaneous crossing of multiple vehicles, such as on high-volume,