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OCR for page 22
22 Truck Drayage Productivity Guide
The third step in the process was to take test data from the Qualcomm system. This was
successful as a proof of concept, but the polling frequency was too long to produce reliable
results.
The next step was to modify the Qualcomm system to poll the location of the truck fleet every
5 minutes for 4 weeks during January 2010. These individual reports were converted to Excel files,
combined, and then analyzed to produce the resulting performance measures. As part of NCFRP
Project 14, the Tioga Group analyzed the information for nine trucks from January 1 to January
26 as a pilot. The raw data produced by Qualcomm is simply a list in Excel of every observation.
Qualcomm investigated, but did not have a regular turn time report in their standard package. As
a result, these lists were reviewed manually to identify the time a truck entered one of the geofenced
areas and the time it left. Once the month was complete, the full data set had 1,888 usable marine
terminal cycles. These were used to produce various turn time frequency distributions, as well as
to provide standard statistics such as mean and mode.
The biggest issue with the data was false positives and false negatives. These occur repeatedly
because the terminals, regularly used roadways, and the motor carrier's domicile are in very close
proximity. During the manual analysis of the raw data, single, isolated positives or negatives were
ignored. Therefore, if a truck was in one marine terminal for 20 minutes, listed in the adjacent ter-
minal or outside the terminal for one reading, and then back in the terminal for the next reading,
the truck was assumed to be located in the marine terminal for the entire time. Also if a truck was
on a dispatch that went past a marine terminal and showed in the terminal for one reading, that
observation was also ignored.
Remote chassis and container facilities were separately geofenced. Where it was obvious that a
truck was dispatched to pick up a chassis and then immediately pick up a load at the main terminal,
these cycles were combined to produce a cycle time that reflected the full service provided. The same
is true for the case in which a chassis yard cycle immediately followed a main marine terminal cycle.
Local and Regional Traffic Data
DOTs collect a limited amount of information that may be useful in analyzing dray operations.
The records collected by DOTs are used for broad planning purposes and, for this reason, usually
are not designed to discern the subtle distinctions that characterize dray operations. Rather, DOT
records should be seen as providing context as to the overall level of congestion, from all vehicle
types, on corridors that could be impacted by drayage.
Typically, each DOT has a network of permanent sites that are regularly used to collect data in
an on-going fashion. The data collected are usually annual average daily traffic (AADT) in which
trucks are not discerned from passenger vehicles. On a sample of highways, classification counts
are made to develop factors that can translate average vehicle counts into autos and trucks. This
can be viewed as a calibration exercise to obtain estimates of truck flows. Sites may be located on
key ramps or highway segments that serve marine terminals (e.g., California) but, in general, state
DOTs leave data collection responsibilities to metropolitan planning organizations (MPOs).
For roadways in close proximity to a port terminal, there are cases where almost all traffic is made
up of dray vehicles and, in this sense, the total traffic count would approximate the dray impact.
For the majority of roadways, however, dray traffic will constitute a small share of total traffic.
Therefore, the magnitude of the impact of drayage can be assessed by comparing truck generation
from the terminals with total traffic counts on connecting corridors and removing the share of dray
trips that terminate in close proximity to the terminal itself and therefore do not significantly inter-
fere with passenger vehicle movements. Dray truck volumes on networks are best derived from
