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Collect and Validate New Data 55
turned (departed the intersection). For example, a truck approaching northbound toward an
intersection would be indicated as NB in the Approaching On column, and if it turns eastbound
at the intersection would be indicated as EB in the Departing On column. For roads and inter-
sections that do not correspond directly to north, south, east, and west, the data collector may
have to adopt these directions as frames of reference, and be sure to note which direction corre-
sponds to which roadway segment, etc.
Since each count sheet provides for collection of data from only 25 trucks, it is likely that many
data sheets will be needed for a single count period on a busy roadway or intersection. This type
of data collection effort, data processing (tabulation), and analysis can be intensive. For addi-
tional information about collecting, processing, and analysis of intersection data, see the sources
provided earlier in this chapter in the example titled "For More Information about Sampling,"
or seek assistance from a transportation engineering or planning professional. These sources may
also provide examples of alternate directional or intersection traffic survey data collection sheet
configurations. Analysis of these data will be similar to data analyzed in Appendix K.4 through
K.9, depending on the type of data that were collected.
5.3.5 Shipping Manifest Surveys
Shipping manifest surveys can fill an important information gap for hazmat traffic flows since
they can be used to identify hazmat shipments in both placarded and unplacarded vehicles, ship-
ment sizes and packing methods, specific materials, and shipment origin and destination (which
can yield information about how the vehicle will travel through a jurisdiction). Unfortunately,
shipping manifest surveys also can be the most labor-intensive manual hazmat traffic survey to
conduct.
In this method, access to trucking shipping manifests is obtained by working with license and
weight bureaus of authorized local and state police services, or similar vehicle inspection author-
ities. Shipping manifests are reviewed as part of the inspection process, and truck drivers may be
interviewed regarding their most likely route. Shipping paper information of interest from the
2008 ERG (5) is shown in Appendix B, but it should be noted that information formatting and
location on shipping papers is widely variable.
DOE has conducted shipping manifest studies for 24-hour continuous counts at license and
weigh stations in cooperation with state enforcement agencies. Information collected includes
the following:
· Time of day,
· Shipment origin/destination,
· Truck type,
· Placard class/division and UN/NA ID,
· Material description, and
· Shipment weight.
Additional information from driver interviews also may be recorded. Depending on the infor-
mation desired, a table or chart can be used for multiple truck manifests, or a single page or a
notebook may be used for each truck or manifest record.
5.4 Validate New Data
The new data are validated as they are collected and compiled by the project team. Validation
helps ensure that the collected new data meet the data requirements of the HMCFS objectives.
This can be done in advance of the actual data analysis. For example, users might ask themselves,
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56 Guidebook for Conducting Local Hazardous Materials Commodity Flow Studies
does precision of collected data match data requirements? What other information might help
meet the HMCFS objective data requirements? Addressing the following additional concerns
helps the project team underscore the validity of the HMCFS data:
· Are data appropriately documented?
· Are there data outliers or questionable values?
· Are data collected at similar locations consistent?
· Is information consistent across different sources (existing and new data from interviews,
databases, surveys, etc.)?
Hopefully, an HMCFS project has many different participants. However, a commonly con-
tributing factor to data validity problems is the fact that the data are collected by people. This is
an inherent source of error in every project using human data collectors, and it is impossible to
avoid. Data validity concerns identified by the project team early in the data collection phase can
be addressed much more easily than at the end of data collection. The project team may wish to
review the data collection procedures with volunteers, make sure that new data collection loca-
tions enable accurate and efficient data collection, and review the data collection sampling and
precision frameworks versus the data requirements. Remember that at least some variation in traf-
fic should be expected and may be substantial for certain locations. Further validation of the data
will take place as data are analyzed. Analysis of HMCFS data is described in Chapter 6.
