Cover Image

Not for Sale

View/Hide Left Panel
Click for next page ( 56

The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement

Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 55
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,

OCR for page 55
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.