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3.0 Secondary Data Sources
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Secondary Data Sources
The impetus for compiling an inventory of secondary data sources is to classify those sources which are
available and potentially useful to transportation planners, both at the MPO and state levels. In this
way, the compendium supports the underlying objective of Task 4 which is to provide the data
identified through the strategic needs assessment (Task 1) which, in turn, will support the travel models
used in the planning process.
The secondary data sources identified can be found in Appendix 2.0 and include those available from
Federal agencies, such as the Bureau of Transportation Statistics (BTS), state agencies, and private
institutions currently involved in data collection and dissemination. The secondary sources have been
divided between those providing freight data and those providing passenger related data In order to
ensure complete coverage of sources and ease-of-use of the Guidance Manual, Passenger and Freight
have been duplicated to be included in both sections. The breakdown of sources within the Freight and
Passenger sections follows the same format as the data organization framework presented in both the
Guidance Manual and the Final Report (e.g., supply, demand, performance, system impacts, etc.)
which can be found in Appendix 1.0. In addition, all sources have been referenced in the framework to
allow users to move quickly Dom identifying the type of data needed to where that data can be found.
The motivation for isolating freight data for this compendium was three-fold. First, preliminary
research and discussions indicate that the quality, quantity, and availability of data on goods movement
has been severely lacking in the past. Second, there are a number of places where freight data is
required in order to effectively comply with ISTEA and the 1990 CAAA For example, there are a
host of planning factors which necessitate a need for specific freight data, both at the TO and state
level. Also, information on freight emissions will need to be included in conformity analysis in order to
accurately gauge emissions contributions by source. Finally, there is a demand for freight data in some
of the management systems instituted by ISTEA E.g. the IMS and the CMS).
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A majority of the secondary sources were identified through the BTS's Directory of Transportation
Data Sources'. For consistency, the format for describing each source was pandered after the
Director and includes where possible:
Title - note that a 0)) following the title represents a data base' 0)) delineates a publication, and
~ illustrates a model.
Mode
Abstract
Source of Data
Attributes
Significant Features/L~mitations
Sponsoring Organization
Performing Organization
Availability
Contact for Additionad Infonnation
Appendix 3.0 contains a recent Product Catalog put together by the Bureau of Transportation
Statistics. The Catalog provides a list and a brief description of the products and services available
from the BTS, currently or in the near future. The announcement lists both electronic (e.g., CD-ROM)
and printed products. A large majority of products are applicable to the data needs of Me
transportation planner.
Also included in this section are examples of applications of secondary data that were not originally
collected for transportation purposes (e.g., tax data). As the cost and need for data collection
increases, as wed as the budget constraints at the state and local level, there has become a heightening
demand for identifying data that has already been collected by another agency or private enterprise that
can be adapted to transportation planning needs. This appears to be an area of increased interest and
need for figure research.
iDirectolY of Transportation Data Sources. Bureau of Tran~of~tion Statistics, Department of
Transportation, DOT-VNTSC-BTS-95-1, 1995.
~7
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3.! Applications of Secondary Sources
k~d-use data - One of the secondary data applications currently being used is the use of tax maps.
The Florida Department of Transportation (FDOT) has recently tried to incorporate annually collected
county section-level land-use data (tax maps) with population figures gathered from the U.S. Bureau
of the Census.2 The hope is that the land use data gathered for tax purposes could be used to fine tune
or update the traditionally used census population figures and [DOTmollected land use data. The
IDOT historically gathered land use data independent of county tax data pand use) via expensive and
time consuming surveys. Both population and land use are vital components of Flor~da's traffic model,
the Florida Standard Uniform Traffic Modeling System (FS=MS). Florida needed to find a cost-
effective method of updating their data because the population explosion that Florida has been
undergoing (4500 new residents a week in the 1990's) quickly delegates the decennial census figures to
rough estimates, at best.
Merging the two types of data together into a usable form involved overcoming some incompatibility
in their different uses. The primary difficult was that annually collected land-use data are based on the
Public rend Survey System grid, whereas the census data currently used in the traffic model is based
on geographic tract block group boundaries, caned Tradic Analysis Zones (TAZ) in the models.
Solving Me problem involved three steps:
converting the TIGER coordinate file to census tract polygons;
overlaying census boundaries and section boundaries to create a new set of polygons redecting
the boundaries of each, and;
creating a cross-a~ocation table from the resulting polygon file for use in assigning the data
Dom one polygon type to the other.
One of the main limitations of this approach is that the classification codes used by county proper
appraisers do not correspond to the census codes. The county appraisers also do not break multifamily
buildings down into units, rather they merely are categorized as multifamily. Estimates on the number
of units have to be made based on the total square feet of the building. Another limitation is that the
2Hatchitt, James and M. Brady. "Flonda Department of Transportation Tests Uses of County Data to Update
Topic Model'', Geo Info Systems. May 1995, pp. 36- 41.
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merged data assume the data for each area are distnbuted evenly across space. This results In the data
being more accurate in the rural areas where several county sections fit into a single census hi rather
than in the urban areas where the census tracts are much smaller.
Electronic data interchange - Another application of secondary data involves utilizing electronic data
interchange REDO and relates to the exchange of data and information between carriers and shippers
that has traditionally been carried out through the processing, duplication, and transfer oftons of paper
work. The Increasing trend in the freight transport industry is to transfer information such as orders to
shippers, requests for fieight services, billings from carriers' and payments, electronically from
computer-to-computer, also known as electronic data interchange (ED0.3 Access to this information
by MPOs and state DOTs could provide valuable information on freight flows. International standards
and protocols among industries were developed in EDIFACT (EDI for Administration, Commerce
and Transportation) in order to simplify and expedite the Bow of freight information. As of 1990,
eighty-six percent of all customs information on imports is now being handled by ED! technology.
Similar strides on the domestic front coed provide valuable freight data that has historically been
inadequate.
Emergency Response Vehicles4 - Many emergency response providers maintain databases on all
responses. The database may include inforn'.ation regarding the location of the event, time of the
call, location of the response units responding to the call, time of dispatch, time of arrival at the
Event, time of transport, and time of arrival at the destination (hospital). Access to these
databases could allow transportation planners to use these emergency vehicles as "probes" to
determine travel times and speeds and, therefore provide an estimate of the level of congestion.
The University of Nevada recently completed a study using ambulance data to measure travel
speeds in a downtown area. The data was linked to a GIS in order to better clarify and display
3PisarsW, Alan E. '`Appendix B- New Technologies for Transportation Data Collection and Analysis:
Opportunities and Applications", Data for Decisions: Requirements for National Transportation Policy Making.
Special Report 234, Transportation Research Board, Washington, DC 1992.
4Sathisan, Shashi et al. "Using GIS for Urban Congestion Analysis". presented at the National Traffic Data
Acquisition Conference, Albuquerque, NM 1996.
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the spatial data retrieved. Some of the difficulties encountered involved having to make
assumptions on the routes taken by ambulance drivers, as well as factoring into the overall
estimate of travel delay and congestion the fact emergency vehicles are given the right-of-way.
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Representative terms from entire chapter:
freight data
