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1
Appendix 1
MPO Survey: Questionnaire and
Summary of Results
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Jack Faucett Associates, Inc.
Final Report
March 1997
MPO TRANSPORTATION SURVEY RESULTS
As part of the NCHRP research project on transportation data collection, MPOs were surveyed
with respect to their data collection needs and collection activities. This appendix describes the
survey instrument, outlines the sample development, summarizes the survey responses, and draws
several conclusions regarding MPO data activities.
Survey Instrument
A copy of the survey questionnaire is provided at the end of this appendix. The survey instrument
was designed to provide information in a variety of areas:
current modelling activities.
data used to support modelling,
data collected to monitor growth and transportation system use,
organization/storage of data,
hardware and software support, and
gaps in data availability.
The survey fonn was designed to be self-a~ninistered. It was mailed to the sample. Follow-up
calls were conducted for those recipients that did not respond within two weeks. In most
instances, the calls became phone interviews.
NCHRP Multimodal Transportation
Planning Data
Al-l
Project 8-32~5)
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Jack Faucett Associates, Inc. Final Report
.
Survey Sample
March 1997
The sample included twenty-f~ve MPOs. The sample was selected to provide broad geographic
coverage and diversity in size of the area represented.
A total of sixteen of the survey recipients responded to the survey form either by return mail or
through a phone interview. No attempt was made to augment the responses since the survey was
designed to identify general trends and activity patterns rather than establishing Weir statistical
validity. The respondents are categorized in Exhibit 18 of Section 2.2 by size of weir urban area.
Survey Responses
The survey responses are summarized below in the order of the questions.
Question No. 1: Principal Forecasting Models for Travel Demand, Demand Management and
Air Quality Analysis-Most of Me respondents identified a specific modelling package for travel
demand forecasting. Tranplan was the most widely used package, primarily by Me medium-sized
MPO's. A modified version of Tranplan (FSTUMS) is the standard modelling package in the
State of Florida.
The larger MPOs did not have a typical forecasting package. Two of the MPOs use their own
mainframe models which are partially based upon UTPS or emme2. Over large MPOs use the
standard emme2 package' TMODEL2' or minutp.
Very few of the respondents currently simulate travel demand management approaches in the
model stream. Only one cited use of an actual model. Two of the MPOs re-adjust the trip table
in an attempt to forecast TDM impacts on travel levels. Several of the respondents are anticipating
modifications to their modelling system for sensitivity to TDM options.
NCHRP-Multimodal Transportation Al-2
Planning Data
Project 8-32~5)
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March 1997
_
Most of the MPOs conduct air quality modelling because of federal requirements. MOBILES and
MOBILSa are the primary air quality models used by the respondents. Several other models were
mentioned by individual respondents, including PPAQ, CAL3QHC, EMIS, and DTIM.
Question No. 2: Principal Data Sources-This question has considerable overlap with question
No. 1 1 dealing with transportation system surveillance data sources. Much of the surveillance
data is used as the base for forecasting future year demographic characteristics and for calibrating
the forecasting models.
With respect to the key data for forecasting, most respondents begin with U.S. Census data for
population characteristics, and state statistics for employment. The population data is frequently
updated using building permits, utility hook-ups, aerial photography, field surveys, and meetings
with representatives of the various local jurisdictions. The employment data characteristically ties
employment to central offices rather than the actual physical location. Therefore, the state
employment data requires extensive field survey or phone interviews to check accuracy.
Four of the large MPOs have either completed a recent household survey, or have one scheduled
within the next two years. A few of the respondents had conducted recent origin/destination travel
surveys.
The Florida Application for Development Approval (ADA) provides an extremely rich data base
for MPOs in that state. In addition to identifying development plans, the application provides
detailed information and land characteristics such as soil type, as well as current aerial
photography for the site. These data, collected at private expense, significantly enhance the ability
of the MPOs to forecast the type, intensity, and location of future development.
Data sources for forecasting the impacts of various Travel Demand Management (TDM)
techniques are not nearly as extensive. Most of the spreadsheet models used to evaluate these
NCHRP-Multimodal Transportation . A1-3
Planning Data
Project 8-32(5)
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Final Report March 1997
strategies are based upon a limited supply of published data collected in a handful of cities. The
Institute of Transportation Engineers has published the most extensive documentation of TDM
analysis, but it does not appear to be used extensively at this time.
Most of the data used to drive the air quality models are derived from the travel forecasts. This
includes the percent of hot and cold starts, travel speeds, and travel volumes. As me models are
becoming more sophisticated, however, they are requiring more finite data than the models can
predict with reliable accuracy.
Question No. 3: Anticipated Changes to Modelling Procedures - Over three quarters of the
respondents identified impending changes they would be making to the modelling process. The
most frequently cited changes were increased sensitivity to time-of-day and incorporation of
trip-chaining. Other changes identified by individual respondents are listed below:
· increased market segmentation,
· peak spreading,
o
trip distribution sensitivity to peak hour impedances,
· trip generation sensitivity to modal availability, and
· incorporation of new Highway Capacity Manual.
Question No. 4: Sources of Key Data This question was found to be redundant with question
No. 2. Information reported here that had not already been reported under question No. 2 was
reported as a No. 2 response.
Question No. 5: Organization of Data This appeared to be the most challenging question on
the survey. A number of respondents that were contacted by phone appeared to be confused by
the question. They typically asked for an explanation or more detailed example. Some
NCHRP Multimodal Transportation A14
Planning Data
Project 8-32~5)
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March 1997
respondents would then agree that Hey followed the example cited in He question, but would then
explain how they were different.
Half of the respondents indicated that they followed the data framework outlined in the question
(i.e., model development, model calibration, and TIP development), while the other half indicated
that they followed another format. To some extent, the larger MPOs appeared to be more likely
to have organized the data in a particular format.
Of the respondents that said Hey organized data differently from the example, the general theme
was to separate the data into two categories: 1) model inputs and 2) system monitoring. Air
quality was identified as another possible category by which to organize data.
Question No. 6: Use of a Centralized Data Base-As with question No. 5, this question resulted
in an even split between the respondents. Eight of the agencies have a centralized database, while
eight do not. Those that have the central data base typically maintain the data themselves. The
data base is usually accessible by the state-DOT, local counties, and local cities. One of the
respondents make the data available Trough the Internet. Most of He agencies did not think there
was redundancy in the data collected or stored.
About half of the respondents outlined concerns regarding ~nter-organ~zational data sharing. These
concerns are outlined below:
.
uncertainty regarding He responsibility for the data or collection techniques and fear of a
shift of data gathering and maintenance responsibilities to the MPO,
· difficulty converting others' data to a readable format,
· consistency in the level of detail,
· differences in software packages used by the various agencies, and
· access to data collected by others.
NCHRP-Multimodal Transportation Al-S
Planning Data
Project 8-32~5J
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March 1997
Question No. 7: type of Hardware Platform-Eleven of the sixteen respondents use personal
computers for modelling activities. The pc's are typically networked. Two of the large MPOs
use a mainframe. Another two use Sun work-stations. One of the mid-sized MPOs uses a
minicomputer. Each of the agencies uses the same hardware platform for modelling and data
storage.
Question No. 8: Database Software-There was kale consistency in the database programs used
by the respondents. The most commonly used software is dBase, but this is used by only six
respondents. Four of the agencies identified ARCINFO as their database software. A number of
agencies listed several software packages. The remaining programs identified in the survey
include: Access' Atlas GIS' Rapid File, Paradox' STATA, Foxpro' SAS, and SPSS.
Question No. 9: Data Storage Upgrades-Over half of the respondents have short-term plans to
improve their data storage capabilities. Most in this group intend to increase storage capacity.
One is switching to a relational database while another is adding GIS capabilities. The smaller
MPOs appear to be in a "catch-up" mode, one adding a network, and another attempting just to
keep up with technology.
Question No. 10: Use of GIS-Fourteen of the sixteen agencies already use GIS in some form
in their planning process. Over half of the respondents have integrated their travel demand
models with GIS. The remaining agencies use GIS for data storage or mapping of information.
Surprisingly, two of the three small agencies are already using GIS. Of the two agencies that are
not using GIS, one is small and one is large. Both are planning on implementing GIS in the near
future.
Question No. 11: Surveillance Data Sources-This question covered data sources for seven
areas: demoaraphic/socio-economic. vehicle volumes, public transit, intermodal, construction
monitoring, travel mode information, and travel time/trip length.
NCHRP Multimodal Transportation A1-6
Planning Data
Project 8-32~5)
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March 1997
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Most of the respondents listed the U.S. Census as a key source for demographic data. This
information is typically updated and checked through a variety of sources including building
permits, utility hook-ups, aerial photography, field survey, and meetings with individual
jurisdictions.
Employment data is generally based upon state statistics. While this source is reasonably
comprehensive, it frequently assigns branch office or remote employees to the central office or
payroll location. This significantly distorts the actual geographic distribution of employees,
requiring extensive cross-checking and field work by the MPO.
The state-DOT was the most commonly cited source of data on traffic counts. While there was
some variation in the functional classes of roadways which they monitored, almost all of the
respondents depend on the state for some traffic data. Cities and counties also contribute count
data in a number of areas. About one third of the MPOs collect count data themselves.
Public transit data is typically provided by the transit operator. In most instances, this is an
independent transit authority. In a few cases, the core city operates the bus system. Transit data
provided by the transit operator typically include standard Section 15 information. Occasionally,
onboard surveys are conducted to be used for model updates. The MPO is sometimes responsible
for the administration of the survey.
Intermodal data is difficult to obtain in most cities. Total freight volumes are available Trough
public port authorities, or interviews with shippers. Detailed information on specific commodities
or shipping patterns is not readily available. Due to the competitive nature between private
shippers, it appears that such information may always be difficult to obtain.
Construction status infonnation is typically provided by the state-DOT for its projects. The MPOs
contact appropriate local agencies to update the status of their projects.
NCHRP Multimodal Transportation A1-7
Planning Data
Project 8-32~5)
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March 1997
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Travel mode information varies by location. Bicycle and pedestrian data are not collected
regularly in most areas. Auto occupancy information is typically regional in nature, unless a
special study has been conducted along a specific facility. About one-third of the respondents had
conducted some type of Gavel survey over the last several years that provided some information
on travel modes. Two of the three small MPOs were included in this group.
Travel time data is not being collected comprehensively on a regular basis. Six respondents
indicated that they had conducted origin/destination surveys which provided travel time
information. Two respondents noted speed and delay studies.
Question No. 12: Data Gaps-Most of the respondents identified gaps in data resources. Most
of the problems fell in one of three data areas: population/employment, travel characteristics, and
traffic counts.
Accurate employment information by place of work was the most frequently mentioned problem.
As mentioned earlier, the state employment information Epically aggregates employees by central
office or payroll locations. This requires extensive effort on the part of the MPO to disaggregate
the data to actual work location. On the population side, one respondent desired more information
on household income.
A wide variety of needs were identified for travel data. Medical and recreational trips were
mentioned as areas needing more research. Better identification of external to external trips was
also mentioned. Finally, trip route information was also identified as a need.
Traffic counts are needed more often, in more locations, and for shorter periods of time. In one
instance, the accuracy of the state counts for freeway links was cited as unreliable. In general,
there was a desire to have more opportunity to check the validity of existing data.
NCHRP Multimodal Transportatwn Al-8
Planning Data
Project 8-32~5J
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_ March 1997
Other general areas of need included better concurrence between population, dwelling units, and
auto registration databases, current information on transportation funding, detailed freight
movements, and roadway inventories.
Question No. 13: Recommended State Data Collection Acidities-Although several respondents
suggested more~state activity in collection of traffic counts, there were no recommendations that
received broad support. The Florida respondent suggested that the state standardize GIS use for
MPOs within its jurisdiction. This would be comparable to Florida DOT's approval of a specific
forecasting software. Other suggestions included: goods movement for Intermodal Management
Systems (IMS), better employment data, speed and delay studies, and broader roadway inventory.
Summary of Conclusions
The survey results appear to support two general conclusions.
· Many MPOs do not have a long-term vision with respect to organization of data.
· Data needs are increasing, which will place further demands on data gathering, storage,
and maintenance activities.
As indicated above, many respondents appeared to be confused by question No. 5 regarding their
data organization framework. This suggests that the current data organization is likely to be more
a reaction to short-term needs than a thoughtful long-term strategy. This is reinforced by the
significant absence of a centralized database in half of the MPOs. The lack of the database was
evenly split for small, medium, and large area agencies. It does not seem to be related to the size
or complexity of the organization, or funding availability. Again, it seems to be more a lack of
long-range vision.
NCHRP- Mulfimodal Transportation Al-9
Planning Data
Project 8-32~5)
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. , .
_March 1997
Most of the agencies desire to increase the sophistication of their models. As air quality models
become more complex, more time-specific data will be required to support their operation.
Limited transportation intending is beginning to necessitate more accurate forecasting and analysis
of congested roadways. A greater emphasis will be placed on peak hour forecasts. This will
require a shift from a 24-hour or weighted peak/off-peak analysis format in many cities. Better
representation of peak spreading will be required in all models. Effective modelling of TDMs will
also grow in importance as low-cost solutions become necessary.
Finally, better representation of medical and recreational trips will become more critical as the
number of these trips grows with an aging society. The significant shift to two-worker households
has created a need to undersuand and represent Hip chaining in the models, if they are to represent
reality.
With the flood of information and information technology, the natural reaction has been to focus
on immediate opportunities to apply both. While this may have improved our responsiveness in
the short teen, it exacerbates the long-tenn condition. The complexity of our travel behavior and
transportation funding needs are eclipsing both information and technology. If we are to keep pace
with our needs, we must take the time to fully evaluate the resources available, and consciously
develop a plan to use those resources to the fullest.
NCHRP Multimodal Transportation A1-10
Planning Data
Project 8-32~5)
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Metropolitan Planning Organizations
Multimodal Transportation Planning Data Needs
Survey Instrument
1. Please identify principal models that are used for:
A.) Travel modeling and forecasting (trip generation, trip distribution, mode choice,
and network assignment)
.
B.) Transportation demand management
C.) Measurement of emissions affecting air quality
2. Please list principal data sources for input to models listed above:
A.)
B.)
C.)
-
NCHRP-Multimodal Transportalvion Al-ll Project 8-32(5)
Planning Data
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3.
Is your agency considering implementation of new models (e.g., time-of-day, trip
chaining, non-motorized transportation, etc.) in the transportation planning system? If so,
which ones, and what are the major issues driving the decision?
4. What are Me sources of the key data used to derive the models? (Please list primary and
secondary data sources by model.)
5. Do you currently organize your data collection activities along the lines of model
development, model calibration, and TIP development?
Yes
No
If "no", how would you characterize your data organization framework?
NCHRP-Mu~imo~Transportation A1-12 Project8-32(5)
Planning Data
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6. Do you maintain a centralized database for the input data listed in 2 and 4 above?
Yes
No
A.) If "yes", which organizations access the database?
B.) Which organizations maintain the data?
C.) . If "no", do you believe there is redundancy in the types of data collected or stored?
D.) Are there any inter-organ~zational data sharing issues? (These problems could be
hardware/soiftware related, data scale related, or related to proprietary nature of the
clata.)
NCHRP - Multimodal Transportation Al-13 Project 8-32~5)
Planning Data
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I .
7 What type of computer hardware platform do you use for
A.) modeling?
B.) data storage?
S. What database software packages are currently in use?
9. Are you planning to update your data storage capabilities in the near term?
Yes
No
A.) If "yes", what are you planning to change?
NCHRP-Multimodal Transportation Al-14 Project 8-32(5)
Planning Data
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10. Do you currently use a GIS system to support transportation planning?
Yes
No
A.) If "yes", please briefly describe how the system is used, the software package that
is used, and the types of data which support any analytic capability.
B.) If ``no`~' are you planning on using a GIS system in the near future? (Please
explain.)
NCHRP-Multimo~Transportation Al-lS Project8-32(5J
Planning Data
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_
11.
What are your transportation system surveillance data sources?
A. ~ Demographic/Socioeconomic Characteristics
B.) Vehicle Travel Volumes (Peak Period and ADT Counts)
C.) Public Transit System Characteristics
D.) Intermodal System Characteristics (Rail, Truck, Air, Waterway, Pipeline, Other)
E.) Construction Project Monitoring Information
F.) Travel Mode Information (SOY, HOV, Transit, Bicycle, Pedestrian)
G.) Travel Time and Trip Length Information
NCHRP-Multimodal Transportation A1-16 Project 8-32~5)
Planning Data
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12. What are the major gaps between the data you have and the data that you need?
13. Can you suggest data you need (and not available to you) that could be collected (or
otherwise developed) more efficiently by the state DOT and made available to you?
NCHRP Multimodal Transportation A1-17 Project 8-32~5)
Planning Data
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Representative terms from entire chapter:
data project
