WELCOME
2004 Florida
Commuter Choice Summit
What’s New in TDM
Research
Philip L. Winters
TDM Program Director
Center for Urban Transportation
Research
University of South Florida
Overview

Recently completed research (partial list)
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Commuter Choice Program Case Study Development
Worksite Trip Reduction Model and Manual
Clearinghouse
Price Elasticity of Rideshare: A Case Study for Vanpools
Analyzing the Effectiveness of Commuter Benefit
Programs: A Descriptive Analysis Approach
Research in progress
Research about to begin
Commuter Choice Program
Case Study Development and
Analysis
Sara J. Hendricks, AICP
Study Results Will Help You:
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Target Most Receptive
Work Sites
Provide Tips to
Employers to Increase
Work Site TRP
Effectiveness
Provide Tips to ETCs
Study Questions

What makes work site trip reduction
programs successful?
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What explains the other 82 percent of
variance in effectiveness?
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Hypothesis: Work site trip reduction
program effectiveness influenced by
work site organizational culture.
Research Results
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The null hypothesis is sometimes true.
Supportive management and an effective
ETC is necessary if there is poor access to
high quality transportation alternatives.
The most effective ETC usually cannot
overcome lack of management support.
The worst ETC usually cannot undermine a
work site TRP that has management support.
What We Learned
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External factors usually trump effects of
internal organizational culture.
ETCs shoulder great responsibility, but are
powerless, unsupported.
Where ETCs can make a difference, their
work style influences their success.
Relative Importance of Factors
Contributing to TRP Success
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Work site has access to
high quality transit
Large staff for whom
cost of transportation is
more important than
time savings and
convenience
Top management
support
Effective ETC
For More Information
Final Report Available
from the National Center for Transit
Research at the University of South
Florida
in pdf and HTML versions
Streaming on-demand presentation
http://www.nctr.usf.edu
Worksite Trip Reduction
Model and Manual
Philip L. Winters
Rafael Perez, PhD.
Ajay Joshi
Jen Perone
Data Collection

Compile 6,000+ worksite trip reduction plans
from employers with 100 or more workers
that have been developed and tracked for
several years
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Southern California
State of Washington
Pima County (Tucson)
Data Summary
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Over 40% of worksite trip reduction plans showed
modest reductions (up to 7 vehicle trips reduced per
100 employees) over approximately one-year period
About 13% of worksite trip reduction plans had
substantial reductions (reduced more than 7 vehicle
trips per 100 employees) in vehicle trip rates
Variables
Results
45.00%
Bin Classification Accuracy on bins a2 to a5
40.00%
35.00%
30.00%
25.00%
20.00%
15.00%
10.00%
5.00%
0.00%
58-26-1(NN)
Linear model
86-13-1(NN)
77-59-1(NN)
Linear model
No variable selection
force enter regression
No variable selection
No variable selection
force enter regression
Equally combined data
Equally combined data
LA Grouped incentives
with records with 'no
incentives' removed
Washington full sample
Tucson full sample
ungrouped incentives data ungrouped incentives data
Models on Equally Sampled Combined Data & best independant models
Accuracy on LA Validation
Accuracy on Wash validation
Accuracy on Tucson Validation
Accuracy on Training set
Worksite Trip
Reduction Model
www.nctr.usf.edu/worksite
Clearinghouse
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TRANS-TDM listserv has 770 subscribers
Online Help Desk (over 330 Q&A)
Netconferences
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Paying for Performance: Cash for Commuters (November 4, 2004)
Talk the Talk: Communicating TDM in Business Terms (June 3, 2004)
Transit-Oriented Development: Possibilities for TDM Professionals (January
27, 2004)
Using TDM to Manage Traffic at Special Events (October 15, 2003)
Access Management: Expanding the Congestion Management Toolkit
(August 20, 2003)
Bus Rapid Transit: A New Commuter Choice for your Community. (June 12,
2003)
Getting to Yes!: Lessons Learned for Increasing the Effectiveness of
Commuter Benefit Programs (December 11, 2002)
Making Telework Happen: Tips for an Effective Regional Telework Program
NetConference (August 14, 2002)
Other resources:

Carpool/Vanpool Road Signage report
Price Elasticity of
Rideshare: A Case
Study for Vanpools
Francis Wambalaba, PhD, ACIP,
Sisinno Concas
Marlo Chavarria
Elasticity Defined

The degree of responsiveness to quantity consumed with respect
to price
 Elastic: Quantity changes easily when price changes
 Inelastic: Quantity doesn't change easily with changes in price
 Elasticity = (% Change in Quantity)/(% Change in Price)

If elasticity is greater than one (elastic), then a 10% change in
price results in a more than 10% change in quantity consumed.
If elasticity is less than 1 (inelastic), then a 10% change in price
results in less than 10% change in quantity consumed.
And if elasticity is equal to 1 (unit elastic), then a change in price
by 10% results in exactly the same 10% change in quantity
consumed.
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Direct Point Elasticity Analysis
Vanpool Elasticity = ▲Ridership/▲ Cost * Mean Cost/Mean Ridership
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VOTRAN (Daytona) Elasticity= -1.69
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Fare increase from $28 to $30 per person in 2000
10% increase in fares leads to a decrease in vanpool
ridership by 16.9%
Vanpool Elasticity (Puget Sound)= -0.61
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A 10% increase in fares leads to a decrease in vanpool
ridership by 6%.
Application of More Technical
Research Methods
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Used over 260,000 employee records from State of
Washington for 1997 and 1999
Applied logistic regression modeling technique
Addresses short-comings of early models
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Model is based on mode choice, accounting for competing
modes
Model includes socio-economic predictors such as
employee job descriptions
Model assesses the impact of subsidy
Results
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Vanpool Cost
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Odds ratio value of -2.6
$1 increased in vanpool price is associated with
2.6% decrease in the predict odds of choosing
vanpool with respect to drive alone
Vanpool Subsidy
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Odds ratio of 1.089
Odds of choosing vanpool with respect to drive
alone increase by 8.9%
Results
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Work Status
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Odds of choosing vanpool increase
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50% for administrative employees
23% for technical field employees
Fare Elasticity (-0.61)
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For 10% increase in vanpool price, there is a 6%
decrease in vanpool choice with respect to auto
Conclusions and
Recommendations
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Elasticity rates for vanpooling vary widely
(limited datasets)
More likely to be very elastic relative to transit

Vanpool industry faces volatile conditions and
rapid growth complicating elasticity – influence of
fares and subsidy on ridership – and making it
difficult to generalize
Conclusions and
Recommendations
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Vanpools have a “Tipping Point” where loss
of one rider may collapse vanpool group
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Agencies should have “Vanpool Save” program to
sustain short-term fluctuations in ridership to
avoid loss of groups
Sharp decreases in fares (e.g., employerprovided commute benefits) could increase
vanpool ridership but data not available
Conclusions and
Recommendations
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Vanpool industry should develop guidelines
for comparable data collection
More cooperation needed
Future models should recognize the
multiplicity of factors influencing mode choice
More research needed with respect to the
effect of on-going subsidies versus temporary
discounts
Analyzing the Effectiveness
of Commuter Benefit
Programs: A Descriptive
Analysis Approach
Philip L. Winters
Chris Hagelin
Ajay Joshi
Under subcontract to ICF Consulting
Methodology
Benefits (frequency)
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Isolate worksite records
where an individual
worksite either introduced
or eliminated a benefit
Focus on records where
other programs did not
change (control)
Examine changes in
vehicle trip rate as well as
transit share after either
introducing or removing a
benefit
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Compressed work week (37)
Direct non-financial benefits (119)
Facilities & Amenities (73)
Financial benefits other than
transit and vanpool (104)
Flextime
Guaranteed Ride Home (48)
Marketing (88)
Onsite (145)
Parking management (13)
Rideshare matching (73)
Telecommute (14)
Transit Benefits (57)
Transit Benefits (no control) (943)
Vanpool (51)
Data
Age of records
Quality control issues
Representativeness
Mandatory program
Confounding factors
Ignores $ level
Weaknesses
No self-selection bias
Number of examples
Strengths
Impact of Introducing Transit
Benefit
Not controlling for changes to other benefits
58% reduced Vehicle Trip
Rate following the
introduction of transit benefits
400
350
No. Companies
23% decreased VTR by an
average of 9 trips per 100
and transit share increased
from 1.8% to 2.9%
Modest
300
Decrease
250
200
(0 to -5)
Modest
150
Large
Increase
Decrease
(0 to 5)
100
(<-5)
Large
Increase
50
(5+)
0
No Control
Impact of Introducing Transit
Benefit
Controlling for changes to other benefits
 Worksites that introduced
transit benefits were more
than twice as likely to have
the vehicle trip rate
increase as decrease
No. Companies
 28% reduced VTR following
the introduction of transit
benefits
25
Modest
20
Increase
(0 to 5)
15
10
5
Large
Increase
(5+)
Modest
Large Decrease
Decrease (0 to -5)
(<-5)
0
Intro Transit Benefits - Control for Other Strategies
Impact of Removing Transit
Benefit
Controlling for changes to other benefits
47% reduced Vehicle Trip
Rate following the elimination
of transit benefits
29% decreased VTR by an
average of 7 trips per 100
and transit share remained
steady at 0.6%
No. Companies
20
Modest
15
Increase
Large
10
(<-5)
5
(0 to 5)
Decrease
Modest
Decrease
(0 to -5)
Large
Increase
0
Control - Remove Benefit
(5+)
Impact of Vanpool Benefit
at Southern California worksites
Introducing Vanpool Benefit
No. Companies
20
Modest
15
Modest
Increase
Decrease (0 to 5)
10
(0 to -5)
Large
Large
5
Increase
Decrease
(5+)
(<-5)
0
Introducing Vanpool Benefit
 47% experienced a reduction in VTR
 Vanpool may be found in the most comprehensive (8 other incentives) programs
 Worksites with the largest reductions in VTR
saw their transit share fall by more than 1%
point
Removing Vanpool Benefit
No. Companies
20
Modest
15
Increase
Large
10
Decrease
(<-5)
5
(0 to 5)
Modest
Decrease
(0 to -5)
Large
Increase
(5+)
0
Removing Vanpool Benefit
 28% saw their transit share increase by over
1% point
 46% had an average decrease in VTR of 5.9
trips per 100 employees
Findings and Conclusions
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More than likely the introduction of
transit benefits may result in a
reduction in VTR, but it is not
guaranteed

Conversely, the elimination of transit
benefits does not mean a loss of
transit share
Findings and Conclusions
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Transit benefits are most effective when there
are fewer other incentives programs to
compete for the commuter’s attention
Within commuter choice programs, more
choices often means more competition between
benefits
Employers must understand that some benefits
complement each other and others compete
with one another
Partial List of
Research in Progress at CUTR
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Traveling Smart: Increasing Transit Ridership By Automatic
Collection (TRAC) of Individual Travel Behavior Data and
Personalized Feedback
Return on Investment Analysis of Bikes on Bus programs
South Florida Commuter Services Evaluation
Incorporating TDM into the Land Development Process
Teenage Attitudes and Perceptions Regarding Transit Use
Impacts of Development on Public Transit Ridership
Enhancing the Rider Experience: The Impacts of Real-time
Information on Transit Ridership
TDM Evaluation and Measurement for Atlanta’s Framework
Partners
Research About to Begin at
CUTR
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National Smart Transportation Archive
Researcher (NSTAR) (case studies)
Impact of Employer-based Programs on
Transit System Ridership and Transportation
System Performance
Wireless Video for Instant Access (Wi-Via)
Security System
National Research - Completed
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TCRP Report 63: Enhancing the Visibility and
Image of Transit in the United States and
Canada
TCRO Report 102: Transit-Oriented
Development: State of the Practice, and
Future Benefits
TCRP Report 87: Strategies for Increasing
the Effectiveness of Commuter Choice
Programs
New Publications - FHWA
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Mitigating Traffic Congestion: The Role of
Demand Side Strategies
Traffic Congestion and Reliability: Linking
Solutions to Problems
Commuter Choice Primer: An Employer's
Guide to Implementing Effective Commuter
Choice Programs
National Research in Progress
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Analyzing the Effectiveness of Commuter Benefits Programs
 TCRP H-25A: Completion Date: December 31, 2004
Update the "Traveler Response to Transportation System
Changes" Handbook
 TCRP B-12A. Completion Date: December 31, 2004
Carsharing: Where and How It Succeeds
 TCRP B-26. Completion Date: April 9, 2005
Guidelines for Evaluating, Selecting, and Implementing Suburban
Transit Services
 TCRP B-25. Completion Date: April 22, 2005
Understanding How Individuals Make Travel and Location
Decisions: Implications for Public Transportation
 TCRP H-31. Completion Date: August 16, 2005
National Research - Pending
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Determining the Elements Needed to Create
High-Ridership Transit Systems
Ensuring Full Potential Ridership from
Transit-Oriented Development
For More Information
Philip L. Winters
TDM Program Director
Center for Urban Transportation Research
University of South Florida
winters@cutr.usf.edu
(813) 974-9811
Ramifications for Evaluating Work Site TRP
Success (what TDM professionals can do)

Set realistic
trip reduction
targets for
organizations
based upon
benchmarking
Figure 1: Change in Vehicle Trips Reduced
for Participating Work Sites
100
80
60
40
20
0
1995
1997
1999
2001
2003
"A"
"B"
"C"
"D"
"E"
"F"
"G"
"H"
"I"
"J"
"K"
"L"
"M"
What TDM Professionals Can Do

Encourage
employers to
locate where
there are high
quality
transportation
alternatives

Target more
receptive
organizations
Target Receptive Organizations
Work site access to good quality transit
 Large staff for whom transportation cost
savings is more important than time
savings and convenience
 Employees remain in an office setting
 Employees work routine predictable
hours
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Target Receptive Organizations
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Organizations that:
 Deal with environmental hazards
 Want to cultivate a “green” image
 Have employee recruitment/retention
problems
 Feel a responsibility to take a
leadership role
ETCs Shoulder Great
Responsibility…
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Most ETCs did not
volunteer for job.
ETCs required to do
duties on own time.
ETC duties not
recognized in job
description.
Many ETCs could not
identify a supervisor.
Performance of ETC
duties not part of job
evaluation.
Administering Commuter
Survey onerous.
Policy Considerations for Designing TROs
(What TDM Professionals can do)
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Designation of an ETC may not be
necessary.
For commuter surveys, require a random
sample that is representative of the employee
population than an across-the-board high
response rate.
What Employers Can Do
to Help Their ETCs
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Ask for a volunteer ETC
Incorporate job duties of
ETC into job description
Arrange for ETC to
report directly to top
management, preferably
to same supervisor as
for other duties
Carefully select
volunteer with work style
that matches demands
of the job
ETCs Can Make a Difference

Profile of ETCs with More Successful TRPs
 High “Influencing” work style (DiSC™)
 High Expressed Affection (FIRO-B)
 Low need for control (FIRO-B)
 Values Relations over Work (CVAT)
 Values Flexibility and Political Savvy
(CVAT)
DiSC™ Instrument
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Premise 1: No work style is better than
another. Every work style makes a valuable
contribution. Each person has strengths and
weaknesses under varying work conditions.
Premise 2: People are capable of adapting
their behaviors to fit the needs of a situation.
Scenarios for ETC Effectiveness
Where Top Management is Supportive
Effective ETC Work Style
(DiSC™)
i
Program of incentives does Yes
not require active
administration
Program of incentives
requires active administration
Hands-off management style
Program of incentives needs
refining
C
D
S
Yes
Yes
Yes
Traveling Smart:
Increasing Transit Ridership By
Automatic Collection (TRAC) of
Individual Travel Behavior Data and
Personalized Feedback
Department of Computer Science &
Engineering,
Center for Urban Transportation Research
(CUTR),
and the National Center for Transit Research
(NCTR)
TRAC-IT
Personal Digital Travel Diary
Complete System
Wireless Data Connection
through Cellular Provider
Global
Positioning
System Satellites
TRACIT Server
Communication Tower
Internet
WLAN 802.11b
Personal Digital Assistant w/
Global Positioning System and
Wireless Connectivity Card
Wireless Router
Other Sources of Real Time Information
Testing

Automatically
Captures:
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User Enters:
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GPS Points Recorded Using Two Different Algorithms –
Continuous Update vs. Selective Update by Walking
Time
Distance
Speed
Route
Trip Purpose
Occupancy
Mode
User Uploads Data to
database
Next Steps – Deploy Expert
System
GPS Satellite
Alternate Locations
Transit Data
Server
Internet
Expert
System
Database
Remote PC