welcome 2004 florida commuter choice summit. what’s new in tdm research philip l. winters tdm...
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WELCOME2004 Florida
Commuter Choice Summit
What’s New in TDM Research
Philip L. WintersTDM Program Director
Center for Urban Transportation Research
University of South Florida
Overview
Recently completed research (partial list) 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:
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?
What explains the other 82 percent of variance in effectiveness?
Hypothesis: Work site trip reduction program effectiveness influenced by work site organizational culture.
Research Results
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
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 FactorsContributing to TRP Success
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 presentationhttp://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 Southern California State of Washington Pima County (Tucson)
Data Summary
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
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
30.00%
35.00%
40.00%
45.00%
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 incentiveswith records with 'noincentives' removed
Tucson full sampleungrouped incentives data
Washington full sampleungrouped incentives data
Models on Equally Sampled Combined Data & best independant models
Bin
Cla
ssif
icat
ion
Acc
ura
cy o
n b
ins
a2 t
o a
5
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 TRANS-TDM listserv has 770 subscribers Online Help Desk (over 330 Q&A) Netconferences
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.
Direct Point Elasticity Analysis
VOTRAN (Daytona) Elasticity= -1.69 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 A 10% increase in fares leads to a decrease in vanpool
ridership by 6%.
Vanpool Elasticity = ▲Ridership/▲ Cost * Mean Cost/Mean Ridership
Application of More Technical Research Methods
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
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
Vanpool Cost 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 Odds ratio of 1.089 Odds of choosing vanpool with respect to drive
alone increase by 8.9%
Results
Work Status Odds of choosing vanpool increase
50% for administrative employees 23% for technical field employees
Fare Elasticity (-0.61) For 10% increase in vanpool price, there is a 6%
decrease in vanpool choice with respect to auto
Conclusions and Recommendations
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
Vanpools have a “Tipping Point” where loss of one rider may collapse vanpool group Agencies should have “Vanpool Save” program to
sustain short-term fluctuations in ridership to avoid loss of groups
Sharp decreases in fares (e.g., employer-provided commute benefits) could increase vanpool ridership but data not available
Conclusions and Recommendations
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
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
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)
Benefits (frequency)
Data
Age of records
Quality control issues
Representativeness
Mandatory program
Confounding factors
Ignores $ level
No self-selection biasNumber of examples
StrengthsWeaknesses
Impact of Introducing Transit Benefit
58% reduced Vehicle Trip Rate following the introduction of transit benefits
Not controlling for changes to other benefits
23% decreased VTR by an average of 9 trips per 100 and transit share increased from 1.8% to 2.9%
Large
Increase
(5+)
Large
Decrease
(<-5)
Modest
Decrease
(0 to -5) Modest
Increase
(0 to 5)
0
50
100
150
200
250
300
350
400
No Control
No. C
om
panie
s
Impact of Introducing Transit Benefit
Large
Increase
(5+)
Large
Decrease
(<-5)
Modest
Decrease
(0 to -5)
Modest
Increase
(0 to 5)
0
5
10
15
20
25
Intro Transit Benefits - Control for Other Strategies
No. C
om
panie
s
28% reduced VTR following the introduction of transit benefits
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
Impact of Removing Transit Benefit
Large
Increase
(5+)
Large
Decrease
(<-5)Modest
Decrease
(0 to -5)
Modest
Increase
(0 to 5)
0
5
10
15
20
Control - Remove Benefit
No. C
om
panie
s
47% reduced Vehicle Trip Rate following the elimination of transit benefits
Controlling for changes to other benefits
29% decreased VTR by an average of 7 trips per 100 and transit share remained steady at 0.6%
Impact of Vanpool Benefitat Southern California worksites
Large
Increase
(5+)
Large
Decrease
(<-5)
Modest
Decrease
(0 to -5)
Modest
Increase
(0 to 5)
0
5
10
15
20
Introducing Vanpool Benefit
No. C
om
pani
es
Introducing Vanpool Benefit 47% experienced a reduction in VTRVanpool may be found in the most compre-
hensive (8 other incentives) programsWorksites with the largest reductions in VTR
saw their transit share fall by more than 1% point
Large
Increase
(5+)
Large
Decrease
(<-5)Modest
Decrease
(0 to -5)
Modest
Increase
(0 to 5)
0
5
10
15
20
Removing Vanpool Benefit
No. C
om
pani
es
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
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
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 ofResearch in Progress at CUTR
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
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
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
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
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
Determining the Elements Needed to Create High-Ridership Transit Systems
Ensuring Full Potential Ridership from Transit-Oriented Development
For More Information
Philip L. WintersTDM Program Director
Center for Urban Transportation ResearchUniversity 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
0
20
40
60
80
100
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 transitLarge staff for whom transportation cost
savings is more important than time savings and convenience
Employees remain in an office settingEmployees work routine predictable
hours
Target Receptive Organizations
Organizations that:Deal with environmental hazardsWant to cultivate a “green” imageHave employee recruitment/retention
problemsFeel a responsibility to take a
leadership role
ETCs Shoulder Great Responsibility… 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)
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 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
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 EffectivenessWhere Top Management is Supportive
Effective ETC Work Style (DiSC™)
i C D S
Program of incentives does not require active administration
Yes
Program of incentives requires active administration
Yes
Hands-off management style Yes
Program of incentives needs refining
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-ITPersonal Digital Travel Diary
TRACIT Server
Internet
GlobalPositioning
System Satellites
Communication Tower
Personal Digital Assistant w/Global Positioning System and
Wireless Connectivity Card
Complete System
WLAN 802.11b
Wireless Data Connectionthrough Cellular Provider
Wireless RouterOther Sources of Real Time Information
Testing Automatically
Captures: Time Distance Speed Route
User Enters: Trip Purpose Occupancy Mode
User Uploads Data to database
GPS Points Recorded Using Two Different Algorithms
– Continuous Update vs. Selective Update by Walking
Next Steps – Deploy Expert System
Internet
GPS Satellite
ExpertSystem
Database
Transit DataAlternate Locations
Server
Remote PC
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