WELCOME 2004 Florida Commuter Choice Summit. What’s New in TDM Research Philip L. Winters TDM Program Director Center for Urban Transportation Research.

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

[email protected](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