VMT Reduction Programs: VMT Reduction Programs: Time for a Change?Time for a Change?

Stacey Bricka, PhD, NuStats

sbricka@nustats.com

12th TRB Planning Applications Conference

Products of Your EnvironmentMay 18, 2009

This dissertation research was funded wholly or in part by the United States Environmental Protection Agency (EPA) under the Science to Achieve Results (STAR) Graduate Fellowship Program. EPA has not officially endorsed this publication and the views expressed herein may not reflect the views of the EPA

Research ObjectivesResearch ObjectivesThis research seeks to identify the factors

that influence trip chaining in order to better understand the policy implications of this travel behavior pattern with regard to VMT reduction programs.

61% of all working age adults trip chainThis 61% generates 68% of avg daily VMTMost trip chaining is done by automobile

Definition of Trip ChainingDefinition of Trip Chaining

A sequence of trips bounded by stops of 30 minutes or

less.

-McGuckin and Nakamoto 2004 (page 1)

Prevailing Trends*Prevailing Trends*

Increase in Working WomenChanging Household Structures

◦ Proportionately fewer Nuclear Families◦ More Single Parent and Single Person HH

Increase in Immigrants (“instant commuters”)

Growth in Automobile Travel◦ All other modes declined in numbers and in

share11% Increase VMT from 1995 to 2001***Alan Pisarski, Commuting in America III

**Center for Urban Transportation Research

Prevailing HypothesesPrevailing Hypotheses

Research QuestionsResearch Questions1. What are the factors that influence

trip chaining?2. What are the implications of trip

chaining for employer-based VMT reduction programs?

Hypothesis:Trip Chaining is a function of WHO the

traveler is and WHERE the traveler lives.

Research FrameworkResearch Framework

Proportion of Trips Chained

Destination Choices

Transportation Options

Rules & Hours of Operation

Activity Setting

Commuter Characteristi

cs

HH Factors

Work Factors

Demographic Factors

Societal Expectations

TRIP CHAINING TRIP CHAINING INFLUENCERSINFLUENCERS

DataData2001 National HH Travel Survey

◦National Sample Only◦Working Age Adults (18-65)◦Reported Weekday Travel◦N=24,626

2000 Census◦# Employees per Industry per Tract◦Proxy for Land Use

Market SegmentationMarket SegmentationAnalytical Approach - Triangulation

◦ Logit Model◦ Automatic Interaction Detection (AID)◦ Factor Analysis

Results◦ Workers – no Kids◦ Workers – w/ Kids◦ Non-Workers – no Kids◦ Non-Workers w/ Kids

Segmentation FindingsSegmentation FindingsWorkers w/ Kids

(63%)+ Females- # Adults

Workers/0 Kids (54%) + Education - # Adults

Non-Workers w/Kids(74%)+ Female+ # Kids

Non-Workers/0 Kids(66%)+ Middle Age (35 to 44)- # Adults

TRIP CHAINING TRIP CHAINING IMPLICATIONSIMPLICATIONS

Time for a Change?Time for a Change?

Employer-Based Programs Commuters

Reduce VMT through Mode Shifts

Assumes “Typical Commute”

Assumes Flexibility to Change Mode of Travel

Focus only on Travel Time and Costs

Significant Time Constraints

Fitting Non-Work Travel into Commute

No Flexibility in Travel Mode

Estimate of ReachEstimate of Reach174 million Working-Age Adults

Employer-Based Programs = Employees Only

63 million non-workers excluded

111 million workers◦ 42% have traditional commute◦ 30% live in households with 1+ Adult and 1.0+

veh/wrkr ratio

33.3 to 46.6 million targets for programs 64.4 to 77.7 million workers not targets for

programs

OptionsOptionsRefocus Existing ProgramsConsider Broader Focus

Refocusing Current Refocusing Current ProgramProgramEliminate trips for ALL workers,

regardless of work modeVMT Elimination Strategies

◦Eliminate Trips◦Main Tool = Employer Amenities◦Food Service, ATM, Postal Services,

Day Care, Convenience Stores

Broaden FocusBroaden FocusShift to Household FocusBenefits

◦ Capture 174 million working age adults (100%)

◦ Recognize differences across and within segments

Reach via Targeted Marketing◦ Explicit recognition of household composition◦ Tailor based on age of youngest child◦ Educate on VMT reduction techniques and

Mode OptionsCurrent Programs

◦ TravelSmart (Australia)◦ SmartTrips (Portland)

ConclusionsConclusionsTrip Chaining Influencers

◦Vary based on traveler and activity setting characteristics

◦Household and demographic characteristics primary

◦Differences within segments◦More influencers to be identified

Trip Chaining Implications◦Some market viability for employer-

based programs◦Stronger results if focus on households

not employers

Thank you!Thank you!

Questions and Comments?

sbricka@nustats.com