Commuting Connections: Carpooling and Cyberspace

Presented at the Association for Commuter Transportation TDM Summit, Halifax, October 21, 2008

by:Catherine HabelProgram Coordinator, Smart CommuteMetrolinx

Co-authors:Kalina SoltysMaster’s CandidateUniversity of Toronto at Mississauga

Ron BuliungProfessor, Department of GeographyUniversity of Toronto at Mississauga

Outline1. Background 2. Research Partnership3. Research Objectives4. Literature Review5. Methodology6. Findings 7. Conclusions

Background – Smart Commute

Background – Carpool Zone Online ridematching service Administered and paid for by Metrolinx Open and free of charge to the public Promoted by ten TMAs at GTHA employers

Research Partnership University of Toronto at Mississauga (UTM)

Department of Geography Since 2006 with Smart Commute Association, Smart

Commute Mississauga and Peel Region 2008 data-sharing agreement between Metrolinx & UTM Centre for excellence – commuting research in Canada

Research Partnership (cont.) Resources

in-kind time Assistant Professor, UTM

– Directing research– Coordinating funding proposals

Undergraduate/graduate student, UTM Program Coordinator, Smart Commute

– Conducting CPZ satisfaction survey– Compiling database– Reviewing draft reports

Data extraction capabilities, Pathway Intelligence

Research Partnership (cont.) Benefits:

Building capacity for TDM Practical application for student research In-depth analysis of data set New knowledge of carpool behaviour Canadian example Policy direction Smart Commute profiled during Geography Week Guest lecture at UTM

Research Objectives1. Model determinants in forming a successful

carpool2. Explore gender differences in carpooling

attitudes and behaviours3. Evaluate the performance of Carpool Zone and

provide recommendations for the refinement and extension of the program

4. Inform Smart Commute policies and programming

Research Objectives (cont.)

How do socio-demographic, economic, attitudinal, and spatial factors influence carpool formation and use?

How can we leverage the power and flexibility of other systems (e.g., Internet) to do a better job in the task of moving people?

Literature Review Existing thoughts about differences in levels of

mobility and commuting patterns Literature on gender and travel behaviour Literature on the use of ICT to improve urban

mobility

Methodology – Survey Yearly survey a component of SC monitoring and

evaluation framework, fall 2007 Individualized link e-mailed to all registered users Incentive provided – draw for iPod Touch Reminder (319 additional responses) Responses associated with profile information Excel database extracted, identifiers removed,

data provided to UTM Follow up questions and clarifications

Methodology – Questionnaire 22 questions, multiple choice or one answer Reasons for interest in carpooling Usage level (carpooling, waiting for better matches, etc.) Ratings of Carpool Zone features and services Ease of use and extent of feature usage Communication between users Follow up (testimonials and further input) Recommendation Open comment field

Methodology – Profile Information Home postal code Gender Age Household car ownership Commute mode Length of trip (time) Language Community characteristic urban/suburban and

median income by FSA (inferred)

Methodology – UTM Modelling Exploratory/descriptive analysis of motivations,

current commuting behaviour, and performance.

Logistic regression analysis of the likelihood of successfully forming and using a Carpool Zone- enabled carpool.

Methodology – Challenges Researchers would have preferred more

demographic information e.g.: Education level, individual and household income,

occupation SC does not ask these questions for privacy reasons

Destination information Weren’t able to provide this with the first data set,

however, trip information has since been extracted and provided to UTM – findings should be available by the end of this year

Findings – Descriptive Analysis 1,425 respondents (25% response rate) 89% of respondents are satisfied with the

service overall Of those who formed carpools through the

system, 84% were satisfied with the quality of the carpools.

87% of respondents would definitely or likely recommend Carpool Zone to their friends and colleagues.

41%

43%

16%

Male

Female

NA

Gender Distribution of Survey Respondents

Findings – Descriptive Analysis

0

2

4

6

8

10

12

14

16

18

20

under 20 20-24 25-29 30-34 35-39 40-44 45-49 50-54 55-59 60-64 65-69 70-74 75-79

age category

% r

esp

on

den

ts

Age Distribution of Survey Respondents

Findings – Descriptive Analysis

Overall Satisfaction with Carpool Zone

Res

po

nse

POOR

NOT VERY GOOD

SATISFACTORY

VERY GOOD

EXCELLENT

MALE (n=486) FEMALE (n=505)

U = 122,657.00, p > 0.10

Findings – Descriptive Analysis

Motivations for CarpoolingR

esp

on

se

DON’T DRIVE

CAR UNAVAILABLE

SAVE MONEY

ENVIRONMENT

HOV USE

OTHER

MALE (n=495) FEMALE (n=532)

x2 = 22.316, p < 0.001

Findings – Descriptive Analysis

24% have started carpooling

Legend:JR-just registeredWM-waiting for matchWBM-waiting for better matchWR-waiting on responseFWOS-formed without startingFS-formed and startedDO-dropped outOTH-other

Findings – Descriptive Analysis

Current Commute MethodR

esp

on

se

PUBLIC TRANSIT

DRIVE ALONE

GET DROPPED OFF

DRIVE A CARPOOL

PASSENGER IN A CARPOOL

BICYCLEWALK

OTHER

MALE (n=535) FEMALE (n=557)

x2 = 39.243, p < 0.001

Findings – Descriptive Analysis

Findings – Predictive Model Regression analysis - independent variables:

1.Demographics2.Spatial3.Motivations4.Current commute mode

Findings – Demographic More females (13%) in carpools than males (11%) Gender has greatest explanatory effect:

female respondents are 1.3 times more likely to be carpooling

Age and inferred median income insignificant Demographic information “parsimonious”, further

research required

Findings – Spatial Matching potential close to home (significant within 1 km

buffer zone) Addition of one match within 1 km of residence

increases the odds of forming a carpool by 4-21% Increase of matches within broader market (> 3 km)

doesn’t appear to increase rate of carpooling Distance from carpool lot, urban v. suburban and place

of residence don’t appear to be significant More research being conducted to include trip-end

variables into analysis

Findings – Motivations Environment and cost had similar effects but

weren’t considered significant Desire to use an HOV lane was the only

significant motivational factor that explained carpool formation and use associated with saving time almost two times more likely to form a carpool than

concern for the environment

Findings – Current Commute Mode Transit commuters 40% less likely to form a

carpool than SOV commuters Passengers 1.8 times more likely to form a

carpool than SOV commuters Insufficient evidence with respect to active

commuters

Conclusions Utility in considering residential-based

marketing Urban density (home) = more carpools Accessibility to potential matches near the

home is associated with carpool formation Potentially important role of HOV lanes (even

more than carpool lots)

Conclusions (cont.) Making connections…:

with academic institutions and researchers keen to contribute knowledge to our field

with the next generation of TDM practitioners by looking at the Canadian context between the various factors that influence commuter

behaviour

Thank You

Catherine Habel Smart Commute, Metrolinx

catherine@smartcommute.ca, (416) 874-5934