Commuting Connections:

1. Commuting Connections: Carpooling and Cyberspace

2. Presented at the Association for Commuter Transportation TDM Summit, Halifax, October 21, 2008 by: Catherine Habel Program Coordinator, Smart Commute Metrolinx Co-authors: Kalina Soltys Master’s Candidate University of Toronto at Mississauga Ron Buliung Professor, Department of Geography University of Toronto at Mississauga

3. Outline • Background • Research Partnership • Research Objectives • Literature Review • Methodology • Findings • Conclusions

4. Background – Smart Commute

5. 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

7. 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

8. 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

9. 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

10. Research Objectives • Model determinants in forming a successful carpool • Explore gender differences in carpooling attitudes and behaviours • Evaluate the performance of Carpool Zone and provide recommendations for the refinement and extension of the program • Inform Smart Commute policies and programming

11. 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?

12. 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

13. 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

14. 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

15. 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)

16. 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.

17. 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

18. 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.

19. Findings – Descriptive Analysis Gender Distribution of Survey Respondents

20. Findings – Descriptive Analysis Age Distribution of Survey Respondents

21. Findings – Descriptive Analysis U = 122,657.00, p > 0.10

22. Findings – Descriptive Analysis x2 = 22.316, p < 0.001

23. Findings – Descriptive Analysis 24% have started carpooling Legend: JR-just registered WM-waiting for match WBM-waiting for better match WR-waiting on response FWOS-formed without startingFS-formed and started DO-dropped out OTH-other

24. Findings – Descriptive Analysis x2 = 39.243, p < 0.001

26. Findings – Predictive Model • Regression analysis - independent variables: • Demographics • Spatial • Motivations • Current commute mode

27. 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

28. 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 – Spatial

29. 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

30. 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

31. 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)

32. 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

33. Thank You Catherine Habel Smart Commute, Metrolinx catherine@smartcommute.ca, (416) 874-5934