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 1. Background 2. Research Partnership 3. Research Objectives 4. Literature Review 5. Methodology 6. Findings 7. 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 1. Model determinants in forming a successful carpool 2. Explore gender differences in carpooling attitudes and behaviours 3. Evaluate the performance of Carpool Zone and provide recommendations for the refinement and extension of the program 4. 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. Gender Distribution of Survey Respondents Findings – Descriptive Analysis

20. Age Distribution of Survey Respondents Findings – Descriptive Analysis

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

22. x 2 = 22.316, p < 0.001 Findings – Descriptive Analysis

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

24. x 2 = 39.243, p < 0.001 Findings – Descriptive Analysis

26. Findings – Predictive Model  Regression analysis - independent variables: 1.Demographics 2.Spatial 3.Motivations 4.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. 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

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