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Commuting Connections: Carpooling and Cyberspace

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Presented by: Kalina Soltys, Ron Buliung and Catherine Habel …

Presented by: Kalina Soltys, Ron Buliung and Catherine Habel
Presented at: ACT Canada 2008 TDM Summit, Halifax, October 2008

Published in: Automotive

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  • 49% (46%) of respondents have matched through the system; 28% (16%) have formed carpools and 24% (14%) are actually carpooling; and 38% (42%) are still waiting for matches.
  • Transcript

    • 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
    • 6.  
    • 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 x 2 = 22.316, p < 0.001
    • 23. Findings – Descriptive Analysis x 2 = 39.243, p < 0.001
    • 24. 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 starting FS-formed and started DO-dropped out OTH-other
    • 25.  
    • 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. 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