Commuting Connections: Carpooling and Cyberspace

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

Outline 1. Background 2. Research Partnership 3. Research Objectives 4. Literature Review 5. Methodology 6. 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 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

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 ed 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.

Gender Distribution of Survey Respondents Findings – Descriptive Analysis

Age Distribution of Survey Respondents Findings – Descriptive Analysis

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

x 2 = , p < 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 Findings – Descriptive Analysis

x 2 = , p < Findings – Descriptive Analysis

Findings – Predictive Model  Regression analysis - independent variables: 1.Demographics 2.Spatial 3.Motivations 4.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 (416)