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Environmental Impacts of Shared Mobility: Insights from North America
This document summarizes research on the environmental impacts of shared mobility services in North America. It finds that carsharing reduces personal vehicle ownership, with estimates of 9-13 vehicles removed for each carsharing vehicle. Carsharing users drive less and bike and transit use increases. The growth of bikesharing systems worldwide and in the US is also summarized, with over 1 million public bikeshares now worldwide. Surveys find bikesharing users tend to be wealthier, more educated, younger and drive less as a result.
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Environmental Impacts of Shared Mobility: Insights from North America
- 1. ENVIRONMENTAL IMPACTS OF SHARED MOBILITY: INSIGHTS FROM NORTH AMERICA Susan A. Shaheen, Ph.D. Adjunct Professor, Civil & Environmental Engineering and Co-‐Director, TSRC University of California, Berkeley
- 2. OVERVIEW Ø Carsharing Ø Market trends Ø N. American survey findings Ø Bikesharing Ø Market trends Ø N. American survey findings Ø Ridesourcing/TNCs Ø Market trends Ø San Francisco survey findings Ø Acknowledgements
- 3. © UC Berkeley,2015 Roundtrip Carsharing: Round trip, pay by the hour/mile, non-‐ profit and for profit fleet models Peer-‐to-‐Peer Carsharing: Shared use of private vehicle typically managed by third party One-‐Way Carsharing: Pay by the minute, point to point, fleet operated, street parking agreements Fractional Ownership Carsharing: Individuals sublease or subscribe to a vehicle owned by a third party There are many flavors of carsharing Carsharing
- 4. GROWTH OF WORLDWIDE CARSHARING 2006 2008 2010 2012 2014 Members 346,610 670,822 1,163,405 1,788,027 4,828,616 Vehicles 11,501 19,403 31,967 43,554 103,980 0 20,000 40,000 60,000 80,000 100,000 120,000 0 1,000,000 2,000,000 3,000,000 4,000,000 5,000,000 6,000,000 Vehicles Members © UC Berkeley, 2015 Shaheen and Cohen, 2015
- 5. NORTH AMERICAN LONGITUDINAL TRENDS 2006 2008 2010 2012 2014 Members 117,656 318,898 516,100 908,584 1,625,652 Vehicles 3,337 7,505 10,420 15,795 24,210 0 5,000 10,000 15,000 20,000 25,000 30,000 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 1,400,000 1,600,000 1,800,000 Vehicles Members Shaheen and Cohen, 2015 © UC Berkeley, 2015
- 6. © UC Berkeley,2015 Public Bikesharing: Point to point, pay by the ½ hr, fleet operated, docking stations Closed Community Bikesharing: Campuses and closed membership, mainly roundtrip, linking to carsharing Peer-‐to-‐Peer Bikesharing: Rent or borrow hourly or daily from individuals or bike rental shops Growing exponentially in urban centers Bikesharing
- 7. WORLDWIDE & US BIKESHARING: May 2015 Ø 880 cities with IT-‐based operating systems Ø 1,036,000 bikes Ø ~811,500 bikes in China (and 256 cities) Ø US: 72 cities with IT-‐based systems Ø ~24,700 bikes Ø 2,440 stations Ø In 2015, 21 new programs to begin operating in world: 13 are in China and 8 in U.S. Source: Russell Meddin, 2015
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- 10. N.A. VEHICLE HOLDINGS: KEY FINDINGS Ø Between 9 to 13 vehicles removed, including postponed purchase Ø 4 to 6 vehicles/carsharing vehicle sold due to carsharing Ø Most shift due to 1 car households becoming carless Ø Second largest shift, 2 car households become 1 car households Ø 25% sell a vehicle; 25% postpone purchase Ø Net CO2 reduction of 27% observed and 43% full impact (average) Martin, Shaheen, Lidicker, 2010
- 11. 2013 N. AMERICAN BIKESHARING STUDY Ø 23 operator interviews in the US, Canada, & Mexico Ø IT-‐based bikesharing program growth tracking (including planned programs) Ø Surveys with members of 5 public bikesharing systems in Spring/Summer 2013 Ø 2013 operational data analysis from Nice Ride MN © UC Berkeley, 2015 Shaheen et al., 2014
- 12. 2013 MEMBER SURVEY: DEMOGRAPHICS Compared to general population bikesharing users tend to be… Ø Wealthier Ø More educated Ø Younger Ø Caucasian Ø Male Shaheen et al., 2014 © UC Berkeley, 2015
- 13. MEMBER SURVEY: 2013 Shaheen et al., 2014 © UC Berkeley, 2015
- 14. CHANGE IN DRIVING 0% 0% 44% 9% 29% 4% 0%0% 0% 49% 6% 38% 0% 0% 0% 10% 20% 30% 40% 50% 60% Much more often More often Less often Much less often About the same Did not ride the drive before/after Changed driving, not due to bikesharing. As a result of my use of bikesharing, I drive a personal vehicle (e.g., car, SUV, etc.) … Minneapolis Saint-‐Paul, N = 620 Salt Lake City, N = 72 0% 0% 19% 10% 16% 3% 0%0% 0% 23% 12% 24% 4% 0% 0% 10% 20% 30% Much more often More often Less often Much less often About the same Did not ride the drive before/after Changed driving, not due to bikesharing. Montreal, N = 1095 Toronto, N = 993 1% 0% 32% 21% 19% 2% 0% 0% 5% 10% 15% 20% 25% 30% 35% Much more often More often Less often Much less often About the same Did not ride the drive before/after Changed driving, not due to bikesharing. Mexico City, N = 3329 Shaheen et al., 2014 © UC Berkeley, 2015
- 15. 10% 15% 27% 29% 13% 1% 5% 14% 18% 25% 24% 13% 0% 7% 0% 5% 10% 15% 20% 25% 30% 35% Less than once a month Once a month Every other week 1 to 3 days per week 4 to 6 days per week Once a day More than once a day Currently, how often do you check out a bikesharing bicycle? Minneapolis Saint-‐Paul, N = 618 Salt Lake City, N = 72 5% 3% 10% 30% 24% 7% 22%20% 7% 13% 23% 19% 4% 15% 0% 10% 20% 30% 40% Less than once a month Once a month Every other week 1 to 3 days per week 4 to 6 days per week Once a day More than once a day Montreal, N = 1090 Toronto, N = 1010 4% 6% 12% 30% 21% 7% 21% 0% 10% 20% 30% 40% Less than once a month Once a month Every other week 1 to 3 days per week 4 to 6 days per week Once a day More than once a day Mexico City, N = 3330 Shaheen et al., 2014 © UC Berkeley, 2015
- 16. CHANGE IN URBAN RAIL Shaheen et al., 2012 1% 10% 2% 0% 61% 3% 0%1% 13% 4% 3% 72% 0% 0% 0% 10% 20% 30% 40% 50% 60% 70% 80% Much more often More often Less often Much less often About the same Did not ride the rail before/after Changed rail use, not due to bikesharing. As a result of my use of bikesharing, I use urban rail… Minneapolis Saint-‐Paul, N = 620 Salt Lake City, N = 72 1% 6% 37% 20% 30% 1% 0%1% 7% 35% 14% 39% 2% 0% 0% 10% 20% 30% 40% 50% Much more often More often Less often Much less often About the same Did not ride the rail before/after Changed rail use, not due to bikesharing. Montreal, N = 1096 Toronto, N = 1005 4% 9% 12% 5% 44% 1% 0% 0% 10% 20% 30% 40% 50% Much more often More often Less often Much less often About the same Did not ride the rail before/after Changed rail use, not due to bikesharing. Mexico City, N = 3333 Shaheen et al., 2014 © UC Berkeley, 2015
- 17. Response Categories MontrealToronto Minneapolis-‐Saint Paul Salt Lake City Mexico City Lower cost and faster travel 25% 48% 0% 0% 28% Just lower cost 5% 9% 7% 0% 2% Too many connections (not have to transfer) 3% 2% 7% 0% 6% Just faster travel 14% 14% 14% 40% 12% Improve travel time reliability 4% 7% 0% 60% 6% Want to get exercise 31% 8% 50% 0% 17% Public transit vehicle is crowded 6% 6% 0% 0% 18% No space for my bike, which I use to connect 0% 0% 0% 0% 2% I consider it safer to travel with bikesharing 1% 0% 7% 0% 2% Not applicable 1% 2% 0% 0% 3% Other, please specify: 8% 5% 14% 0% 3% Total N 631 491 14 5 577 What is the primary reason that you are using the rail LESS because of bikesharing? Shaheen et al., 2014 © UC Berkeley, 2015
- 18. © UC Berkeley,2015 A new category of transportation services; need for study Ridesourcing Ridesourcing/TNC: Prearranged trips, App to pay and connect passengers with drivers who use their personal vehicles
- 19. SOME RIDESOURCING/E-‐HAIL MARKET TRENDS Ø Lyft: 60 cities; 51,000 drivers (2014) Ø Uber: 58 countries; 311 cities; over 162,000 drivers in U.S. Ø Sidecar: 10 cities; ~10,000 drivers Ø Flywheel: 6 cities, over 5,000 drivers Ø Curb: 60 cities; 35,000 cabs Said, 2015; Miller, 2015 © UC Berkeley, 2015
- 20. RIDESOURCING: SOME EARLY UNDERSTANDING Taxi, 39% Bus, 24% Rail, 9% Walk, 8% Drive Own Car, 6% Bike, 2% Get A Ride, 1% Other, 11% Ø 92% would have still made this trip Ø 8% induced travel effect Ø 33% would have taken public transit (bus or rail) Ø 4% named a transit station origin/destination, suggesting some ridesourcing usage to access public transportation Ø 20% avoided driving after drinkings Rayle et al. 2014 How would you have made this trip if Uber/Lyft/Sidecar were not available? © UC Berkeley, 2015
- 21. KEY FINDINGS: WAIT TIMES Rayle et al, 2014 About how long did you wait for your ride (from the time you made the request to the time the vehicle arrived)? Percentages of wait times less than or equal to 10 minutes: Wait Times Ridesourcing Taxi (Phone) Taxi (Street Hail) M-‐F 4am-‐6pm 93% 35% 39% M-‐F (6pm-‐4am) 92% 16% 33% S-‐Su 88% 16% 25% © UC Berkeley, 2015
- 22. KEY FINDINGS: VEHICLE OWNERSHIP, OCCUPANCY & DRIVING FREQUENCY Ø Ridesourcing and taxis serve residents who don’t own a car Ø Ridesourcing survey: 43% no vehicle at home Ø Taxi survey: 35% car-‐less Ø Occupancy: 1.8 TNCs and 1.1 taxis Ø Ridesourcing is still new, with potential to impact VMT/ VKT and vehicle ownership Ø 90% of vehicle owners did not change ownership level Ø 40% drove less since using ridesourcing Rayle et al. 2014
- 23. ACKNOWLEDGEMENTS Ø Mineta Transportation Institute, San Jose State University Ø California Department of Transportation Ø Adam Cohen, Elliot Martin, Nelson Chan, and Matt Christensen, TSRC, UC Berkeley Ø Special thanks to the worldwide shared mobility operators and experts who make our research possible, including Timothy Papandreou and Russell Meddin
- 24. www.tsrc.berkeley.edu Email: sshaheen@berkeley.edu Twitter: SusanShaheen1