The Future of Driving in the Land of Freeways

The Future of Driving in the Land of Freeways Susan Handy Center for Transportation Studies Annual Research Conference Minneapolis, MN November 2018 27,476 lane-miles of freeways 24.8 million licensed drivers 27.7 million cars & trucks 38.8 million residents http://www.politifact.com/punditfact/stateme nts/2014/jul/23/pat-buchanan/pat-buchananright-now-one-third-all-illegal-alien/ https://www.fhwa.dot.gov/policyinformation/statistics/abstracts/2014/state.cfm?loc=ca 1

https://www.thedailybeast.com/how-la-la-land-staged-adance-number-on-an-la-freeway 4 2

http://articles.latimes.com/2013/may/31/local /la-me-pc-carpool-lane-opening-20130531 http://confessions ofaprepper.com/su rviving-a-forestfire/ http://indianap ublicmedia.org/ eartheats/mass ive-droughtscomingcountrysummer-2060/ 3

Pacific Coast Highway, Big Sur, California, 2017 7 The California Experiment to reduce vehicle travel 8 4

Annual VMT per Capita in US (VMT = vehicle miles traveled) 12,000 10,000 8,000 6,000 4,000 2,000-1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 Source: Bureau of Transportation Statistics, U.S. Census Annual VMT per Capita CA vs US (VMT = vehicle miles traveled) 10,500 10,000 9,500 US 9,000 8,500 California 8,000 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Source: Bureau of Transportation Statistics, U.S. Census, CA Dept of Finance 5

AB32 The California Global Warming Solutions Act of 2006 80% reduction of GHG from 1990 levels by 2050 +SB 32 of 2016: 40% below 1990 by 2030 Million metric tons (CO 2 equivalent) 700 600 500 400 300 200 100 0 1990 2000 2004 2020 2050 http://www.arb.ca.gov/cc/scopingplan/meetings/070808/slides_julyspworkshops.pdf California Emission Sources (2008) AB32 Emission Reduction Strategies High GWP gases, 3% Industrial, 20% Other, 6% Transport, 37% High GWP Measures, 7% Cap and Trade, 16% Forestry, 4% Clean Cars and Standards, 27% Renewable Energy, 19% LCFS, 13% Utilities, 34% Energy Efficiency, 12% Smart Growth, 3% Source: CARB, California GHG Inventory for 2000-2008; Scoping Plan, 2020 Emissions Forecast 6

SB375 Sustainable Communities and Climate Protection Act of 2008 Targets for reducing per capita GHG emissions from cars and light trucks for metropolitan areas by reducing vehicle-miles-traveled (VMT) Examples 2020 2035 Sacramento -7% -16% San Francisco Bay Area -7% -15% Los Angeles region -8% -13% San Diego -7% -13% 13 Sustainable Communities Strategies (SCSs) 14 7

San Diego Example 2013 1999 - Conventional vision for 2020 2013 - Post-SB375 vision for 2050 How do we know what will work? 16 8

Research Contribution: Syntheses of Findings Impact of Transportation and Land-Use Related Policies on Greenhouse Gas Emissions Handy, Boarnet, with Tal, Lovejoy, Rodier, Circella, Spears, Shu, Weinreich for the California Air Resources Board, 2012-2014 The impacts from the studies that used California data [lead us to the] conclusion that doubling density is associated with VMT reductions that range from approximately 5 to 12 percent (an elasticity of -0.05 to -0.12). Google SB375 research 9

Research Contribution: Syntheses of Findings Volker and Fang Boarnet Research Contribution: Planning Tools Evaluation of Sketch-Level VMT Quantification Tools Handy, Fang, Lee for the California Strategic Growth Council, 2015-2017 12 tools initially identified 6 tools selected for application to case studies (in bold) Widely different estimates Pros and cons for each method California Emissions Estimator Model (CalEEMod) 2013 California Emissions Estimator Model (CalEEMod) 2016 California Smart Growth Trip Estimation Tool Envision Tomorrow / Envision Tomorrow+ GreenTrip Connect ITE Trip Generation for the Urban Context MXD Sketch 7 Watch it on YouTube! Urban Emissions Model (URBEMIS) https://ncst.ucdavis. UrbanFootprint edu/events/webinar- VMT+ quantifying-vehicle- miles-traveled/ VMT Impact Tool 20 10

Research Contribution: Planning Tools Quantification Methods for Estimating Greenhouse Gas and Air Pollutant Emission Reductions Handy, Kendall, Barbour, and Volker For the California Air Resources Board, 2017-2019 Quantification methods used to estimate GHG emissions for proposed land development and transportation projects for Greenhouse Gas Reduction Fund Programs. Reviews of methods for: - Infill development - Transit investments - Bicycle facilities - Pedestrian facilities - More to come Research Contribution: Before-and-After Studies Driving Reduction After the Introduction of Light Rail Transit Spears, Boarnet, Houston for the California Air Resources Board, 2011-2014 At 18 months after opening, daily mileage for households close to the line was approximately 30 percent lower than that of control households. This change appears to have resulted primarily from changes in car trip lengths. Average car trip length declined in each wave for the households within 1 km of new light rail stations, resulting in a difference of nearly four miles per trip 22 11

Research Contribution: Before-and-After Studies Measuring the Impact of Local Land Use Policies on Vehicle Miles of Travel Handy, Lovejoy, Salon, Mokhtarian, Sciara for UC Davis Sustainable Transportation Center, 2010-2011 92.9% 98.2% Vehicle-miles (per capita 100% monthly) 81.7% 81.6% 120 80% 100 96.8 60% 79.6 80 40% 60 40 20 0 20% 0% Year 1 Year 2 Downtown Davis Elsewhere in town*** Beyond Davis *** at Target (in town) Overall ** Significance for t-test of means (across years): * p < 0.10; ** p < 0.05; *** p < 0.01. It will take all of these strategies. It will take action at all levels of government. The benefits will go far beyond GHG reductions. 24 12

The future of driving in California and beyond 25 VMT in the future? The aggregate trends discussed do not allow us to forecast with any certainty the car use that we can expect in the future. Goodwin and Van Dender, 2013 12,000 10,000 8,000 6,000? 4,000 2,000-1936 1940 1944 1948 1952 1956 1960 1964 1968 1972 1976 1980 1984 1988 1992 1996 2000 2004 2008 2012 2016 13

Autonomous Vehicles Predictions of the past 14

Predictions of the past There s talk among tech insiders that it could be bigger than the PC. [Inventor Dean] Kamen says it will be to the car what the car was to the horse and buggy. - Wall Street Journal 9/27/10 http://blogs.w sj.com/digits/ 2010/09/27/fr om-hype-todisastersegwaystimeline/ Technology as a social construct Technology does not act as a kind of traffic policeman that is distinct in nature from the traffic it directs. Wiebe Bijker, The Social Construction of Technological Systems Technological development should be viewed as a social process, not an autonomous occurrence. Wiebe Bijker, Of Bicycles, Bakelites, and Bulbs 15

What do AVs mean for the future of driving? Point 1: It depends on what people decide to do with them How do we as individuals and households make choices about travel? How and why are these choices changing? How can we nudge them in the right direction? Nested choices Long-term Choices Lifestyle Residential Location Mid-term Choices Driver s license Auto ownership Short-term Choices Trip frequency Trip destination Mode choice 16

Choice process Set of choices available Qualities of choices available Value placed on different qualities Drive alone Shared ride Bus Rail Bicycle Walk Skateboard Cost Time Comfort Safety Knowledge, perceptions Cost vs. Time vs. Comfort vs. Safety Needs, constraints Changes in all cells Choice Sets Choice Qualities Value of Qualities Long-term Choices Mid-term Choices Short-term Choices 17

B Bike Sharing Neighborhood Electric Vehicles Mobility as a Service 18

Changes in all cells Choice Sets Choice Qualities Value of Qualities Long-term Choices Mid-term Choices Short-term Choices Research Contribution: Behavioral Research California Panel Study of Emerging Transportation Trends Led by Giovanni Circella for the ITS-Davis 3 Revolutions Future Mobility Program Statewide longitudinal study with rotating panel 2015 survey: Millennials (18-34) and Generation X (35-50) 2018 survey: All age groups Quota sampling by geographic region and neighborhood type Focus on changing lifestyles, travel behavior, adoption of shared mobility and propensity to use AVs 19

Adoption of Shared Mobility: 2015-2018 2015 - Bikesharing I use it 3 or more times a week I use it 1-2 times a week I use it 1-3 times a month I use it less than once a month I used it in the past, but not It's familiar but I ve never used it I am not familiar with it 0% 10% 20% 30% 40% 50% 60% 70% 2015 - Carsharing I use it 3 or more times a week I use it 1-2 times a week I use it 1-3 times a month I use it less than once a month I used it in the past, but not It's familiar but I ve never used it I am not familiar with it 0% 10% 20% 30% 40% 50% 60% 70% 2018 - Bikesharing I use it 3 or more times a week I use it 1-2 times a week I use it 1-3 times a month I use it less than once a month I used it in the past, but not anymore It's familiar but I ve never used it I am not familiar with it 0% 10% 20% 30% 40% 50% 60% 70% 2018 - Carsharing I use it 3 or more times a week I use it 1-2 times a week I use it 1-3 times a month I use it less than once a month I used it in the past, but not anymore It's familiar but I ve never used it I am not familiar with it 0% 10% 20% 30% 40% 50% 60% 70% Adoption of Shared Mobility: 2015-2018 2015 - Ridehailing I use it 3 or more times a I use it 1-2 times a week I use it 1-3 times a month I use it less than once a I used it in the past, but not It's familiar but I ve never I am not familiar with it 0% 20% 40% 60% 80% 2018 - Ridehailing I use it 3 or more times a week I use it 1-2 times a week I use it 1-3 times a month I use it less than once a month I used it in the past, but not anymore It's familiar but I ve never used it I am not familiar with it 0% 10% 20% 30% 40% 50% 60% 70% 2018 - Shared Ridehailing I use it 3 or more times a week I use it 1-2 times a week I use it 1-3 times a month I use it less than once a month I used it in the past, but not anymore It's familiar but I ve never used it I am not familiar with it 0% 10% 20% 30% 40% 50% 60% 70% 20

Changes in Use of Ridehailing by Region On average, the adoption and frequency of use of ridehailing almost doubled from 2015 to 2018: 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 74% 44% MTC 90% 85% 80% 70% 60% 52% 50% 40% 30% 30% 20% 16% 17% 8% 9% 10% 2% 0% Never used Never used Less than monthly basis Weekly basis or no longer once a month uses 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 71% 46% 15% 30% 17% 11% 3% 7% Never used Less than monthly basis Weekly basis or no longer once a month uses or no longer uses 9% SACOG 28% 15% 4% 2% 5% Less than monthly basis Weekly basis once a month 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% SANDAG 71% 46% SCAG 30% 15% 17% 11% 3% 7% Never used Less than monthly basis Weekly basis or no longer once a month uses Preliminary results; 2015 data: N=1975; 2018 data: N=2451, online sample only 2015 2018 41 Use of Ridehailing by Place Type Ridehailing is far lower in rural areas, regardless of region: 100% 80% 60% 40% 20% 0% 64% 75% 88% Urban Suburban Rural 100% 80% 60% 40% 72% 77% 20% 52% 0% Urban Suburban Rural 100% 80% 60% 40% 20% 0% 62% 71% 88% Urban Suburban Rural Uses at least once a month Uses less than once a month Never used or not longer use 42 21

Impact of Ridehailing on VMT 70% 60% 50% 40% 30% 20% 10% What Would You Have Done if Ridehailing Was Not Available? 0% Car Public bus Light rail/tram/subway Commuter rail Bike or walk Taxi I would not have made this trip (N=1,260) Attitudes Towards Autonomous Vehicles 80% 70% 60% 50% 40% 30% 20% 10% 0% Be one of the first people to buy a self-driving vehicle Gen Z (18-20 yrs old) Gen Y (21-37 yrs old) Gen X (38-53 yrs old) Baby Boomers (54-72 yrs old) Silent Generation and older (73-90 yrs old) Very unlikely Somewhat unlikely Neither unlikely nor likely Somewhat likely Very likely 22

Attitudes Towards Autonomous Vehicles Expectations about the Adoption of Autonomous Vehicles and Changes in Vehicle Ownership 0% 10% 20% 30% 40% 50% 60% Keep the vehicle(s) that I/my household owns (if any) and not use a driverless taxi or shuttle Get rid of one (or more) of my household vehicles and use a driverless taxi or shuttle. Very unlikely Somewhat unlikely Neither unlikely nor likely Somewhat likely Very likely Research Contribution: Behavioral Research Behavioral Experiment to Simulate Life with an Autonomous Vehicle Walker and Circella for the UC Institute of Transportation Studies program Results from pilot study 23

What do AVs mean for the future of driving? Point 1: It depends on what people decide to do with them Point 2: It depends on what society decides to do about them Set of choices available Qualities of choices available Value placed on different qualities Things policy can influence Minnesota examples 48 24

What do AVs mean for the future of driving? Point 1: It depends on what people decide to do with them Point 2: It depends on what society decide to do about them Set of choices available Qualities of choices available Value placed on different qualities Knowledge, perceptions Policy can also influence these Minnesota examples http://www.minneapolismn.gov/publicworks/saferoutes/wcms1p-132920 50 25

Research Contribution: Before-and-After Studies Impact of Bike Share on Bicycling Behavior and Attitudes: Assessment of the Sacramento Region Bike Share System Fitch and Handy for Caltrans and the National Center for Sustainable Transportation Before-and-after household survey Panel survey of Jump bike users How do people use Jump bike? What impact does Jump bike have on bicycling in general? How important is electric assist? 51 Research Contribution: Policy Possibilities Opportunities for Shared-Use Mobility Services in Rural Disadvantaged Communities in California s San Joaquin Valley: Existing Conditions and Conceptual Program Development Rodier for the National Center for Sustainable Transportation In this study, the cost-effectiveness of existing inter-city transit service in rural disadvantaged communities in the San Joaquin Valley (California) is compared to hypothetical ridesharing and carsharing services. The results show significant potential to reduce transit costs and reinvest those cost saving to expand shared mobility services. 52 26

Research Contribution: Policy Possibilities Active Transportation in and Era of Sharing, Electrification and Automation Handy and others for the 3 Revolutions Initiative at UC Davis To maximize the societal benefits of the three revolutions, policies should prioritize human mobility and community livability over vehicle mobility. Communities should be designed for people, not vehicles; AVs should serve the community, rather than the community serving AVs. Policies with respect to Built environment Automated vehicles Planning processes and practices At all levels of government Research Contribution: Simulations Automated Vehicle Scenarios: Simulation of System-Level Travel Effects Using Agent- Based Demand and Supply Models in the San Francisco Bay Area Rodier for the National Center for Sustainable Transportation 27

Research Contribution: Policy Possibilities Three Revolutions in Urban Transportation Fulton, Mason, Meroux for the ClimateWorks Foundation, Hewlett Foundation, Barr Foundation The 3R scenario builds on the 2R scenario, with policy support for both electrification and automation, but also substantial policy support for shared-use mobility and urban planning that supports shorter trip lengths and high levels of walking, cycling, and public transport use, even in a future where vehicular travel is significantly less expensive. Without strong policy support for compact cities, even a scenario with fairly high levels of vehicle sharing in smaller vehicles could result in significantly higher vehicle kilometers and lower levels of access. 55 Lew Fulton, UC Davis Institute of Transportation Studies and the Institute for Transportation & Development Policy https://steps.ucdavis.edu /threerevolutionslandingpage/ 28

https://3rev.ucdavis.edu 57 AVs and other technological innovations - a threat and an opportunity for communities How do we as a society push their development and their use toward the desired outcome toward the community vision? Going Driverless 29

Thanks! Questions? slhandy@ucdavis.edu 30