Exploring equity implications of emerging transportation...

Exploring equity implications of emerging transportation technologies

Thursday, March 19, 20202:00-3:30 PM ET

TRANSPORTATION RESEARCH BOARD

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Purpose

Explore how agencies can define and address disparity as they relate to emerging technologies and new transportation modes

Learning ObjectivesAt the end of this webinar, you will be able to:• Describe potential equity implications of emerging transportation

technologies and modes• Identify equity measures and/or analysis methods that could inform

transportation planning and programming decisions

• The Hot Spot Analysis tool calculates the Getis-Ord Gi* statistic (GI-star) for each feature in a dataset. The resulting z-scores and p-values tell you where features with either high or low values cluster spatially. This tool works by looking at each feature within the context of neighboring features. A feature with a high value is interesting but may not be a statistically significant hot spot.

• To be a statistically significant hot spot, a feature will have a high value and be surrounded by other features with high values as well. The local sum for a feature and its neighbors is compared proportionally to the sum of all features. When the local sum is very different from the expected local sum, and when that difference is too large to be the result of random chance, a statistically significant z-score results.

2012

2017

2022

Source: U.S. Census Bureau, American Community Survey 5-Year Estimates

2012 - 2017

2017 - 2022

Source: U.S. Census Bureau, American Community Survey 5-Year Estimates

2012

2017

Source: U.S. Census Bureau, American Community Survey 5-Year Estimates

2012

2017

Source: U.S. Census Bureau, American Community Survey 5-Year Estimates

The Adoption of New Mobility Optionsamong Various Segments of the Population

March 19, 2020

1Director, 3 Revolutions Future Mobility Program2Graduate Student Researcher, 3 Revolutions Future Mobility ProgramInstitute of Transportation Studies, University of California, Davis

Dr. Giovanni Circella1, Grant Matson2

Twitter: @CircellaGLinkedIn: @giovannicircella

Email: gcircella@ucdavis.edu

Shared mobility, vehicle automation and electrification are bringing big changes in:

• Transportation supply

• Transportation demand

Need for rigorous research and impartial policy analysis to understand the impacts of these revolutions, and guide industry investments and government decision-making.

"People won’t have as many vehicles because they’ll share one

and own one."

Jim Hackett, Ford CEO

5

Future Mobility:

“Heaven” or “Hell” ?

✓Cars are all electric

✓Energy mix is clean

✓Increased capacity of transportation

✓Better livability in cities

✓Integration with public transit

✓Everybody shares intelligent vehicles

✓Increased congestion

✓Electricity produced with coal

✓Increased travel demand

✓More car-dependence of society

✓Reduced role of transit

✓“Ghost” vehicles traveling on streets

vs.

The future will largely be shaped by the policies that are developed today…

6

How are these transportation “revolutions” affecting vehicle ownership and travel behaviors among various population groups?

Uber/Lyft ridership is growing quickly…

7

2018 Ridership (estimates):

• Local bus 4.7 billion

• Urban rail 4.2 billion

• Taxi/TNC 3.8 billion

(Annual rate)

Source: The New Automobility: Lyft, Uber and the Future of American Cities, Schaller

Consulting, July 2018. Revised January 2019.

Note: TNC ridership continued to grow during 2019 and early 2020, before the sharp decline with the COVID-19 outbreak

From Bike Share to Shared Micromobility

8

Apps on the Phone

9

43.0%

32.0%

2.5%

1.1%

2.1%

0.6%

3.6%

5.4%

4.8%

1.1%

12.2%

54.6%

0% 10% 20% 30% 40% 50% 60%

Uber

Lyft

Zipcar

Turo

Getaround

Other Carsharing

JUMP

Lime

Bird

Other Bikesharing/Scooters

Airbnb

Amazon

Users’ Reported Use of Apps on Their Smartphones

Source: 2019 Data from the “The Pulse of the Nation on 3R” Data Collection, preliminary data, N=974, Cities included in the data collection: Boston, Los Angeles, Kansas City, Salt Lake City, San Francisco, Sacramento, Seattle, Washington DC

Apps on the Phone

10

43.0%

32.0%

2.5%

1.1%

2.1%

0.6%

3.6%

5.4%

4.8%

1.1%

12.2%

54.6%

0% 10% 20% 30% 40% 50% 60%

Uber

Lyft

Zipcar

Turo

Getaround

Other Carsharing

JUMP

Lime

Bird

Other Bikesharing/Scooters

Airbnb

Amazon

Users’ Reported Use of Apps on Their Smartphones

Source: 2019 Data from the “The Pulse of the Nation on 3R” Data Collection, preliminary data, N=974, Cities included in the data collection: Boston, Los Angeles, Kansas City, Salt Lake City, San Francisco, Sacramento, Seattle, Washington DC

California Panel Study of Emerging Transportation Trends

• 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, adoption of shared mobility and attitudes towards AVs

11

Timeline of the Project

12

2015

Opinion panel

Generation XMillennials

N = 2,400

2018

Opinion panel, paper survey

Baby Boomers (and older)Generation XMillennialsPost-Millennials

N = ~ 4,500(Version in Spanish is also offered)

2021

(same method…)

2018 2019 2020 2021

Annual updates…

2015-2018 Changes in the Use of Ridehailing by Income Group

0%

10%

20%

30%

40%

50%

60%

70%

Less than $25,000 $25,000 to$49,999

$50,000 to$74,999

$75,000 to$99,999

$100,000+

2015 2018

15%

24%

23%

27%

17%

13

Changes in the Use of Shared Mobility

Changes from 2015 to 2018:

• Sharp increase in ridehailing use in both low- and high-vehicle ownership households

• More frequent use observed among those in zero-vehicle households

• Shared ridehailing (e.g. UberPOOL) now a common presence in big cities

• Appearance of micromobility (dockless bikesharing and e-scooter sharing)

14

15

Latent-class adoption model to investigate differences in the use of ridehailing:

For more details:Alemi, F., G. Circella, S. L. Handy and P. L. Mokhtarian (2018) “Exploring the Latent Constructs behind the Use of Ridehailing in California”, Journal of Choice Modelling, 29, 47-62.

Adoption of Ridehailing among Various Groups of Users

Adoption of Micromobility: Shared E-scooters

Study on new mobility trends in rapidly growing cities in four southern US States

TOMNET-funded project with cooperation of ASU, Georgia Tech, UT Austin and USF

Atlanta, GAPhoenix, AZ Austin, TX Tampa, FL

16

Micromobility Users

17

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Users (n=77) non-users (n=892) All (n=969)

Work/study status

Neither a workernor a student

A student (part-time or full-time)

Both a worker anda student

A worker (part-time or full-time)

2019 Atlanta sample, N=969

E-scooter Trips – Primary Trip Purpose

18

10.1%

5.8%

20.3%

53.6%

10.1%

Work/school

Shopping/errands

Eating/drinking

Social/recreational

Just to enjoy the ride/trythe new service

2019 Atlanta sample, active users, N=77

E-scooter Trips – Impacts on Other Modes

19

14.5%

4.3% 1.4%

1.4%

15.9%

2.9%

52.2%

7.2%

Drive private vehicle, alone

Drive private vehicle, with others

Ride in private vehicle, with others

Take subway

Use Uber/Lyft

Use my own bike or scooter

Walk

I would not have made this trip

2019 Atlanta sample, active users, N=77

20

Propensity towards Ownership and Use of Automated Vehicles: Who Are the Adopters? Who the Non-adopters? Who Is Hesitant?

Who are the adopters?

21

Source: Le, T.V., Circella, G., & Matson, G. (2020) “Propensity towards Ownership and Use of Automated Vehicles: Who Are the Adopters? Who the Non-adopters? Who Is Hesitant?” Paper presented at the 2020 Transportation Research Board Meeting, Washington DC, January 2020.

2018 California Panel Survey DataN = 2336

Who are the adopters?

22

Source: Le, T.V., Circella, G., & Matson, G. (2020) “Propensity towards Ownership and Use of Automated Vehicles: Who Are the Adopters? Who the Non-adopters? Who Is Hesitant?” Paper presented at the 2020 Transportation Research Board Meeting, Washington DC, January 2020.

2018 California Panel Survey DataN = 2336

Who are the adopters?

23

Source: Le, T.V., Circella, G., & Matson, G. (2020) “Propensity towards Ownership and Use of Automated Vehicles: Who Are the Adopters? Who the Non-adopters? Who Is Hesitant?” Paper presented at the 2020 Transportation Research Board Meeting, Washington DC, January 2020.

2018 California Panel Survey DataN = 2336

Who are the adopters?

24

Source: Le, T.V., Circella, G., & Matson, G. (2020) “Propensity towards Ownership and Use of Automated Vehicles: Who Are the Adopters? Who the Non-adopters? Who Is Hesitant?” Paper presented at the 2020 Transportation Research Board Meeting, Washington DC, January 2020.

2018 California Panel Survey DataN = 2336

Privately-owned AVs? Potential Large Increase in Vehicles Miles Traveled

25For more details:Harb, M., Y. Xiao, G. Circella, P. L. Mokhtarian and J. Walker (2018) “Projecting Travelers into a World of Self-driving Vehicles: Estimating Travel Behavior Implications Via a Naturalistic Experiment”, Transportation, 45 (6), 1671–1685.

Re

sult

s fr

om

p

ilot

stu

dy

Don’t have to drive the car ✓Full multitasking ✓No parking worries ✓Can send on errands ✓

Policies to Contain Impacts of Vehicle Automation

Recommendations to support VMT and GHG containment goals include:

• Take advantage of opportunities to introduce pricing

• Deploy driverless vehicles as shared use vehicles, rather than privately owned

• Ensure widespread carpooling

• Deploy driverless vehicles with zero tailpipe emissions

• Increase line haul transit use rather than replacing it

• Ensure driverless vehicles are not larger or more energy consumptive

• Program vehicle behavior to improve livability, safety and comfort on surface streets

26

Source: Circella, G., C. Ganson and C. Rodier (2017) "Keeping Vehicle Use and Greenhouse Gas Emissions in Check in a Driverless Vehicle World." 3 Revolutions Policy Briefs. University

of California, Davis, April 2017; available at https://3rev.sf.ucdavis.edu/policy-brief/keeping-vehicle-use-and-greenhouse-gas-emissions-check-driverless-vehicle-world

Important Equity Considerations…

• Shared mobility options are getting rapidly adopted in many regions

• Use of pricing desirable to mitigate environmental impacts

• Promotion of pooling not easy in presence of low-density land uses

• Importance of considering impacts among rural communities and disadvantaged communities

27

Transit Cooperative Research Program (TCRP)

• TCRP B-47 Project: Impact of Transformational Technologies on Underserved Populations

28

https://apps.trb.org/cmsfeed/TRBNetProjectDisplay.asp?ProjectID=4686

Changes in Vehicle Ownership?

• Medium-term impacts on vehicle ownership are largely unclear

• Need for longitudinal analysis of changes in vehicle ownership associated with shared mobility adoption

• Mobility as a Service (MaaS) likely to affect future car ownership

29

Impacts of Disruptions?

• What happens to users that rely on these services in case of major disruptions?

30

https://3rev.ucdavis.edu/research-program

Acknowledgements

• Yongsung Lee

• Farzad Alemi

• Jai Malik

• Ali Etezady

32

• Pat Mokhtarian

• Susan Handy

• Dan Sperling

• Joan Walker

• Kate Tiedeman

• Tho Le

• Kailai Wang

• Rosaria Berliner

3 Revolutions Future Mobility Program Sponsors:

3 Revolutions Future Mobility Program Research and Data Partners:

• BAAQMD

• Faurecia

• PG&E

• BMW

• Ford

• Uber

• Caltrans

• Honda

• US EPA

• CARB

• Lyft

• Volvo Cars

• ClimateWorks

• NCST

• Daimler

• Nissan

• AvisBudget

• NREL

• California SGC

• NUMO Alliance

• CCJPA

• SACOG

• IFMO-BMW

• WRI

• Xiatian Wu

• Keita Makino

• Rosa Dominguez-Faus

• Mustapha Harb

Director, 3 Revolutions Future Mobility Program

Institute of Transportation Studies, University of California, Davis

Email: gcircella@ucdavis.edu | Twitter: @CircellaG

Dr. Giovanni Circella

Any questions? Please contact:

34

APPENDIX - How does the use of ridehailing affect the use of other modes?

…what replaces what?

Impacts on Other Travel Modes for Various Groups of Users

35

Latent-class analysis to investigate the impacts of ridehailing on other travel modes:

For more details:Circella, G. and F. Alemi (2018) “Transport Policy in the Era of Shared Mobility and Other Disruptive Transportation Technologies”, in Advances in Transport Policy and Planning, Volume 1, edited by Yoram Shiftanand Maria Kamargianni, Chapter 5, 119-144, Elsevier.

“Not all on-demand mobility services are created equal”…

36

Impact of ridehailing on use of other modes - “What Would You Have Done if Ridehailing Was Not Available?”

For more details:Circella, G., G. Matson, F. Alemi and S. L. Handy (2019) “Panel Study of Emerging Transportation Technologies and Trends in California: Phase 2 Data Collection”, Project Report, National Center for Sustainable Transportation. University of California, Davis, January 2019; available at https://escholarship.org/uc/item/35x894mg

28.6%

14.8%

7.3%

4.5%

0.7%

4.1%

27.9%

5.0%

7.0%

28.5%

16.2%

13.5%

7.5%

0.8%

7.5%

15.0%

4.9%

6.0%

0% 5% 10% 15% 20% 25% 30% 35%

Drive alone

Carpool

Public bus

Light rail/tram/subway

Commuter rail

Bike or walk

Taxi

Other

I would not have made this trip

Ridehailing Shared ridehailing(N=1,915)

Integrating Equity into Planning Through Target Setting

Stefanie Brodie, PhDSenior Researcher | Associate

Toole Design Group

March 2020

2

Defining Equity• Fair and just• Account for past inequality• Adjust for current disadvantages and needs• Allow overall improvement

• Minimum level of service for all• Minimize adverse effects• Distribute beneficial outcomes• Address historical impacts

Evaluating Equity

3

Performance-based Equity Evaluation

Brodie, S. and Amekudzi, A. “Performance-Based Methodology for Evaluating Equity for Transportation System Users.” 2017

4

Performance-Based Planning

FHWA. Performance Based Planning and Programming Guidebook.” 2014

5

Maximax

5

Maximize average benefit

Limit range between greatest and least impact

• Improves average impacts• Creates dynamic floor constraint• Distributes both the negative and positive

impacts of public investments

(Martens et al. 2012)

6

7

Geographic Distribution

8

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

Apr May Jun Jul Aug

Trip

s pe

r Pe

rson

Floor Constraint

Gap Limitation

Utilization

9

Measure Impact

Evaluate Equity

Today’s Participants

• Sunny Farmahan, Arkansas Department of Transportation, Sunny.Farmahan@ardot.gov

• Giovanni Circella, University of California, Davis, gcircella@ucdavis.edu

• Stefanie Brodie, Toole Design Group, sbrodie@tooledesign.com

• Elise Barrella, Wake Forest University, barrelem@wfu.edu

Panelists Presentations

http://onlinepubs.trb.org/onlinepubs/webinars/200319.pdf

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