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Barriers to “New Mobility”: A Community-Informed Approach to Smart Cities Technology

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Aaron Golub, Portland State University; Vivian Satterfield, Verde

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Barriers to “New Mobility”: A Community-Informed Approach to Smart Cities Technology

  1. 1. Collaborative Research- OPAL, Forth and PSU with support from the 11th-Hour Project, the City of Portland, and the National Institute for Transportation and Communities at PSU Community-based Assessment ofTransportation Needs to inform City of Portland Smart Cities Plan Aaron Golub, Toulan School of Urban Studies and Planning, Portland State University Vivian Satterfield, Director of Strategic Partnerships at Verde (formerly of OPAL Environmental Justice Oregon) Michael Serritella, Portland Bureau of Transportation Jai Singh, Organizer at APANO (formerly of OPAL Environmental Justice Oregon)
  2. 2. Smart mobility Transportation services, operations and/or traveler information systems assisted by fast, real-time, wireless, distributed data streams Autonomous, electric, connected and shared mobility technologies Almost always require traveler to have internet connectivity and direct linking to bank or credit card accounts Early/current examples: Bikeshare/e-scooters, ridesourcing (Uber/Lyft, etc), transit information apps, smart transit fare payment systems
  3. 3. https://www.portlandoregon.gov/transportation/69999 Portland Smart Cities UB Mobile PDX proposal to DOT
  4. 4. As public transportation providers and agencies move to payment, information, trip-planning and last/first-mile connectivity systems which require travelers to have access to private internet and banking services What steps can be taken to ensure that the coming wave of “smart” transportation innovations will benefit all groups equitably? Or even better - achieve transportation justice?
  5. 5. Existing research on these issues • FDIC 2015 survey on banking access: ~20% underbanked, 7% unbanked • Much higher for low-income and POC • Low incomes, fees and trust/privacy issues are primary causes • Digital divide issues are widely noted in existing research on smart mobility (see reference list on last frame of this presentation) • For example – work done here at PSU on equitable access to bikeshare found: • Lack of diverse payment methods and wifi/internet access a barrier to use • Recommended increasing payment options, improve connectivity with transit systems, and more robust outreach and educational programs
  6. 6. Research Questions ● How can smart mobility technologies address the current and future needs of transportation disadvantaged communities? ● What are the barriers to using smart mobility technologies experienced by different communities? ● What potential solutions show the most promise in overcoming these barriers?
  7. 7. Transportation disadvantaged communities • Low-income • Communities of color • Mobility challenged • Age • English proficiency
  8. 8. Transportation disadvantaged communities • Low-income • Communities of color • Mobility challenged • Age • English proficiency Focus on East Portland
  9. 9. Source: Metro 2018 regional transportation plan - Appendix E – Transportation Equity Evaluation
  10. 10. Source: Metro, Regional Snapshot Displacement of People of Color
  11. 11. Methods Two focus groups Bus Riders Unite Lower-income East Portland residents Larger sample survey Online and in-person at several community events and intercepts on buses and transit stops August - October of 2017 308 survey responses Representing a racially, socio-economically diverse group of individuals 46% of survey respondents identifying themselves as people of color 55% reporting lower than the study area’s median income; solid distribution of ages
  12. 12. Survey sample
  13. 13. Study Area vs Survey responses
  14. 14. Income comparison Study area Survey sample Median household income $43,700 ~$35,000 Poverty rate 23.7% ~32% Source: 5-YR American Community Survey
  15. 15. Age profiles for the study area and survey sample
  16. 16. Race/ethnicity profiles for the study area and survey sample
  17. 17. Analysis methodology • Analyze focus group and survey data for overall results • Compare survey responses between: • Non-Hispanic White and respondents of color • Low-income and high-income • Baby Boomers, Gen-X and Millennials (see report) • Do responses differ? • Differences tested for statistical significance
  18. 18. Sample breakdown: Low-income = <$50k (~70% of AMI)
  19. 19. Results
  20. 20. Basic transportation issues https://pamplinmedia.com/go/42-news/368510-250722-trimet-to-decriminalize-fare-evasion
  21. 21. Transportation access Overall Income Race/ Ethnicity How many cars, trucks, vans, or motorcycles are available in your household for you to use? [Multiple choice] Almost 30% had no vehicle; 40% had one vehicle Higher more ND Does your employer / school provide you a transit pass? [Y/N] 28% Higher more ND Does your employer / school provide free parking? [Y/N] 23% Higher more ND Does your employer / school provide secure onsite bicycle parking? [Y/N] 26% Higher more ND Does your employer / school provide you a Biketown subscription? [Y/N] 4% ND ND
  22. 22. Transportation access Overall Income Race/ Ethnicity How many cars, trucks, vans, or motorcycles are available in your household for you to use? [Multiple choice] Almost 30% had no vehicle; 40% had one vehicle Higher more ND Does your employer / school provide you a transit pass? [Y/N] 28% Higher more ND Does your employer / school provide free parking? [Y/N] 23% Higher more ND Does your employer / school provide secure onsite bicycle parking? [Y/N] 26% Higher more ND Does your employer / school provide you a Biketown subscription? [Y/N] 4% ND ND Higher income – more access to support for commuting
  23. 23. Commuting behavior Overall Income Race/ Ethnicity The most common mode of travel to work: Drive alone [Y/N] 27.5% Higher more ND The most common mode of travel to work: Carpool [Y/N] 5% Lower more ND The most common mode of travel to work: Public transportation [Y/N] 36% Lower more POC more The most common mode of travel to work: Walked [Y/N] 12% Lower more ND The most common mode of travel to work: Bicycle [Y/N] 23% ND ND The most common mode of travel to work: Ridesourcing (TNCs) [Y/N] 2.3% Lower more (4.1 vs 0%) ND The most common mode of travel to work: Work at home [Y/N] 6% Higher more NHW more
  24. 24. Commuting behavior Overall Income Race/ Ethnicity The most common mode of travel to work: Drive alone [Y/N] 27.5% Higher more ND The most common mode of travel to work: Carpool [Y/N] 5% Lower more ND The most common mode of travel to work: Public transportation [Y/N] 36% Lower more POC more The most common mode of travel to work: Walked [Y/N] 12% Lower more ND The most common mode of travel to work: Bicycle [Y/N] 23% ND ND The most common mode of travel to work: Ridesourcing (TNCs) [Y/N] 2.3% Lower more (4.1 vs 0%) ND The most common mode of travel to work: Work at home [Y/N] 6% Higher more NHW more Lower income – more multi-modal and users of ridesourcing (TNC) for commuting
  25. 25. Paying for transit Overall Income Race/ Ethnicity How do you typically pay for the TriMet fare: On board [Y/N] 42% Lower more (51 vs 33%) POC more (49 vs 37%) How do you typically pay for the TriMet fare: TriMet or retail store [Y/N] 10% ND ND How do you typically pay for the TriMet fare: School or Work [Y/N] 15% Higher more ND How do you typically pay for the TriMet fare: Online or Phone App [Y/N] 35% Higher more (42 vs 32%) NHW more (41 vs 31%) How do you typically pay for the TriMet fare: Social service agency [Y/N] 3% Low more ND
  26. 26. Paying for transit Overall Income Race/ Ethnicity How do you typically pay for the TriMet fare: On board [Y/N] 42% Lower more (51 vs 33%) POC more (49 vs 37%) How do you typically pay for the TriMet fare: TriMet or retail store [Y/N] 10% ND ND How do you typically pay for the TriMet fare: School or Work [Y/N] 15% Higher more ND How do you typically pay for the TriMet fare: Online or Phone App [Y/N] 35% Higher more (42 vs 32%) NHW more (41 vs 31%) How do you typically pay for the TriMet fare: Social service agency [Y/N] 3% Low more ND Lower income and POC – Less use of online/phone apps and higher use of on- board (cash) payment
  27. 27. Barriers to Access: Documentation, Data and Banking
  28. 28. Access to data and internet Overall Income Race/ Ethnicity How frequently do you use email and/or the internet? [Frequency, times per month] 88.8 Higher more (96 vs 84) NHW more (93 vs 85) At your home, do you have access to the Internet? [Y/N] 92% Higher more (98 vs 88%) NHW more (97 vs 87%) If you work, at your workplace, do you have access to the Internet? [Y/N] 79% (out of 84% who work) Higher more (99 vs 87% of those who worked outside the home) ND Is your cell phone a smartphone? [Y/N] 89% ND POC more (91 vs 89) If you have a cell phone, how frequently do you use public Wi-Fi in order to reduce your data use? [Multiple Choice] 65% connect to Wi-Fi whenever possible or occasionally Lower more (72 vs 63%) ND Have you ever had to cancel your cell phone service for a period of time because of cost? [Y/N] 25% Lower more (35 vs 12%) POC more (33 vs 18%)
  29. 29. Access to data and internet Overall Income Race/ Ethnicity How frequently do you use email and/or the internet? [Frequency, times per month] 88.8 Higher more (96 vs 84) NHW more (93 vs 85) At your home, do you have access to the Internet? [Y/N] 92% Higher more (98 vs 88%) NHW more (97 vs 87%) If you work, at your workplace, do you have access to the Internet? [Y/N] 79% (out of 84% who work) Higher more (99 vs 87% of those who worked outside the home) ND Is your cell phone a smartphone? [Y/N] 89% ND POC more (91 vs 89) If you have a cell phone, how frequently do you use public Wi-Fi in order to reduce your data use? [Multiple Choice] 65% connect to Wi-Fi whenever possible or occasionally Lower more (72 vs 63%) ND Have you ever had to cancel your cell phone service for a period of time because of cost? [Y/N] 25% Lower more (35 vs 12%) POC more (33 vs 18%) Internet and data access fairly high for everyone Lower income and POC – Lower use of internet and access to cell/data/internet
  30. 30. Access to banking and credit Overall Income Race/ Ethnicity Do you have a credit card or prepaid card account? [Y/N] 72% Higher more (90 vs 60%) NHW more (79 vs 64%) Do you have a checking or savings account? [Y/N] 90% Higher more (98 vs 85%) NHW more (95 vs 84%) How comfortable are you in linking your bank account or credit card to transportation apps on your phone? [Likert] 3.3 Higher more (3.7 vs 3.1) NHW more (3.6 vs 3) Do you have a driver’s license? [Y/N] 80% licensed Higher more (95 vs 70%) NHW more (89 vs 67%)
  31. 31. Access to banking and credit Overall Income Race/ Ethnicity Do you have a credit card or prepaid card account? [Y/N] 72% Higher more (90 vs 60%) NHW more (79 vs 64%) Do you have a checking or savings account? [Y/N] 90% Higher more (98 vs 85%) NHW more (95 vs 84%) How comfortable are you in linking your bank account or credit card to transportation apps on your phone? [Likert] 3.3 Higher more (3.7 vs 3.1) NHW more (3.6 vs 3) Do you have a driver’s license? [Y/N] 80% licensed Higher more (95 vs 70%) NHW more (89 vs 67%) Clear banking / credit access issues
  32. 32. Familiarity and comfort with smart mobility technologies https://www.theverge.com/2018/1/12/16880978/gm-autonomous-car-2019-detroit-auto-show-2018
  33. 33. Impressions of new transportation technologies Overall Income Race/ Ethnicity How familiar are you with electric cars? [Likert] 3.3 Higher more NHW more How interested are you in owning an electric car? [Likert] 3.5 ND ND How familiar are you with autonomous vehicles? [Likert] 2.7 Higher more NHW more How comfortable would you be riding in an autonomous vehicle? [Likert] 2.6 Higher more ND
  34. 34. Impressions of new transportation technologies Overall Income Race/ Ethnicity How familiar are you with electric cars? [Likert] 3.3 Higher more NHW more How interested are you in owning an electric car? [Likert] 3.5 ND ND How familiar are you with autonomous vehicles? [Likert] 2.7 Higher more NHW more How comfortable would you be riding in an autonomous vehicle? [Likert] 2.6 Higher more ND Lower income and POC – Less familiar/comfortable with new vehicle technologies
  35. 35. Smart mobility applications Overall Income Race/ Ethnicity If you have a smartphone, how often do you use your phone to get public transportation information? [Frequency, Days per Month] 13.4 Lower more POC more If you have a smartphone, how often do you use your phone for navigation? [Frequency, Days per Month] 15.8 ND ND If you have a smartphone, how often do you use your phone to reserve a ridesourcing or carsharing service? [Frequency, Days per Month] 2.2 Lower more ND If you have a smartphone, how often do you use your phone to use bikesharing? [Frequency, Days per Month] 1.2 ND ND
  36. 36. Smart mobility applications Overall Income Race/ Ethnicity If you have a smartphone, how often do you use your phone to get public transportation information? [Frequency, Days per Month] 13.4 Lower more POC more If you have a smartphone, how often do you use your phone for navigation? [Frequency, Days per Month] 15.8 ND ND If you have a smartphone, how often do you use your phone to reserve a ridesourcing or carsharing service? [Frequency, Days per Month] 2.2 Lower more ND If you have a smartphone, how often do you use your phone to use bikesharing? [Frequency, Days per Month] 1.2 ND ND Lower income and POC – More use of existing smart mobility applications
  37. 37. Language “I like the [transit] screens in downtown. I can read a little bit of English , but the time I was lost, I had to ask because the instructions were only in English. So not everyone can understand. In a situation that is unexpected like that, I don’t know what the screen or the conductor is saying then it’s frustrating. It makes you fearful…” Many smartphone apps, transit signage, etc. are not available in languages other than English
  38. 38. Trust / privacy / security “I do have a bank account, but am afraidTriMet will use it and share it.” “I don’t have any information on my phone. I am afraid people will hack my phone. I would rather pay cash.” Identity theft could be devastating to a low-income person – these issues are likely to be underappreciated by middle-class planners
  39. 39. Recommended Policies – Ranked #1 Improve real time communication between buses and riders about crowding, arrival time, etc. #2 Public wifi and charging stations for smartphone/mobile technology #3 Rebates or financing to help buy clean electric vehicles #4 Smartphone apps for transportation services translated to languages other than English #5Autonomous neighborhood shuttles and micro-transit
  40. 40. Punchlines • Smart mobility technologies could potentially address many of the needs of transportation disadvantaged communities • Designed to facilitate multi-modal travel • Respondents of color were more likely to own a smartphone than their counterparts • More regular users of currently available smart mobility applications • Significant barriers exist which prevent smart mobility technologies from benefiting all communities • Un- and under-banked / Heavy reliance on cash • Lower access to data and internet • Language / translation
  41. 41. Thanks to our survey respondents and focus group participants…
  42. 42. Questions?
  43. 43. Additional resources • Papangelis, K., Velaga, N., Ashmore, F., Sripada, S., Nelson, J., & Beecroft, M. (2016). Exploring the rural passenger experience, information needs and decision making during public transport disruption. Research in Transportation Business & Management, 18, 57-69. • Alessandrini, A., Campagna, A., Site, P. D., Filippi, F., & Persia, L. (2015). Automated Vehicles and the Rethinking of Mobility and Cities. Transportation Research Procedia, 5, 145-160. • Rode, P., Floater, G., Thomopoulos, N., Docherty, J., Schwinger, P., Mahendra, A., & Fang, W. (2017). Accessibility in Cities: Transport and Urban Form. Disrupting Mobility, 239-273. • Velaga, N., Beecroft, M., Nelson, J., Corsar, D., & Edwards, P. (2012). Transport poverty meets the digital divide: accessibility and connectivity in rural communities. Journal of Transport Geography, 21, 102-112. • Acheampong, R.A., Thomoupolos, N., Marten, K., Beyazit, E., Cugurullo, F., & Dusparic, I. (2018). Literature Review on the Social Challenges of Autonomous Transport. Short Term Scientific Mission Report for COST Action CA16222 “Wider Impacts and Scenario Evaluation of Autonomous and Connected Transport (WISE-ACT). • Gruel, W. & Stanford, J. (2016). Assessing the Long-Term Effects of Autonomous Vehicles: A Speculative Approach. Transportation Research Procedia, 13, 18-29. • Grush, B. & Niles, J. (2017). Transit Leap: A Deployment Path for Shared-Use Autonomous Vehicles that Supports Sustainability. Disrupting Mobility, 291- 305. • Hörl, S., Ciari, F., & Axhausen, K. (2016). Recent Perspectives on the Impact of Autonomous Vehicles. Institute for Transportation Planning and System, 1-37. • Litman, T. A. (2017B). Autonomous Vehicle Implementation Predictions: Implications for Transport Planning. Victoria Transport Policy Institute, 1-23. • McNeil, N., Dill, J., MacArther, J., Broach, J., & Howland, S. (2017). Breaking Barriers to Bike Share: Insights on Equity. Transportation Research and Education Center (TREC). 1-20.. • Brakewood, Candace, and George Kocur. "Unbanked Transit Riders and Open Payment Fare Collection." Transportation Research Record: Journal of the Transportation Research Board 2351 (2013): 133-141.

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