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Suggested Citation:"6. Conclusion." National Academies of Sciences, Engineering, and Medicine. 2019. Transportation Network Companies (TNCs): Impacts to Airport Revenues and Operations. Washington, DC: The National Academies Press. doi: 10.17226/25671.
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Suggested Citation:"6. Conclusion." National Academies of Sciences, Engineering, and Medicine. 2019. Transportation Network Companies (TNCs): Impacts to Airport Revenues and Operations. Washington, DC: The National Academies Press. doi: 10.17226/25671.
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Suggested Citation:"6. Conclusion." National Academies of Sciences, Engineering, and Medicine. 2019. Transportation Network Companies (TNCs): Impacts to Airport Revenues and Operations. Washington, DC: The National Academies Press. doi: 10.17226/25671.
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ACRP 01-35: TNCS: IMPACTS TO AIRPORT REVENUES AND OPERATIONS AUGUST 19, 2019 FINAL DRAFT DELIVERABLE Reference Guide | 6-1 | Conclusion 6. CONCLUSION Commercial GT operations will continue to evolve in response to: increasing passenger enplanements; the need to maintain or expand adequate nonaeronautical revenue; new federal and state regulations; emerging technologies; evolving business models; and enhancements to airport infrastructure. The following topics are among those that airport operators will need to monitor, and they could also be areas for future ACRP research, webinars, or presentations at the Annual Meeting.  Regulations, Taxes, and Fees Enabling legislation continues to be introduced to respond to specific issues. Proposed legislation introduced at the national level would require ride-hailing companies to give riders more information about the driver picking them up. Senate bill S. 1871 and House bill H.R. 3262 would require states to improve safety guidelines for ride-hailing companies by mandating that the drivers display the following: a front and rear license plate; a scannable quick response (QR) or similar code on the passenger windows for riders to verify they are entering the correct authorized vehicle; and illuminated signs that are visible during both day and night and readable from 50 feet. The federal legislation, titled “Stop, Ask, Match, Inform,” would impose a 1 percent reduction in federal highway funding for states failing to enact conforming state-level laws. At the state level, a bill recently introduced in the Massachusetts House would require TNC drivers to undergo fingerprinting as part of the state's criminal background checks. Massachusetts currently requires a two-step screening. TNCs are first required to perform multi-state criminal and driving background checks, as well as a check of a national sex offender database. Drivers who pass are then referred to the state for checks of criminal histories, including crimes such as violent felonies, serious driving offenses, or sex abuse convictions. A driver cannot operate in Massachusetts until they clear the secondary check. And in New Jersey, after the March 2019 death of a university student who got into the car of an Uber impersonator, the governor signed legislation in June requiring TNC drivers to show additional vehicle and personal verification. Lyft has started continuous background checks and has enhanced identity verification. Continuous criminal background checks will monitor drivers daily and will immediately notify Lyft of “any disqualifying criminal convictions.”52 Active drivers who do not pass an annual screening—which includes a Social Security number trace, a nationwide criminal search, a county court records search, a federal criminal search, and a U.S. Department of Justice 50-state sex offender registry search—in addition to continuous screenings will be barred from the platform. Additionally, cities and states continue to review and update taxes and fees imposed on TNCs. Their challenge is to balance long-term mobility policy goals with revenue objectives. According to the Eno Foundation, in 2019 “the state of New York adopted new surcharges on TNC and taxi trips in the busiest areas of Manhattan, while in Washington State, efforts to apply the taxi sales tax to TNCs failed. Georgia lawmakers proposed a TNC-trip fee as part of a regional transit bill. Philadelphia officials called for switching its per-trip percentage assessment to a $0.50 surcharge in order to generate more revenue.”53  Financial Trends and Impacts Airport operators will continue to track TNC revenue trends, as well as rental car transactions and parking revenue. Adjusting trip fees, using dynamic pricing strategies, and considering other methods to differentiate 52 Lyft Blog, “Lyft’s Commitment to Safety,” April 15, 2019, https://blog.lyft.com/posts/2019/4/14/lyfts-commitment-to-safety (accessed August 12, 2019). 53 Kim, So Jung and Robert Puentes, “Eno Brief: Taxing New Mobility Services: What’s Right? What’s Next?” https://www.enotrans.org/etl material/eno-brief-taxing- new-mobility-services-whats-right-whats-next/ (accessed August 2, 2019).

ACRP 01-35: TNCS: IMPACTS TO AIRPORT REVENUES AND OPERATIONS AUGUST 19, 2019 FINAL DRAFT DELIVERABLE Reference Guide | 6-2 | Conclusion products will be essential to maintaining adequate nonaeronautical revenue. Airport operators should continue to benchmark their airports against comparable airports; they should also strive to have increases in nonaeronautical revenue per enplaned passenger keep pace with the rate of inflation.  Business Models With the recent IPOs by Lyft and Uber, TNC financials will be more readily available for scrutiny by financial analysts and investors. Moreover, the employment model used by TNCs (i.e., independent contractors) will continue to be examined. For example, California is adjusting to last year’s state Supreme Court ruling called Dynamex that makes it harder for companies to claim workers are independent contractors. Pending legislation could force companies to change how they classify their workers. Assembly Bill 5 was passed at the end of May 2019, and it would codify Dynamex, extending its reach beyond wage issues to other labor code matters and exempting some professions; the bill still needs to pass the Senate. Lawmakers, companies, and unions are now considering how that would work—with many enterprises, such as TNCs, seeking exemptions.54 “Uber and Lyft . . . said that their business models, as well as drivers’ stated preferences, rely on flexibility, which they said would be hard to achieve while also meeting requirements such as mandated meal/rest breaks and overtime. Both said they’d likely need to insist that drivers work for only one service, and limit how many drivers work at a time, two changes that would curb drivers’ earnings potential.”55 In Massachusetts, Senate Bill 1090 would establish collective bargaining rights for TNC drivers; a companion bill, S. 2289, includes requirements related to data sharing, accommodating riders with disabilities, passenger security, and fines for violations. In July 2019, Uber implemented layoffs as part of cost-driven changes, letting go about a third of its 1,200 employees in the marketing department. The effort to slash costs came in the wake of reported first quarter 2019 losses of $1 billion; second quarter loses were reported at $5.2 billion. And Lyft has raised prices on routes in several cities touting “…their upcoming pricing algorithms, which they hinted might be able to more precisely predict what riders might be willing to pay for a ride. Lyft said those pricing changes would boost revenue per rider by next quarter.”56  Technology As new technologies continue to promote the growth of mobility-on-demand services, effectively managing access to an airport’s roads and curbs will remain a critical concern. To prepare for the future, many airport operators are installing GT management systems that allow them to track app-based mobility-on-demand service providers as they travel throughout the airport premises. Systems have been installed at airports that use a Web-API interface to monitor and collect information on TNC trips to and from the airport. Collected information includes the TNC ID, driver ID, trip ID, location, timestamp, type of event (e.g., airport entry, pick- up, drop-off, airport exit), and the number of passengers (as reported by the driver). Airport operators can use these systems to their benefit by adopting policies and integrating systems that charge ride-hailing companies for their time spent on the airport’s premises or by charging fees based on the number of passengers in the vehicle. This technology can also serve to support airport-wide access fee initiatives. 54 Scheiber, Noam, “Debate Over Uber and Lyft Drivers’ Rights in California Has Split Labor,” New York Times, https://www.nytimes.com/2019/06/29/business/economy/uber-lyft-drivers-unions.html (accessed June 29, 2019). 55 Said, C. “Deliv switching California couriers to employees — ‘start of a wave’ ” San Francisco Chronicle, June 22, 2019, https://www.sfchronicle.com/business/article/Deliv-switching-California-couriers-to-employees-14029663.php (accessed June 27, 2019). 56 Marshall, A., “Uber and Lyft Suggest the Days of Cheapo Rides Could be Over,” Wired, August 8, 2019, https://www.wired.com/story/uber-lyft-suggest-cheap- rides-could-be-over/ ?bxid=5cc9e14efc942d13eb2015c5&cndid=42801799&esrc=Wired_etl_load&source=EDT_WIR_NEWSLETTER_0_DAILY_ZZ&utm_brand=wired&utm_campaign= aud-dev&utm_mailing=WIR_Daily_080919&utm_medium=email&utm_source=nl&utm_term=WIR_Daily (accessed August 12, 2019).

ACRP 01-35: TNCS: IMPACTS TO AIRPORT REVENUES AND OPERATIONS AUGUST 19, 2019 FINAL DRAFT DELIVERABLE Reference Guide | 6-3 | Conclusion Uber’s IPO filed with the Securities and Exchange Commission (SEC) explains the resources that Uber is deploying to support its platforms. Its Advanced Technology Group (using tools such as artificial intelligence and machine learning) is working on demand prediction, matching and dispatching, pricing strategies, and autonomous vehicles. Uber builds proprietary systems for the following57: — Marketplace technologies: These technologies comprise a real-time algorithmic decision engine that matches supply and demand for Uber’s Personal Mobility, Uber Eats, and Uber Freight offerings. — Demand prediction: This is a proprietary demand prediction engine that uses data to predict when and where peak ride and meal order volume will occur, allowing the company to manage supply and demand in a city efficiently. — Matching and dispatching: Proprietary matching and dispatching algorithms generate more than 30 million match pair predictions per minute. — Pricing: Uber’s technology sets product pricing in real-time at a local level. In areas and times of high demand, it deploys dynamic pricing to help restore balance between driver supply and consumer demand. Dynamic pricing helps to balance demand during the busiest times so that a reliable ride is always within reach. Similarly, Lyft’s SEC filing notes its intention to continue to invest in technology related to mapping, routing, payments, in-app navigation, and matching technologies. As the filling states, these are keys to integrating technology and leveraging data science in Lyft’s platform in order to increase efficiency and improve safety. In addition, Lyft is investing in autonomous technology, which it believes will be a critical part of the future of transportation.58  Industry Associations Both ACI and AAAE have provided leadership by convening working groups and sponsoring activities to help airport operators understand TNC impacts and share ideas and approaches. Regular conference calls, webinars, focused research (e.g., wayfinding standards), and panel discussions at annual meetings and specialty conferences have all contributed to expanding the knowledge base available to airport operators. Both organizations should continue their roles in disseminating information on a timely basis, as well as providing forums for discussion and interaction between airport operators and TNCs.  Airport or Passenger Surveys Regular ground access surveys are essential tools for establishing baseline information on air passenger access characteristics. Such surveys provide information on trip purpose (business/non-business), residency, trip origin, and access mode, and they can include stated preference questions that can help support pricing strategies and the formulation of new ground access services. Data from passenger surveys provided the foundation for developing a key best practice: developing ground access revenue forecasting models. As demonstrated in this Reference Guide, it was possible to develop disaggregate models for DCA and SFO because of the availability of recent, statistically valid ground access survey data. An Excel-based simulation template that shows how the mode-choice model is applied to estimate revenue impact (based on hypothetical policy changes at SFO) is provided as a separate deliverable in addition to this Reference Guide. 57 Uber Technologies, Inc., Form S-1, Registration Statement, https://www.sec.gov/Archives/edgar/data/1543151/000119312519103850/d647752ds1.htm (accessed August 2, 2019). 58 Lyft, Inc., Form S-1, Registration Statement, https://www.sec.gov/Archives/edgar/data/1759509/000119312519059849/d633517ds1.htm (accessed August 2, 2019).

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As of June 2019, transportation network companies (TNCs) such as Uber and Lyft operate in the ground transportation markets at all major domestic commercial airports. The rapid emergence has presented multiple challenges to airport operators, states, regional transit authorities, and city governments.

The pre-publication draft of the TRB Airport Cooperative Research Program's ACRP Research Report 215: Transportation Network Companies (TNCs): Impacts to Airport Revenues and Operations is designed to help airport operators develop and implement practical approaches to managing TNCs within the context of commercial ground transportation policies and programs. The report presents best practices that have proven to be effective tools that airport operators can use to manage TNC operations and develop sustainable revenue models. It particularly is designed to help airport operators evaluate the tradeoffs among customer service, revenue generation, current operations, and long-term facility planning.

Additional resources include a Mode Choice and Revenue Simulator Template spreadsheet and an accompanying dataset.

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