Yu zhang & yuan wang University of south Florida Implications of Autonomous Vehicle to Airport Terminal Planning and Design Yu zhang & yuan wang University of south Florida

Components U.S. Airport Revenue On-going Problems Autonomous Vehicles Study Objective and scope Research Approach Case Study of TPA Suggestions Future Work

Airport Terminal Area Airside Area Terminal building Parking lots Intermodal stations Access roads Runways Taxiways Ramps

Landing, parking and aerobridge fees US Airport Revenue Non-aeronautical Aeronautical Utility and service charges: parking, airport access and rental car facilities Airport concession Land rental Advertising Other Landing, parking and aerobridge fees Passenger and security service charges Franchise fees Other Quote from ACI report: Non-aeronautical revenues critically determine the financial viability of an airport, as these revenue sources tend to generate higher profit margins in comparison with aeronautical activities. Aeronautical revenues are collected from such sources as landing charges, which are circumscribed by either regulated tariffs, contractual agreements between carriers and airports, or a combination thereof. Thus, airports are heavily reliant on the non-aeronautical side of the business as a driver of revenue growth. Concessions – Rents paid by gift shops, restaurants, and newsstands, and, if agreed to in the concession contract, a percentage of the profits. Parking and Airport Access – Fees for all airport-owned parking lots and in some cases, off-airport concessions bringing travelers to and from the airport. Rental Car Operations – Revenue from rental car operations within or outside a terminal. Land rent – Excess airport land may be rented for golf courses, office buildings, hotels, farming or other uses. Advertising – Ads placed on airport walls, billboards and buses is a source of airport income.

US Airport Non-aeronautical Revenue Non-aeronautical revenues critically determine the financial viability of an airport. Medium and small hub airport finance are highly sensitive to the changes of non-aeronautical revenue. Data Source: FAA Compliance Activity Tracking System (CATS) database

US Airport Non-aeronautical Revenue In the ACI Non-Aeronautical Revenue report published in 2013, non-aero revenue takes apart 44.8% of the total operation revenue on average in the US. And 41.2% of them are from parking and ground transportation.

Parking & Ground transportation and Rental Car Facility Revenue The percentage of revenue from only parking and ground transportation in the non-aeronautical revenue were stable in last 14 years, with medium hub airports the highest around 50 percent. Revenue from rental car facilities also keep steady those years, 17% at large hubs, 23% at medium hub airports, and 42% at small hub airports. Data Source: FAA Compliance Activity Tracking System (CATS) database

On-going changes to airport Shared Mobility Services (eg.Uber and Lyft) Convenience and low cost of ride-sharing have caused flight passenger ground access mode shifting, eventually affect airport income from parking and taxi charges. Some airports have taken actions to remedy the lost by charging these companies, eg. Boston Logan Airport and Los Angeles International Airport.

On-going changes to airport Car-sharing Services (eg. Flightcar and Turo) Offering car rental using long-term parking vehicles of flight passengers. Such program has not been popularized yet, but would be harmful to airport parking service in the long run.

What is Autonomous Vehicle? Autonomous vehicles, also known as driverless vehicle, are vehicles capable of sensing the environment and navigating without human input. In 2013, four states have passed laws of permitting autonomous cars. In 2015, these four states together with Washington, D.C have been proved for testing fully autonomous vehicle on public roads.

Levels of Autonomous Vehicles Specific automation Automatic braking, electronic stability Level 2 Limited automation adaptive cruise, lane keeping Level 3 Restricted self-driving Fair weather, specially mapped routes Level 4 Self-driving under all conditions 2015 Tesla Update Model S Google self driving car under test

Autonomous Vehicle Implementation Stage It is been forecast that a fully self driving vehicle would be available to the market in 2020. If autonomous vehicle implementation follows the pattern of other vehicle technologies it will take one to three decades to dominate vehicle sales plus one or two more decades to dominate vehicle travel. Litman, Todd. "Autonomous Vehicle Implementation Predictions." Victoria Transport Policy Institute 28 (2014).

How autonomous vehicles (AVs) affect airports ? Other travel modes ---- Private car. Private car parking ---- Send car back home. Private car Private car ---- Ride-sharing. Rental car ---- ride-sharing because of the mobility. (This situation is on-gong at airports, even without AVs) On-demand Share Mobility Rental car companies may choose remote facilities. Rental car service may attract more residents to use to airports. Rental car/Car sharing When Autonomous Vehicle Dependent on the purchase price of AV, business models of AV transportation services, and the charges of different AV services, there are many possible mode shift of airport ground access with the emerging of AVs. Furthermore, the release of driving burden offered AV would encourage the shift of air travel to ground transportation short to medium distance flight trips (e.g. the flight trips between under 1000 miles, which represent 1-3 hours of air travel There are also mix situations, the travel mode shift caused by Autonomous Vehicle is much complex under current situation. Also we can find when AVs serve mainly as private cars or rental cars, they will have more impacts to the airports than other modes. In this preliminary study, we mainly focus on AVs as private cars.

How autonomous vehicles (AVs) affect airports as private cars or rental cars? With the emerging of AVs, airports are going to face challenge of revenue shrinkage from parking and rental car facilities for several reasons: Less parking at airports due to self-driving characters of AVs, and the possible drop of car ownership per household with AV emerging. More congestion at curbside because of more drop-off and take-off trip. Less rental car facilities using parking inside airports, eventually will cause reduced income from leasing parking space to rental car companies.

Objective and Scope of Study This research intends to do quantitative study on the implications of AVs as private cars to airports. We will focus on developing a micropscopic simulation framework to evaluate full version AVs’ impact to the parking and ground access of airport, and discussing how airport planners and designers should pro-actively adjust airport terminal building planning and design to provide flexibility in preparing uncertain future. The simulation development in this paper can be generally summarized into sequential processes to answer the following questions: 1) Where are the enplanement passengers’ demographic origination? 2) What is the travel mode choice of that passenger to airport? 3) What is the on-airport activity of the passenger (i.e. curbside drop- off or parking and parking choices and duration?

Simulation Framework Forecast enplanement passengers Zip code = Distance Forecast enplanement passengers Primary and secondary draw area, and population density GoogleAPI recommend route AV market penetration Enplanement passenger travel mode distribution Parking passenger parking choices

Assumptions All the forecast enplanement passengers in 2030 are originated from airport draw area Enplanement passengers locates proportionately base on current population density and draw area level. Rental car users are all tourists. Fuel cost stays same as current situation. There is no travel mode shift caused by AVs emerging. The choice between parking and sending AV back home is only decided by parking charges and fuel cost. Do not distinguish between individual travelers and group passengers.

Scenarios: Static VS Adaptive Scenario 1: Static Simulation Procedure Static simulation approach assumes the initial parking/curbside splits do not change in 2030, keep the same as current situation. We simulate the parking revenue under different AV market penetration, from 0 percent to 100 percent with 5 percent as the increment step. Scenario 2: Adaptive Simulation Procedure Adaptive simulation procedure takes into account the gradual change of splits between curbside and parking caused by the emerging of AVs. At the end of each iteration, the split between parking and curbside usage will be calculated and used for the next iteration of the simulation.

Case Study of Tampa International Airport (TPA) TPA is a medium hub airport, and enjoys high growth rate of enplanement passengers. Parking and rental car income keep steady at the percentage 75- 77% of all non-aeronautical revenue. TPA mainly serves domestic destinations, and international enplanements represent only 3.4 percent of all passengers in 2014. Also, most of passengers are origin and destination (O&D) passengers. Connecting passengers only takes 5 percent among all enplanements. TPA master plan planned to enlarge parking garage , also construct new rental car facilities to accommodate the terminal needs till year 2030.

TPA Data Sources Information used in this study are from ACRP report 40 and 2012 TPA Master Plan Updates. Passenger forecast Enplanement passenger forecast in 2030.(From 2012 TPA master plan) Passenger originations In TPA draw area, based on population density and draw area level.(Tiger Database) Passenger travel modes Use certain percentage of different travel modes share in TPA.(ACRP report 40 and 2012 TPA master plan) Parking duration Parking duration in each parking mode follow certain distribution.(2012 TPA master plan) Private car is AV or not (Market penetration)

TPA Service Area TPA TPA draw area also call service area includes 11 counties, which have 5 primary draw area, 6 secondary draw area. Each zip code region corresponds to a distance obtained from Google recommend route not considering congestion. Enplanements passengers are assigned to each zip code proportionally by population density and draw area level. Data Source: Tiger database

TPA Enplanement Passenger Ground Access Travel Modes Percent Parking Short-term 5.82% Short-term hourly 2.42% Short-term daily 3.40% Long-term 8.89% Economic 4.79% Curbside 36.30% Rental car 36.90% Commercial Vehicle 7.20% Total 100.00% Average duration of short-term hourly parking is 0.8 hour, and the average duration of short-term daily parking is 60 hours. Among all the short-term parking, 87 percent of the parking are less than 6 hours. As for long-term parking, the average duration is 84 hours, with 13 percent of which are less than 6 hours. The average duration of economic parking is 124.8 hours, with 5 percent of which are less than 6 hours. Parking duration: Assume that the distributions of parking duration follow truncated normal distribution. Given aforementioned information, we reversely fit the data and obtain the parameters of the distributions.

Simulation Results- Static Scenario The parking revenue is approximately linearly dependent on the market penetration of AVs. If the market penetration reaches to one hundred percent, 98 percent of the parking revenue will be lost compared to the situation where no AVs present.

Simulation Results- Adaptive Scenario When the market penetration is low, passengers who choose parking their cars at the airport decreases approximately linearly with the increase of AVs, meanwhile the curbside usage increases correspondingly. When AVs market penetration further increases, passengers who choose to park at the airport decrease more rapidly, and curbside usage increases faster as well. Ideally the market penetration reaches 100 percent (This situation probably will not be true), only 0.02 percent of passengers who drive would choose to park at the airport.

Simulation Results- Adaptive Scenario long-term parking decreases faster with the increase penetration of AVs, following by economy parking. Short-term parking is not influenced dramatically at the beginning stage, but is so when the penetration reaches 20 percent.

Simulation Results- Adaptive Scenario – Parking Rate Analysis When the market penetration reaches 15%, airport should increase the overall parking rate by 20% to keep the same average daily parking revenue with year 2014. When the market penetration reaches to 40%, TPA has to double the parking rate to keep even. What is more, high parking rate will lead to more drop-off and pick-up trips.

Simulation Results- Adaptive Scenario –Fuel Price Analysis The fuel price we used in the simulation is the current US national average, even when the fuel price doubled in the future, the parking revenue loss caused by the emerging of AVs will not change too much. Furthermore, manufactures are focusing on cheaper and eco-friendly electronic power to support Autonomous Vehicles. In the long run, fuel price would not be a big concern for AV users.

Suggestion for Airport Planners 1) specify future demand for short-term, long-term and economic parking by taking the emerging of AVs into consideration. Note that long-term and economic parking could be highly compromised with high market penetration of AVs. 2) In the long run, as parking revenue plays a significant role in airport revenue, especially for medium and small airports, airports should adjust their financial strategies to cope with financial loss due to the emerging of AVs.

Suggestion for Airport Designers 1) Curbside could get more congested when passengers choose to be dropped off and picked up instead of parking at the airport. For the TPA case study, curbside congestion is already an issue because there is limited space around the landside terminal building. Thus, airport designers need to consider how to redesign the curbside and airport access road to better accommodate the future demand of more drop- off and pick-up trips. 2) When AVs are highly adopted, there would be a rapid shrink on the demand of parking garage; airport designers should consider the flexibility in parking garage design and make parking garage easily be adjusted into other types of functional space.

Future Work This study is the first step to evaluate the impact of AVs to airport planning and design. Ongoing efforts to refine this study include: 1) involve demographic information into the simulation framework to improve the passenger geographic distribution. 2) develop more comprehensive decision model for passengers ground access and parking decisions 3) analyze possible emerging business models with AVs, explore travel mode shift due to the emerging of AVs and incorporate it into simulation procedure 4) In depth sensitivity analysis regarding airport parking rate and fuel cost, select other representative large hub airport which have different parking patterns.

Thank you! Yu Zhang, yuzhang@usf.edu Yuan Wang, yuanwang1@mail.usf.edu University of South Florida Go Bulls!