National Academies Press: OpenBook

Airport Landside Data: Collection and Application (2023)

Chapter: Chapter 6 - Summary of Case Examples from Airports

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Suggested Citation:"Chapter 6 - Summary of Case Examples from Airports." National Academies of Sciences, Engineering, and Medicine. 2023. Airport Landside Data: Collection and Application. Washington, DC: The National Academies Press. doi: 10.17226/27403.
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Page 29
Suggested Citation:"Chapter 6 - Summary of Case Examples from Airports." National Academies of Sciences, Engineering, and Medicine. 2023. Airport Landside Data: Collection and Application. Washington, DC: The National Academies Press. doi: 10.17226/27403.
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Page 29
Page 30
Suggested Citation:"Chapter 6 - Summary of Case Examples from Airports." National Academies of Sciences, Engineering, and Medicine. 2023. Airport Landside Data: Collection and Application. Washington, DC: The National Academies Press. doi: 10.17226/27403.
×
Page 30
Page 31
Suggested Citation:"Chapter 6 - Summary of Case Examples from Airports." National Academies of Sciences, Engineering, and Medicine. 2023. Airport Landside Data: Collection and Application. Washington, DC: The National Academies Press. doi: 10.17226/27403.
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Page 31

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28 This chapter presents the results of the nine case examples conducted with the assistance of the staff of the airports serving Chicago (Midway and O’Hare); Denver; Fort Lauderdale; Indianapolis; Jackson, Wyoming; Orange County, California; Phoenix; and San Diego. As part of creating these case examples, airport staff were asked about data collection methods, their use of data in decision-making, any problems encountered, and examples in which the airport addressed a problem using data. This chapter documents the interview questions and summa- rizes the information gathered through each case example. Full results are presented in Appen- dix B: Summary of Interviews with Case Example Airports. 6.1 Data Collection Methods and Technologies Data collection methods at responding airports vary primarily based on the types of data collected and the availability of funds. These methods include manual observations, airport intercept surveys, and automated data collection systems such as camera-based technology using AI analytics. The most common methods and technologies used are summarized here: • Aggregated vehicle volume data (i.e., does not distinguish between modes) are typically collected using temporary roadway tube counters or permanent count stations, such as in-pavement loop detectors or camera-based technologies. Permanent count stations allow continuous data collection, while tube counters are typically used as needed for specific data collection efforts. • Vehicle volume data, by mode, are usually continuously collected for commercial vehicles through a ground transportation management system supplemented by self-reported data from TNCs which typically are not included in an airport’s ground transportation manage- ment system. • Passenger intercept surveys or Wi-Fi intercept surveys often collect passenger mode choice data. Passenger intercept surveys are usually scheduled (e.g., annually), while Wi-Fi intercept surveys can be conducted continuously or as needed to address a specific question. • Wait times for transportation services are most often collected as needed through a contractor conducting a manual survey. • Curbside dwell times are often collected as needed by a contractor conducting a manual survey. • Ridership of airport-owned vehicles (e.g., remote parking shuttles) is often collected through automated people counters, which involves using sensors installed on the bus doors, ceilings, or other areas to accurately and automatically count the number of passengers on the vehicle. Other methods involve having airport staff do manual counts. • Ridership of public transit is most often collected by the transit agency and provided to the airport upon request. C H A P T E R   6 Summary of Case Examples from Airports

Summary of Case Examples from Airports 29   • Staging/hold lot wait times are typically collected using ground transportation management systems as they can identify when a vehicle enters and exits the hold lot. • Ticket counter queue length and time-in-queue are often collected using camera systems, video analytics, or a manual survey. • Security checkpoint data are typically collected by TSA and provided to the airport monthly or upon request. Similarly, customs and immigration data are typically collected by U.S. Customs and Border Protection (CBP). • Passenger movements pre-security and post-security as well as passenger accumulations in holdrooms are often collected automatically via cameras or beacons and Wi-Fi data. • Employee mode choice data are often collected using surveys, and employee parking/permit data are part of the airport’s standard permit system. 6.2 Data Analysis In the case example airports, staff indicated that the primary analysis tool used is Microsoft Excel. Staff at some airports use business intelligence tools such as Tableau and PowerBI to sum- marize the data into dashboards for easy access. Data from contractors and third-party sources are generally accessible and available; however, airports reported occasional challenges with timeliness and lack of holistic data. 6.3 Legal Issues with Data Collection and Storage At all case example airports, staff indicated that no legal data collection and storage issues have been encountered. At all the case example airports, stored data can be requested through the federal and state Freedom of Information Acts, per public documentation statutes. Fort Lauderdale (FLL) reported that closed-circuit television (CCTV) data are stored for 30 days for law enforcement purposes only and are exempt from public dissemination. 6.4 Data-Driven Problem Solving In each case example airport, staff provided examples of how the airport used data to address problems at the airport. These examples, summarized here, include a broad range of applica- tions, including using data to address ground transportation service levels, revise automated people mover scheduling, and proactively adjust in-terminal maintenance staffing levels. 6.4.1 Chicago O’Hare International Airport The airport collected traffic volume data, turning movement counts, and signal timing data to address a roadway congestion issue at one of the terminals. A consultant was engaged to analyze the data and model the congestion in a microsimulation model (VISSIM). The VISSIM model was used to test and evaluate the efficacy of potential solutions. 6.4.2 Chicago Midway International Airport Airport staff analyzed ground transportation wait times for TNCs to verify customer com- plaints about substandard service. Data collected included the time when each vehicle left the hold lot and when the driver reported the passenger’s ride had begun. Based on those results, the airport identified and evaluated potential alternative hold lot locations and vehicle routes

30 Airport Landside Data: Collection and Application between the hold lot and the terminal. Based on the analysis results, the airport relocated the hold lot, identified a suggested path for drives, and significantly reduced customer waiting times for TNCs. 6.4.3 Denver International Airport Airport staff recently utilized interterminal train wait time data to make decisions on adjust- ing train frequency during peak periods to ensure sufficient capacity. Peak-period boarding and disembarking passenger volumes were collected using cameras. The collected data were consolidated monthly to aid the operations team in adjusting the interterminal train schedule. 6.4.4 Fort Lauderdale-Hollywood International Airport Airport staff used expected airline load factor data and traffic volume information during the 2022 holiday season to direct drivers to alternative pickup areas on the departure level during high arrival periods and to alternative drop-off areas on the arrivals level during high departure periods. The data were also used to determine staffing levels in the terminal building for predic- tive and preventive maintenance. 6.4.5 Indianapolis International Airport Airport staff recently used traffic volume data and the amount of time TNCs spend at the curbside to guide the building of a new roadway dedicated to TNCs, aiming to have sufficient capacity and provide better customer service. Airport staff also used results from the Airport Service Quality survey, which indicated dissatisfaction with concessions availability, to direct concessionaires to change their hours of operation. 6.4.6 Jackson Hole Airport Analysis of customer survey results indicated a desire by local passengers for a public transit option to travel between the airport and the city center. Airport staff used these results to approach the public transit agency, which has started planning to provide the requested route. 6.4.7 John Wayne Airport (California) Airport staff use passenger survey data to identify opportunities to improve the guest experi- ence. Survey results guide airport staff in crafting relevant messaging for stakeholders as part of the project approval process. 6.4.8 Phoenix Sky Harbor International Airport In 2022, PHX completed the development of a passenger flow model based on 57 different data sources, including automated and manual sources and airline booking data. PHX uses the flow model to be prepared for upcoming passenger activity, including staffing accordingly and planning maintenance and other activities to minimize potential impacts to passengers. The model development also resulted in a pilot program initially conducted with one in-terminal concessions operator. PHX provided the concessionaire with model results regarding forecast passenger volumes in each concourse. The forecast allowed the concessionaire to adjust staffing levels and plan for supplies at each location. Many stakeholders receive the data to improve their ability to allocate resources.

Summary of Case Examples from Airports 31   6.4.9 San Diego International Airport Airport staff used traffic volume data for TNCs and taxicabs to address multiple issues. Peak- period volumes provided the basis for estimating needed hold lot capacity and helped identify potential hold lot locations. Historical vehicle volume data were also used to reallocate the commercial ground transportation loading curb at the airport’s Terminal 2.

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Airports collect data to help understand the customer journey from the entrance or access points of the airport to the boarding gates. Processes may change in order to improve the customer experience when the collected data are analyzed.

ACRP Synthesis 132: Airport Landside Data: Collection and Application, from TRB's Airport Cooperative Research Program, documents landside data, collection methods, analysis, and interpretation and discusses how that information affects airport decision-making.

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