National Academies Press: OpenBook

Passenger Level of Service and Spatial Planning for Airport Terminals (2011)

Chapter: Chapter 2 - Research Approach

« Previous: Chapter 1 - Background
Page 7
Suggested Citation:"Chapter 2 - Research Approach." National Academies of Sciences, Engineering, and Medicine. 2011. Passenger Level of Service and Spatial Planning for Airport Terminals. Washington, DC: The National Academies Press. doi: 10.17226/14589.
×
Page 7
Page 8
Suggested Citation:"Chapter 2 - Research Approach." National Academies of Sciences, Engineering, and Medicine. 2011. Passenger Level of Service and Spatial Planning for Airport Terminals. Washington, DC: The National Academies Press. doi: 10.17226/14589.
×
Page 8
Page 9
Suggested Citation:"Chapter 2 - Research Approach." National Academies of Sciences, Engineering, and Medicine. 2011. Passenger Level of Service and Spatial Planning for Airport Terminals. Washington, DC: The National Academies Press. doi: 10.17226/14589.
×
Page 9
Page 10
Suggested Citation:"Chapter 2 - Research Approach." National Academies of Sciences, Engineering, and Medicine. 2011. Passenger Level of Service and Spatial Planning for Airport Terminals. Washington, DC: The National Academies Press. doi: 10.17226/14589.
×
Page 10
Page 11
Suggested Citation:"Chapter 2 - Research Approach." National Academies of Sciences, Engineering, and Medicine. 2011. Passenger Level of Service and Spatial Planning for Airport Terminals. Washington, DC: The National Academies Press. doi: 10.17226/14589.
×
Page 11
Page 12
Suggested Citation:"Chapter 2 - Research Approach." National Academies of Sciences, Engineering, and Medicine. 2011. Passenger Level of Service and Spatial Planning for Airport Terminals. Washington, DC: The National Academies Press. doi: 10.17226/14589.
×
Page 12
Page 13
Suggested Citation:"Chapter 2 - Research Approach." National Academies of Sciences, Engineering, and Medicine. 2011. Passenger Level of Service and Spatial Planning for Airport Terminals. Washington, DC: The National Academies Press. doi: 10.17226/14589.
×
Page 13

Below is the uncorrected machine-read text of this chapter, intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text of each book. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

7Project Description As previously stated, the objectives of this research were to develop passenger space allocation and LOS guidelines for ter- minal functional areas and a holistic metric for a passenger’s overall airport experience. Our research premise is that by combining the results of the quantitative and qualitative data collection presented herein, we can reach some conclusions regarding factors that affect passenger perception of LOS in the airport environment and then use those to determine guidelines for building airports in the future and redesigning existing infrastructure. Study Design Initial Approach The TransSolutions team’s approach was to develop a data collection methodology that used both quantitative and qual- itative approaches. TransSolutions was primarily responsible for the quantitative data collection and used methodologies that included time stamp, observation, and passenger intercept surveys to quantify measures of passenger service—including wait time, number of passengers in queue, and number of square feet per passenger. At the same time that quantitative data were collected, TransSolutions also asked passengers their perception of the LOS they were experiencing. Strategic Insight Group (SIG) expanded on the qualita- tive aspects of data collection by using ethnographic data collection techniques in the form of passenger intercept sur- veys that explored the passengers’ impressions (using open- ended questions) as well as by observing passenger behavior. One of the team’s challenges was to develop a data collection methodology that complemented the efforts of each team member and secured findings that could be correlated between the two endeavors. One of the first project activities was to survey all com- mercial service airports to assess their views regarding LOS, their prevailing practice regarding use of LOS standards in facility development (including which, if any, standards they used), and their willingness to participate in a data collection study. This was accomplished through an online survey. Of the 162 airports that were sent the survey, approximately 20% responded. Of those responding, 65% said they were familiar with LOS standards (most frequently referencing IATA stan- dards) and used them to plan various elements of their facil- ities. However, only about 30% of respondents believed that a new, universal North American LOS standard would be a major improvement. Impact of Passenger Differences on Passenger Perceptions of Service The initial project approach was for data to be collected in airport passenger processing areas at 10 (later adjusted to seven) North American airports, to quantify objective measures of passenger service (processing and wait time, number of pas- sengers in queue, square feet per passenger, and so forth), and to assess passenger perceptions regarding LOS. An important aspect of the study’s data collection plan design was to select airports that would allow characteriza- tion of many of the diverse passenger characteristics and air- port facility characteristics that aviation stakeholders speculate affect passenger perceptions and hence potential differences in the types of airport facilities they desire. Additionally, data were to be collected at airports that use different airport design paradigms. The data were analyzed to determine whether such differences were significant relative to passenger perceptions. Table 1 shows candidate airports that would provide a valid cross section of data to indicate the types of expected passenger characteristics to be explored in our selection of study airports. Airports indicated in bold were those chosen for this research project. To develop a data collection methodology that would accomplish the study objectives, a team workshop was held to develop the airport survey instrument and perform other C H A P T E R 2 Research Approach

8task activities. Differences in quantitative and qualitative re- search concepts were discussed and folded into strategies by the quantitative and qualitative technical team leaders. From this, each team member built a strategy to best capture the relevant data needed from their particular discipline. Data Collection Cities TransSolutions targeted airports from Table 1 that allowed assessment of any differences that might exist between air- ports from each characteristic category. Airports that voiced a specific interest to be considered for data collection (in our online survey) were the first considered for on-site surveying. Table 2 details the study airports along with the categories and characteristics each airport represents. Data Collection Methodology Initial Approach A pretest of the survey instruments at DFW airport was conducted during the week of January 28, 2008. Results from the pretest were reviewed and the data collection approach adjusted for the first data collection at DFW over President’s Day weekend (February 15–18). TransSolutions initially used four methods to understand passenger perceptions of level of service in relation to the space available to them and the process time involved in their journey: 1. Wait-time studies and observation of queue length corre- lated with passenger perception surveys, 2. Passenger surveys regarding perception, 3. Video capture and analysis of dwell time, and 4. Ethnographic research. The team looked at the results of the DFW test and realized that analysis of the video data would be too time-consuming relative to the number of data points collected. The group re- designed the studies to accomplish the goals of the study in a more efficient manner. Essentially, all of the passenger inter- cepts were converted to a two-person process that could be completed without the use of videographic evidence. The first person would hand the passenger an ID card while ask- Airport Size Characteristic Category Large Medium Small O&D SEA, LGA, BWI, SAN OAK, RDU, PDX, RNO, SDF OKC, DAY, LIT Predominant passenger type Connecting DFW, ATL, ORD, MSP, IAD STL, MEM, HOU, CLE — Legacy ORD, ATL, MIA, IAH, IAD STL, MEM, CLE, SJU SAV, HSV, XNA Low cost ATL, MDW, PHX, BWI, FLL OAK, BNA, HOU, DAL BIL, SFB, PHF Predominant carrier(s) No predominant carrier LAX, JFK, MCO, HNL MSY, BDL, SAT, MHT, SDF HPN, MYR, ABE International LAX, JFK, DFW YYZ, SFO SJU GUM, GSN, SFB Predominant destinations Domestic DEN, DFW, LGA, BWI All PHF, PNS, BTV Leisure LAX, LAS, MCO, HNL SJU, MSY, OGG, RSW ACY, PSP, MYR Purpose of travel Business LGA, LAX, JFK, LAS DAL, PVD, SJC, MCI ILM, OKC, DAY Centralized ATL, DEN PDX, DAL, ANC, AUS, SDF ALB, MDT, LIT, SYR Terminal configuration Decentralized DTW, DFW,MCO, BWI STL, SAT, BNA, YVR — Single terminal ATL, DEN, IAD, YUL PDX, BNA, ABQ, ANC, DAL TUL, MDT, ALB, DAY, PHF Landside terminals Multiple terminals DFW, JFK OAK, SAT — Note: O&D = origin and destination Table 1. Examples of airports by category.

ing him or her several demographic questions and marking the time of the first interview. When the passenger reached the end of the process, a second interviewer would record the intercept time and complete the interview by asking the pas- senger what his or her perception of the process was on a five-point scale (where 1 is excellent and 5 is very bad). Another alteration was made with respect to Federal Inspec- tion Services (FIS) facilities. It was apparent from the test run that it was going to be impractical to obtain the proper num- ber of escorts with the necessary language skills to fairly mea- sure the perception of passengers within the FIS facilities. Given this reality, in addition to international flights’ arrival times being highly variable and the limited availability of the data collection team’s resources, the ACRP panel agreed on the cancellation of conducting FIS interviews. Final Approach After analyzing the results of the initial data collection, the team made the aforementioned adjustments to the scope and techniques and conducted six more full-scale data col- lections at the following airports between August 10 and September 17, 2008: Austin-Bergstrom International Airport (AUS), Hartsfield-Jackson Atlanta International Airport (ATL), Oakland International Airport (OAK), Louisville International Airport (SDF), McCarran International Airport (LAS), and Washington Dulles International Airport (IAD). In addition to DFW, concurrent ethnographic data collections were con- ducted at ATL, SDF, and IAD. At each airport, all areas selected for data collection were documented. This included taking photographs and making physical surveys of the area to determine the size of the area for future calculations of space per passenger. It was not prac- tical to obtain CAD drawings of the airport areas observed. Therefore, the data collectors were careful to determine the area that would be used for calculation of LOS based on • In the check-in area, the queue area designated for waiting and the area designated for check-in service; • In the holdroom, the seating area exclusive of the agent counter, queue, and jet bridge boarding/de-boarding area; and • In the baggage claim area, the active claim area, defined as 11.5 ft from the face of the baggage claim devices. For the quantitative passenger intercepts, the team con- ducted surveys of passenger perceptions in various areas of the airport. The areas where intercepts were conducted com- prised the entire process-based passenger experience once inside the terminal facilities. For passengers checking in, the curbside positions, ticket agent, or kiosk area typically provide their first interaction within the facility. Exhibit 1 shows select check-in areas at various study airports. Due to the short nature of the associ- ated queues and wait times involved, for the kiosk process, the second interview occurred immediately after the passen- ger completed the kiosk process (but prior to bag drop-off). All passengers must then proceed to the security screen- ing checkpoint (SSCP) area, where uniformed Transporta- tion Security Administration (TSA) agents conduct a passive search of the passengers and their bags and possibly recom- mend them for further screening. The waiting line for this process is actually divided into two areas by an ID check pro- cedure. Prior to the ID check, passengers generally wait in a 9 Table 2. Categories and characteristics for subject airports. Airport ID Airport Name and Location Airport Size Predominant Passenger Type Predominant Carrier(s) Predominant Destinations Purpose of Travel Terminal Configuration (per Terminal) Landside Terminals FAA Region DFW Dallas/Fort Worth International Airport— Dallas, TX Large Connecting Legacy Domestic/ international Leisure, business Decentralized Multiple Southwest AUS Austin-Bergstrom International Airport— Austin, TX Medium O&D Low cost Domestic Business Centralized Single Southwest ATL Hartsfield-Jackson Atlanta International Airport—Atlanta, GA Large Connecting Legacy, low cost Domestic/ international Business Centralized Single Southern LAS McCarran International Airport— Las Vegas, NV Large O&D None Domestic Leisure, business Centralized Multiple* Western- Pacific OAK Oakland International Airport—Oakland, CA Medium O&D Low cost Domestic Business Decentralized Multiple Western- Pacific SDF Louisville International Airport— Louisville, KY Medium O&D None Domestic Business Centralized Single Southern IAD Washington Dulles International Airport— Dulles, VA Large Connecting Legacy Domestic/ international Leisure, business Centralized Single Eastern *Smaller secondary terminal Note: O&D = origin and destination

cepts occurred at the entry and exit points for these facilities (see Exhibit 3). The only intercept conducted specifically for arriving passengers occurred at baggage claim (see Exhibit 4). It was performed similarly to the kiosk process above, although the first interview with the passengers occurred when they first arrived to the claim area, and the final interview occurred after the final passengers claimed their bags and were ready to depart. Data Collection Schedule The choice of data collection times and locations was driven by the desire to get the most quantitative data possible. Specifi- cally, departing passenger demand was assumed to be heaviest during the early morning hours and late afternoon. Arriving- passenger demand was assumed to peak during the early to late evening. For those reasons, passenger check-in, SSCP screen- ing, and holdroom intercepts were planned for the early morn- ing and early evening, while bag claim intercepts for arriving passengers were scheduled for the 4 through 6 p.m. time frame, as shown in Table 3. A total of three days’ worth of data were taken for each airport, from Sunday afternoon to Wednesday morning. We expected this would provide a good cross section of par- ticipants that would potentially include leisure and business travelers. Once a current flight schedule was obtained for the candi- date airport, these times were modified to fit the actual pas- senger pattern at that specific airport. In order to maximize the total number of responses, airlines with a larger passen- ger share at a particular airport were scheduled for Monday collection since that day is traditionally a heavier demand day across the system. Statistically valid survey design tech- niques were used to ensure a representative sample of air- lines for each airport with regard to check-in facilities and bag claim locations. 10 ATL – Curbside ATL – Ticketing/ Delta Air Lines OAK – Kiosk Exhibit 1. Select check-in areas at study airports. Exhibit 2. Security screening checkpoint at IAD. single queue (or multiple queues, if the airport separates pas- sengers by type—such as frequent traveler or preferred sta- tus), but once the ID check is complete, the passengers gen- erally move into a set of smaller queues in front of each x-ray lane where they begin to divest their belongings. It was important for the research to gauge passengers’ per- ceptions of the experience prior to their actual interaction with the agent for that process, so our data collectors were sta- tioned at the front and back of each process queue under re- view. For the SSCP, the interviews were done solely in the queues prior to the ID check process (see Exhibit 2). Once the passenger exits security, the remaining areas of interest are not agent- or processor-based. Passengers move through the corridors and concourses, and those waiting for an automated people mover (APM) (for airports that have them) or waiting for boarding while in a holdroom do have an element of waiting involved (or the potential for conges- tion that slows movement, in the case of a crowded concourse or holdroom). For that reason, the interviews for these inter-

Data Collection Procedure Several questions were asked of the subjects to help classify the perception rating at the beginning of the process. For check-in locations, passengers were interviewed prior to joining the queue in front of the agents or kiosks. They were asked the following questions: • How many people are in the traveling party? • How many bags are you checking? • How many carts are you using? • Is your trip primarily for business or leisure purposes? • Is your trip to a domestic or international location? For other airport locations, the passenger was asked • Is your trip primarily for business or leisure purposes? • Is your trip to a domestic or international location? Additionally, the data collectors noted the start and end time of the processing and the number of people in the area of interest at the time of the observation. • For the check-in and security processes, the passengers were interviewed when they entered the queue and again before they talked to the check-in agent or the ID checker prior to the security checkpoint. • The processing time for the holdroom was calculated from initial passenger arrival at the holdroom area to the time they entered the boarding queue. • The processing time for the APM stations was calculated from passenger arrival at the platform to subsequent board- ing of the train. • The calculation of the processing time for the corridor was completed by positioning the data collectors approximately 100 to 200 ft apart and intercepting passengers moving in the dominating flow direction. The data collectors were positioned so that there were no intervening holdrooms, restrooms, or shops to divert passengers. The team thus collected objective passenger processing data, including wait time, number of passengers in queue, and square feet per passenger. Two instruments were used to con- duct the survey in each case. First, a personal digital assistant (PDA) was used to accurately record responses to each of the questions posed by the interviewer. Second, a colored, num- bered card was handed to the interview subject at the beginning of the queue and requested to be returned once the passenger reached the other interviewer at the head of the queue. By rec- onciling the time stamps for the particular passenger, the time spent waiting in process could be calculated. Passengers were asked at the end of the process to rank their experience on the scale shown in Table 4. Finally, ethnographic research was conducted in areas of the airport to record in-depth passenger perceptions. Ethno- graphic data were collected on focused passenger processing 11 Exhibit 3. Corridor, APM, and holdroom intercept areas. Exhibit 4. Baggage claim at LAS. SDF – Corridor DFW – APM area AUS – Holdroom

from solo business travelers to large families on vacation. T-tests were performed to determine if there was any signif- icant difference between the responses for large and small air- port types for a given factor before it was aggregated as shown in Table 6. Since for most areas there was no significant dif- ference, we felt it appropriate to aggregate the data to attempt to form a national standard. Using a standard statistical analysis approach, the null hypothesis for the t-test was defined. For this case, our null hypothesis was that the actual average perception ratings are equal for the two populations under consideration in each test (Data Group A and Data Group B). The calculated p-value represents a probability that corresponds to this question: For an experiment of this magnitude, if the true populations studied really do have the same mean value, what is the proba- bility of observing at least as large a difference between sample means as was actually observed? If the p-value is less than a certain threshold (traditionally .05, or 5%), then we reject the null hypothesis previously stated and conclude that there likely is a difference between the average perception for the two data sets. Essentially, the lower the p-value, the more certain we are that the observed difference between data groupings is statistically significant. If we are unable to reject the null hypothesis, we cannot say with confidence that there is no difference; we were just not able to detect it with this experiment. Once it is determined that the differences in the average perceptions are significant, we can examine the trends within each separate data group to determine when the average perception is likely to be worse than acceptable (in our case when the average perception rating = 3.0). This 12 Sunday Monday Tuesday Wednesday 5:00 a.m . to 7:00 a.m . Ticketing Concourse Holdroo m APM 5:00 a.m . to 7:00 a.m . Ticketing Kiosk SSCP Kiosk 5:00 a.m . to 7:00 a.m . Concourse Holdroo m APM Morning n/a 7:00 a.m . to 9:00 a.m . Ticketing Concourse Holdroo m APM 7:00 a.m . to 9:00 a.m . Ticketing Kiosk SSCP Holdroo m 7:00 a.m . to 9:00 a.m . Concourse Holdroo m APM Afternoon Flight in As required to arrive no later than 2:00 p.m. 9:00 a.m . to 4:00 p.m. Break 9:00 a.m . to 4:00 p.m. Break 9:00 a.m . to 12:00 p.m . Flight out 4:00 p.m. to 6:00 p.m. Kiosk SSCP Concourse Ticketing 4:00 p.m. to 6:00 p.m. Curbside SSCP Bag clai m 4:00 p.m. to 6:00 p.m. Curbside SSCP Bag clai m Evening 6:00 p.m. to 8:00 p.m. Kiosk SSCP Holdroo m Ticketing 6:00 p.m. to 8:00 p.m. Bag clai m 6:00 p.m. to 8:00 p.m. Bag clai m n/a Table 3. Generic data collection plan. Scale Description 1 Excellent 2 Good 3 Acceptable 4 Bad 5 Very bad Table 4. Passenger perception scale. at the ticket counter, security screening checkpoint, and bag- gage claim. In the holdrooms, research was conducted with the objective of evaluating the passengers’ holistic view of their airport experience. As the data collection process progressed from airport to airport, captured data (quantitative) points were cataloged as detailed in Table 5 to ensure coverage of major airlines and facility types. Data Analysis Approach For each condition, the data groupings were compared to determine if there was a difference in the average perception values using a standard statistical technique known as the t-test. The team verified that the underlying assumptions (regarding sample sizes, normalcy, and independence of the data points) for the use of this test were validated. It was further assumed for the purposes of this analysis that the data collected were sufficiently representative of the na- tional air-traveling public as a whole. We chose seven airports that varied in size, geographic location, and function, and col- lected data from passengers of many demographics and types,

will become the nominal “turning point” (TP) of the envi- ronmental factor, the point where we find that the average perception switches from better than acceptable to less than acceptable based on the factor of interest (i.e., space per passenger or wait time). If there are no such significant trends available for a factor, then we cannot reliably deter- mine a TP and thus cannot develop a design metric for that quantitative factor. 13 Collection Location for Data Points Airline No. of Kiosks Kiosk Bag Drop Staffed Agent Check-In Curbside Check-In Baggage Claim SSCP Corridor Hold- room Unknown 0 0 0 11 0 205 0 0 American 8 0 0 0 0 0 0 25 Delta 12 0 0 0 0 0 0 25 JetBlue 4 0 60 0 40 0 0 22 Southwest 6 0 0 0 0 0 0 25 United – domestic 50 39 60 27 150 0 100 35 United – international 22 0 73 0 0 0 0 39 Virgin America Airlines 6 0 0 0 0 0 0 25 Total Data Points Captured 39 193 38 190 205 100 196 Table 5. Catalog of captured data points, IAD. Functions Small/Medium Airport Average Perception Large Airport Average Perception p-value Significant Difference Curbside 1.19 1.93 0.001 Yes Ticketing 2.22 2.42 0.159 No Kiosk 2.12 2.16 0.832 No Bag drop 2.19 2.16 0.893 No SSCP 1.99 1.89 0.310 No Corridor 1.72 2.07 0.001 Yes Holdroom 1.79 1.97 0.056 No Bag claim 2.06 2.21 0.012 Yes Table 6. Measure of perception difference by airport size.

Next: Chapter 3 - Findings and Applications »
Passenger Level of Service and Spatial Planning for Airport Terminals Get This Book
×
MyNAP members save 10% online.
Login or Register to save!
Download Free PDF

TRB’s Airport Cooperative Research Program (ACRP) Report 55: Passenger Level of Service and Spatial Planning for Airport Terminals examines passenger perception of level of service related to space allocation in specific areas within airport terminals.

The report evaluates level-of-service standards applied in the terminal planning and design process while testing the continued validity of historic space allocation parameters that have been in use for more than 30 years.

  1. ×

    Welcome to OpenBook!

    You're looking at OpenBook, NAP.edu's online reading room since 1999. Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website.

    Do you want to take a quick tour of the OpenBook's features?

    No Thanks Take a Tour »
  2. ×

    Show this book's table of contents, where you can jump to any chapter by name.

    « Back Next »
  3. ×

    ...or use these buttons to go back to the previous chapter or skip to the next one.

    « Back Next »
  4. ×

    Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book.

    « Back Next »
  5. ×

    To search the entire text of this book, type in your search term here and press Enter.

    « Back Next »
  6. ×

    Share a link to this book page on your preferred social network or via email.

    « Back Next »
  7. ×

    View our suggested citation for this chapter.

    « Back Next »
  8. ×

    Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available.

    « Back Next »
Stay Connected!