Click for next page ( 32


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 31
The Self-Tagging Decision-Making Tool User Guide 31 Simulation Tool--User Guide Prerequisites A set of prerequisite tasks are required in order to obtain the necessary information that will enable the user to develop useful simulation models. When possible, accurate data reflecting the specific subject airport, airlines, and passengers should be used to provide the most accurate results. Sample values, identified through case studies and from the ACRP Report 25: Airport Pas- senger Terminal Planning and Design, are included as Appendix B for cases in which accurate data cannot be obtained (Transportation Research Board, 2010). The following steps should be per- formed by the user to identify the necessary information: Step 1. Perform an audit of passenger processing throughput at each step of the check-in process by physical observation (Check-in Counter, Check-in Kiosk, Agent-Assisted Bag Drop and Rework). a. Document the time to process each party in seconds. b. Note the party size (1, 2, 3, 4+). c. Note any exceptions or reasons for significant delays. d. Calculate average processing times in seconds for each party size at each step. Step 2. Obtain resource information for the subject area. a. Agent work schedule for all of the Check-In Counter and Bag Drops for a day with peak activity. b. Number of Self-Service Check-In Kiosks. Step 3. Obtain flight information for all airlines serviced by the subject area on the same day for which resource information was identified. a. Identify airline, flight number, destination, and aircraft model for reference pur- poses only. b. Identify departure time in 24-hour format (hh:mm). c. Identify the amount of time prior to departure that check-in closes. d. Identify the amount of time prior to departure that check-in opens on-sight. e. Identify the number of seats available for sale. Step 4. Obtain passenger profile information for each of the flights input in the flight profile. a. Identify the percentage of seats occupied on each flight. b. Identify the percentage of passengers on each flight that are not originating at the subject airport. c. Identify the percentage of local boarding passengers on each flight that are part of a party of 1, 2, 3, and 4 or more. The total must equal 100%. d. Identify the percentage of local boarding passengers that arrive during each of the following check-in time segments. The total must equal 100%. i. The earliest 1/3 of the available duration. ii. The midrange 1/3 of the available duration. iii. The latest 1/3 of the available duration. e. Identify the percentage of local boarding passengers that utilize each of the avail- able check-in resources. The total must equal 100%. i. Traditional Check-In Counter. ii. Web or Mobile Check-In with No Bags (passengers who go directly to the security check-point). iii. Web or Mobile Check-In with Bags. iv. Self-Service Check-In Kiosk with No Bags (not self-tagging). v. Self-Service Check-In Kiosk with Bags (not self-tagging). f. Identify the number of local boarding passengers that require rework.

OCR for page 31
32 Guide to the Decision-Making Tool for Evaluating Passenger Self-Tagging Step 5. Obtain resource quantities and the breakdown of space in square feet allocated to each. a. The number of Check-In Counter Positions and the area required for the agent workspace, the counter, and the passenger workspace. b. The area allocated for Check-In Counter Queuing only. c. The number of Check-In Kiosks and the area required for the passenger workspace. d. The area allocated for Check-In Kiosk Queuing only. e. The number of Agent-Assisted Bag Drop Positions and the area required for the agent workspace, the counter, and the passenger workspace. f. The area allocated for Agent-Assisted Bag Drop Queuing only. g. The total area for Non-Processing purposes, including walking traffic lanes, secu- rity, airport administration, and concessions. Once the prerequisite information has been gathered, the user is prepared to enter the initial data into the five Input Components (Processor, Resource, Flight, Passenger, and Space). After the data entry has been completed by the user, the Simulation Tool will build two models (Queue and Space) of the current environment. As optional tools, the user can also run varying simula- tions by changing input values. Processor Throughput Input (Figure 19) 1. Enter the average calculations for each party size in the appropriate cell for each check-in step. 2. For Self-Tag Kiosk, Self-Tag Application, and Self Bag Drop, use the industry average num- bers from Appendix B. Resource Profile Input (Figure 20) 1. Enter the total number of agents staffing the Check-In Counters and Bag Drop Counters dur- ing each hour throughout the day. 2. Enter the total number of Self-Service Check-In Kiosks (not self-tagging). Flight Profile Input (Figure 21) 1. Enter the flight data for each flight. Passenger Profile Input (Figure 22) 1. Enter the passenger profile data for each flight. Figure 19. Processor Throughput Input.

OCR for page 31
The Self-Tagging Decision-Making Tool User Guide 33 Figure 20. Resource Profile Input. Figure 21. Flight Profile Input.

OCR for page 31
34 Guide to the Decision-Making Tool for Evaluating Passenger Self-Tagging Figure 22. Passenger Profile Input. Space Profile Input (Figure 23) 1. Enter the current quantity and space breakdown information for each resource. At this point the data entry by the user is finished. As mentioned earlier, the following two models are automatically generated by the Simulation Tool based on the input by the user. Figure 23. Space Profile Input.

OCR for page 31
The Self-Tagging Decision-Making Tool User Guide 35 Figure 24. Demand vs. Capacity Over Time summary table. Queue Model The Queue Model illustrates the demand versus the capacity for passenger processing at each of the check-in mediums, in terms of minutes and based on the values entered in the profile input sheets. The cumulative queue times are based on the difference between the resource capacity and processing demand as they accumulate and are resolved over time. A summary table, as shown in Figure 24, is used to identify the average queue buildup, average resources per hour during the queue, average wait time per passenger during the queue, average number of passengers in queue, maximum queue buildup, available resources during maximum queue, maximum wait time per passenger, and maximum number of passengers in queue. Charts, as shown in Figures 25 through 30, are included to provide a graphical depiction of the data. The queue model is preformatted for printing. Space Model The Space Model, as shown in Figure 31, illustrates the current space allocation versus the sim- ulated space requirements for each of the passenger processing resources in terms of square feet, based on the values entered in the profile input sheets. A summary table is used to identify the current square footage and the simulated square footage for each resource. As various simula- tions are tested to achieve the queue results desired, the space model will update to illustrate the space required to provide the necessary resources. Charts are included to provide a graphical depiction of the data. The space model is preformatted for printing. Simulations After the current models have been developed, the user is prepared to run simulations to see the impact that adding self-tagging resources will have on the airport. Perform the following

OCR for page 31
36 Guide to the Decision-Making Tool for Evaluating Passenger Self-Tagging Figure 25. Total Demand vs. Capacity Over Time. Figure 26. Check-In Counter/Bag Drop Demand vs. Capacity Over Time.

OCR for page 31
The Self-Tagging Decision-Making Tool User Guide 37 Figure 27. Agent-Assisted Bag Drop Demand vs. Capacity Over Time. Figure 28. Check-In Kiosk Demand vs. Capacity Over Time.

OCR for page 31
38 Guide to the Decision-Making Tool for Evaluating Passenger Self-Tagging Figure 29. Self-Tag Kiosk Demand vs. Capacity Over Time. Figure 30. Self-Tag Bag Drop Demand vs. Capacity Over Time.

OCR for page 31
The Self-Tagging Decision-Making Tool User Guide 39 Figure 31. Space Model: Current Space Allocation vs. Simulated Space Requirements. steps individually to see the impact they have on the current model. Once an understanding has been established as to the impact of individual changes, develop different simulations by com- bining various changes as desired. Step 1. Modify throughput times as desired to see the effect they have on queue times and space requirements. Step 2. Enter values for agent staffing of the Self-Tag Bag Drop and the Self-Tag Kiosks based on estimates of need. Step 3. Modify all resources as desired to see the effect they have on queue times and space requirements. Step 4. Add additional flights with generic data to see the effect that growth will have on queue times and space requirements. Step 5. Modify the Check-In Medium data to include self-tagging. a. Add a small percentage of Self-Tag Kiosk users. b. Reduce the "Traditional Check-In Counter" and "Self-Service Check-In Kiosk with Bags" accordingly to maintain a total of 100% across all resources. c. Observe the effect that migration to self-tagging will have on queue times and space requirements. Step 6. Modify other passenger profile information to simulate different trends. a. Modify load factors to simulate general increases or decreases in air travel. b. Modify percentage breakdowns of party sizes to simulate seasons of either greater business travel or leisure travel. c. Modify percentage breakdowns of arrival times to simulate passengers trending toward arriving later as they recognize processing times dropping. d. Modify rework percentages to simulate an increase in rework requirements upon initial implementation of self-tagging and a decrease in rework requirements as passengers learn the process.

OCR for page 31
40 Guide to the Decision-Making Tool for Evaluating Passenger Self-Tagging Step 7. Enter the required space in square feet for each of the resources. a. Check-In Counter Positions--square footage per check-in position including standing room for passengers. b. Check-In Counter Queuing--square footage per person in check-in counter queue line. c. Check-In Kiosks--space per kiosk including room for passengers. d. Check-In Kiosk Queuing--square footage per person in kiosk queue line. e. Agent-Assisted Bag Drop Positions--square footage per agent assisted bag drop position including standing room for passengers. f. Agent-Assisted Bag Drop Queuing--square footage per person in agent assisted bag drop queue line. g. Self-Tag Kiosks--space per kiosk including room for passengers. h. Self-Tag Kiosk Queuing--square footage per person in kiosk queue line. i. Self-Tag Bag Drop Positions--square footage per bag drop position, including standing room for passengers. j. Self-Tag Bag Drop Queuing--square footage per person in bag drop queue line. k. Non-Processing Area--square footage for non-processing functions.