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Multilevel passenger processing system structural parking adjacent to terminal (Hamburg Airport Authority)
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Airport Design
Concepts applicable Physical aspects of concepts
Single level connector
Airport size by enplaned pax/year Feeder under 25,000 Secondary 25,000 to 75,000 75,000 to 200,000 200,000 to 500,000 Primary over 75% pax O/D 500,000 to 1,000,000 Over 25% pax transfer 500,000 to 1,000,000 Over 75% pax O/D 1,000,000 to 3,000,000 Over 25% pax transfer 1,000,000 to 3,000,000 Over 75% pax O/D over 3,000,000 Over 25% pax transfer over 3,000,000
X X X X X X X X X X X X X X X X X X X X X
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FIGURE 10-23 Applicable concepts for airport design (Federal Aviation Administration [50])
The concept evaluation rating factors used for Geneva airport are listed in Table 10-3
Schematic Design
The schematic design process translates the concept development and overall space requirements into drawings which show the general size, location, and shape of the various elements in the terminal plan Functional relationships between the components are established and evaluated The adequacy of the overall space program is evaluated by airport users relative to their specific needs This phase of the process specifically examines passenger and baggage flow routes through the system and seeks to examine the adequacy of the facility from the point of view of flow levels and flow conflicts
Aircraft level boarding
Apron level boarding
Multi level connector
Single level terminal
Multi level terminal
Single level curb
Multi level curb
Transporter
Satellite
Linear
Pier
P l a n n i n g a n d D e s i g n o f t h e Te r m i n a l A r e a
Passenger convenience: Walking distance from curb to aircraft Walking distance for transfer passengers Walking distance from parking to aircraft Ease of passenger orientation Ease of passenger processing Operational effectiveness: Efficient taxing routes Ground flow coordination of vehicles and aircraft Apron area maneuverability Apron adaptation to future aircraft Vehicular access flows Direct routes to ancillary facilities Expansion adaptability: Ancillary facilities, flexible land use Staging adaptability Visual character of increments Gross terminal expandability Expandability of terminal elements Economic effectiveness: Capital cost Maintenance and operating costs Ratio of revenue- to non-revenue-producing areas
Source: Reynolds, Smith and Hills [25]
TABLE 10-3 Conceptual Development Rating Factors for Evaluation of Terminal Planning Concepts
Modeling techniques are usually employed in this phase of the process to identify passenger processing, travel, and delay times, and the generation of lines at processing facilities The main purpose for analyzing passenger and baggage handling systems is the determination of the extent and size of the facilities needed to provide a desired level of convenience to the passenger at reasonable cost In this analysis alternative layouts can be studied to determine which is the most desirable
Analysis Methods
A number of systems analysis techniques have proven to be useful for the analysis of facilities for passengers and baggage These include network models, queuing models, and simulation models
Network Models
These models are particularly useful for representing and analyzing the interrelationships between the various components of a passenger
Airport Design
or baggage processing system For example, passenger processing can be represented as a network with the nodes representing service facilities and the links representing the travel paths and passenger splits This type of representation allows estimation of delays to the passenger at various locations within the terminal An example of a network that has been applied to the evaluation of arriving passenger delay is the critical path model (CPM) [47] CPM is used to coordinate the various activities that take place in the system for handling both passengers and baggage Nodes that represent critical activities, that is, those that take the greatest amount of time, are easily identified and can be analyzed in more detail to determine their effect on the overall performance of the system The analysis of the service time and waiting time at each processor in a network model can be obtained through either analytical queuing models or simulation
Analytical Queuing Models
Queuing theory permits the estimation of delays and queue lengths for service facilities under specified levels of demand The application of queuing theory yields useful estimates of processing and delay times from which the required sizes of facilities and operating costs may be derived Virtually all of the components of the passenger handling system can be modeled as service facilities using queuing models The diagram in Fig 10-24 and Example Problem 10-10 showed an example of the application of a deterministic queuing model to the operation of a runway system to determine aircraft delay A similar type of analysis can be applied to the analysis of passenger processing systems It is possible to evaluate the impact of adding ticket agents on delays to passengers and on the size of the queues With this information it is possible to evaluate the feasibility of alternative operating strategies for the ticketing facility Diagrams similar to this may be constructed for each of the processors for the passenger and baggage handling system and yield satisfactory results when the demand rate exceeds the service rate For the analysis of component delay and queue length when the average demand rate over some period of time is less than the average service rate, queuing theory is used to generate mathematical functions representative of the arrival and service performance of the system To specify the mathematical formulation of this problem, it is necessary to define the arrival distribution, the service distribution, the number and use of the servers, and the service discipline Many of the components which service passengers in the airport terminal exhibit a random or Poisson arrival process The service characteristics are usually exponential, constant, or some general distribution defined by average service times and the variance of average service time In most cases, there is more than one channel for the performance
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