National Academies Press: OpenBook

Simulation Options for Airport Planning (2019)

Chapter: Chapter 6 - Conclusions and Future Research

« Previous: Chapter 5 - Simulation Tool Characteristics and Capabilities
Page 45
Suggested Citation:"Chapter 6 - Conclusions and Future Research." National Academies of Sciences, Engineering, and Medicine. 2019. Simulation Options for Airport Planning. Washington, DC: The National Academies Press. doi: 10.17226/25573.
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Page 45
Page 46
Suggested Citation:"Chapter 6 - Conclusions and Future Research." National Academies of Sciences, Engineering, and Medicine. 2019. Simulation Options for Airport Planning. Washington, DC: The National Academies Press. doi: 10.17226/25573.
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Page 46
Page 47
Suggested Citation:"Chapter 6 - Conclusions and Future Research." National Academies of Sciences, Engineering, and Medicine. 2019. Simulation Options for Airport Planning. Washington, DC: The National Academies Press. doi: 10.17226/25573.
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Page 47

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45 Simulation tools clearly represent an integral part of the process for airport planning and design. More than 80 percent of the survey responders indicated that they use simulation tools to support studies ranging from major long-term master plans to day-to-day operational analyses. This clearly suggests that the simulation tool choice is an integral part of the decision process to ensure the quality and reliability of the overall study and analysis. Findings Nearly all airport planners that responded to the survey indicated that they believe simulation tools are an integral and necessary part of the airport planning process. However, the decision about which simulation tools to use and when and at which fidelity level to apply the tools should be made intelligently to maximize the overall study benefits and use time and resources efficiently. As one of the survey responders indicated: Every airport and project is unique, and every application of every tool is different, therefore regard- less of how simple or complex the tool is, the analysis will likely be time consuming and costly (there is normally no quick answer). Keeping this mantra in mind, it is clear that some areas and scenarios are harder to simulate than others. As Figure 17 illustrates, airspace and airfield simulations are generally the most complex models to develop and analyze. This is primarily due to their high input data require- ments, functional complexity, and the level of difficulty in setting up operationally valid sce- narios. Medium levels of complexity and levels of effort are generally exhibited by airport and airfield simulation tools and by more generic passenger flow and terminal process simulations. Curbside simulation studies are generally less complex and resource intensive than those of other areas, at least from a simulation setup perspective. However, it is not just the simulation tool that has an impact on simulation study complexity and level of effort. The amount of data processing, the extent of the experimental design, and the level of statistical analysis can all have significant impacts on the complexity of a simulation study. Another point that was made by a survey responder is that “Tools have no capabilities. The analyst determines the quality of the analysis.” Hence, the application of a simulation tool to evaluate a specific operational or design problem is one thing. But unless the analyst understands the problem, knows how to apply the simulation tool correctly, and knows how to analyze data correctly, the results of a simulation study may very well be irrelevant and incorrect. Using unproven methodologies or varying from industry practices can create significant challenges in C H A P T E R 6 Conclusions and Future Research

46 Simulation Options for Airport Planning trying to retain competence in the simulation tool and confidence in the analysis and results. Confidence needs to be gained by project stakeholders using sound postanalysis practices and validation steps. Any simulation study should have quality assurance and control processes in place to ensure overall success. Processes should be in place to guide the development and execution of a simulation study using industry-accepted data standards, practices, and analyses methods. Other, more generic, engineering principles also need to be considered, including document and information configuration control and the verification and validation of simulation con- figurations and execution plans. A sound process that manages simulation input data across multiple scenarios and configurations is necessary, particularly for larger and more complex simulation studies. For many projects, a simulation tool should also be considered part of a suite of potential tools. Often, ensembles of simulation tools need to be used to produce complete and reliable results. This is particularly true when novel concepts are being simulated or when interfaces between different modes (passengers and aircraft or vehicles and passengers) need to be considered. As an organization, airports need to understand and accept when it is better to outsource simulation studies to consultants that are more familiar with specific tools and analysis practices. This decision needs to be based on an evaluation of the availability of internal knowledgeable resources and the potential cost and impact of the study. Capability Gaps and Problem Areas More than 50 percent of the survey responders felt satisfied with current simulation tool capa- bilities. This response rate may partly be a result of expectation management on the analyst’s side. No simulation tool will ever provide 100 percent of the desired functionality, but a seasoned analyst understands these limitations and knows when and how to use specific tools to answer specific questions. Several basic issues and problem areas with current simulation tools and their application were identified in the survey. The following rank-ordered list provides some insight into areas of perceived simulation study risk as well as missing capabilities in current tools: 1. Amount of experience required to gain proficiency (i.e., learning curve). 2. Usability, ease of use (e.g., user interface, software interaction, setup). 3. Effort required to format and ability to import data. 4. Ability to generate output compatible with follow-on analyses. 5. Quality of visualization (e.g., graphics, 2-D versus 3-D, display customization, user interaction). 6. Ability to debug and troubleshoot models. 7. Ease of setting up and generating visualization. 8. Ability to customize output formats and content. 9. Lack of variables and guidance on human-factors-related assumptions (e.g., buffers, sensi- tivity tests). Study Complexity Airspace Passenger Flow Terminal Process Curbside High Medium Low Airport and Airfield Figure 17. Survey results—simulation study level of complexity and effort.

Conclusions and Future Research 47 Overall, survey responders indicated that most simulation tools lack quality visualization capabilities and require significant effort in terms of input data processing and output data analysis. It is evident that the required experience level is a major decision factor, particularly when selecting higher fidelity simulation tools. Analysts need to understand when the use of simulation tools is appropriate for a study and what the basic study expectations are. A mis- understanding in these areas across the simulation team and external stakeholders can create significant issues. Future Research Although novel approaches and heuristics to airport and airspace planning and design, pas- senger flow analysis, and process optimization are continually introduced, the core method- ologies and analysis concepts remain largely unchanged. Nevertheless, as software technology and information processing theories evolve, other simulation tools and techniques may require consideration. The information on the use of simulation tools presented in this report represents the current state of industry practices in 2018. Ideally, as simulation tool functionality evolves and new tools enter the market, this information would be updated periodically. Such updates should also consider changes in regional preferences in simulation tools across the globe. Also, as system complexity grows, parallel processing simulation software may support ever growing systems. Some tools such as AirTOp already support rudimentary parallel process- ing concepts, but the division of processing tasks and interactions between parallel simulations remains a significant research topic. Another simulation concept that will require consideration is agent-based modeling and simulation. In complex sociotechnical environments where sys- tems, humans, and procedures interact, agent-based tools show promise in simulating emergent behavioral patterns with competing goals (Bouarfa et al., 2013). The concept of safety may also require more future consideration. Although simulation metrics today are primarily geared around efficiency and cost, there is often a safety aspect associated with changes in traffic demand, procedures, or designs. Very few current simulation tools really address this safety concept or other related human factors metrics.

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Global business and tourism depend heavily on the efficient operation of airports and movement of passengers, baggage, and cargo across many areas. With increasing demand and connectivity requirements for airports comes the need for more sophisticated simulation and modeling tools to validate design assumptions.

Furthermore, airport design and planning decisions have significant impacts on policy and major capital improvement decisions, which can be supported by simulation and modeling tools at many levels.

ACRP Synthesis 98: Simulation Options for Airport Planning is the result of the collection and analysis of information on current industry practices and on applications of simulation tools for airport planning and design. Credible simulation projects can help airport administrators, designers, engineers, and planners estimate the impact of planned changes on passenger traffic, aircraft traffic, roadway traffic, baggage movements, and other subsystems such as bus and train links and aircraft ground support operations.

The toolsets and processes used to analyze and simulate airport operations have changed significantly since the 1980s, when analysis techniques were limited to general purpose queuing and network analysis concepts or purpose-built simulation tools. These tools have become much more sophisticated and accurate in emulating real-world aircraft, passenger, and vehicle dynamics.

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