National Academies Press: OpenBook

Airport Landside Data: Collection and Application (2023)

Chapter: Chapter 5 - Data Analysis and Decision-Making

« Previous: Chapter 4 - Terminal and Landside Data Collection at Airports
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Suggested Citation:"Chapter 5 - Data Analysis and Decision-Making." 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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Suggested Citation:"Chapter 5 - Data Analysis and Decision-Making." 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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Suggested Citation:"Chapter 5 - Data Analysis and Decision-Making." 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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Suggested Citation:"Chapter 5 - Data Analysis and Decision-Making." 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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Suggested Citation:"Chapter 5 - Data Analysis and Decision-Making." 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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Suggested Citation:"Chapter 5 - Data Analysis and Decision-Making." 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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22 The ability to effectively analyze and interpret data is critical for airports to stay competitive and meet the current and evolving needs of passengers, airports, and stakeholders. Several steps are typically involved in data decision-making at airports: 1. Defining the problem or question: This involves identifying the issue or question that needs to be addressed and understanding the context and scope of the problem. 2. Gathering data: Once the problem or question has been defined, relevant data need to be collected from various sources, such as surveys, interviews, focus groups, or other data col- lection methods. 3. Analyzing data: Once the data have been collected, the information needs to be analyzed to identify patterns, trends, and relationships. This analysis may involve statistical techniques, data visualization tools, or other methods. 4. Interpreting results: After the data have been analyzed, the results need to be interpreted in the context of the problem or question identified in step one. 5. Making decisions: Based on the data analysis and interpretation results, decisions must be made to address the problem or question identified in step one. These decisions may involve developing strategies, making policy changes, or taking other types of action. 6. Monitoring and evaluating: Once decisions have been made, it is important to monitor and evaluate the impact of those decisions over time to determine if they effectively addressed the problem or question identified in step one and if any adjustments need to be made. 5.1 Why Data Are Collected The following summarizes some of the key reasons that data are typically collected: 1. Passenger flow and demand: This information is critical for understanding passenger demand patterns, optimizing resource allocation, and improving the passenger experience. 2. Terminal and facility utilization: This information is used to understand facility utiliza- tion, optimize resource allocation, improve the efficiency of terminal operations, determine problem areas or bottlenecks in the terminal building, and improve overall customer service. 3. Customer experience: This information is used to understand customer needs and prefer- ences, identify areas for improvement, and enhance the overall passenger experience. 4. Ground transportation: This information is used to understand ground transportation demand patterns, optimize service levels on roadway systems and curbside, and improve the overall ground transportation experience. 5. Employee: This information is used to understand the mode of travel to the airport by employees, optimize any shuttle services to transport employees from parking locations, minimize the travel time for employees, and ensure adequate parking for employees. C H A P T E R   5 Data Analysis and Decision-Making

Data Analysis and Decision-Making 23   5.2 How Data Are Analyzed and Interpreted When analyzing and interpreting data at airports, it is important to understand the data, including what it represents and can be used for. This analysis requires a thorough understand- ing of the data sources, collection methods, and quality. Data quality is critical for effective data analysis and interpretation at airports. Poor quality data can result in incorrect conclusions and ineffective decision-making, hindering the airport’s operations and the passenger experience. Data quality refers to the degree to which data are accurate, complete, and relevant to the intended use. In the context of data collected at airports, data quality is influenced by factors such as the accuracy of the data collection methods, the completeness of the data, and the relevance of the data to the intended use, including understanding the limitations of the data sources and the data collection methods, as well as the potential for errors or biases in the data. For example, data collected from passenger surveys may be influenced by how the questions are phrased, the sample of passengers selected, and the response rate. Once the data have been collected and data sources and collection methods are understood, it is important to validate the data to ensure accuracy and completeness. This validation may involve data cleaning, normalization, and reconciliation to correct errors and ensure that the data are consistent and accurate. Another important aspect of data quality is the relevance of the data to the intended use. Relevance refers to the degree to which the data are useful and appropriate for the analysis and interpretation required. For example, data collected from passenger surveys may not be relevant for understanding the operational efficiency of an airport. In contrast, data collected from sen- sors and cameras may be more relevant. Data bias is another important consideration. Data bias refers to the systematic and often hidden errors in data collection, processing, analysis, and interpretation. Data bias at airports can occur in various ways; examples include biased data collection methods, unequal representation of different groups in data, and the use of algorithms that reflect and amplify existing biases. Biased data can negatively affect airport operations, passenger experiences, and safety. For example, biased data may lead to inaccurate predictions of passenger behavior and demand, resulting in inefficient resource allocation and service delivery. Additionally, biased algorithms used in secu- rity screening or immigration processes may unfairly target certain groups or individuals, leading to discriminatory practices. Airports must recognize the potential for bias in their data collection and decision-making processes and take steps to mitigate it, such as by ensuring diverse repre- sentation in data collection and analysis and regularly auditing algorithms for bias. Several tools and approaches are commonly used for airport data analysis and interpretation. Data analysis and interpretation involves several key steps, including: 1. Data cleaning and preparation involves cleaning and preparing the data for analysis by removing errors and inconsistencies, filling in missing data, and transforming the data into a format suitable for analysis. 2. Data analysis involves using statistical and other methods to analyze the data and under- stand its characteristics, including descriptive statistics, regression analysis, and time-series analysis. 3. Data visualization involves using visual representations of the data to help illustrate pat- terns, trends, and relationships in the data. Tools such as bar charts, line charts, scatter plots, and heat maps, among others, are commonly used to visualize data. 4. Drawing conclusions involves using the insights gained from the data analysis and visualiza- tion to draw conclusions and make informed decisions, such as identifying trends, patterns, and relationships in the data and making predictions about future trends and patterns.

24 Airport Landside Data: Collection and Application 5. Communication involves communicating the findings and conclusions from the data analysis to the relevant stakeholders, including preparing reports, presentations, and dash- boards that communicate the findings and their implications for decision-making. In addition to these key steps, airport data interpretation may involve data visualization and analysis tools, such as Microsoft Excel, PowerBI, Tableau, and other dashboards. These tools can help airport operators quickly and easily visualize and analyze large amounts of data, enabling them to gain insights and make informed decisions. One of the most widely used tools is Microsoft Excel, a spreadsheet software for basic data analysis and visualization. With Excel, airport operators can create charts, pivot tables, and other data summaries to help them better understand their data. In addition to Excel, other business intelligence tools such as PowerBI and Tableau are also commonly used at airports for data analysis and visualization. These tools provide advanced data visualization capabilities, allowing airport operators to create interactive dashboards and reports that can help support decision- making processes. For example, a dashboard in PowerBI or Tableau can show the number of passengers that have traveled through an airport, the average length of time they spend in the terminal, and the most popular retail and food and beverage outlets. An example of a dashboard summarizing airport data is provided in Figure 5-1. In addition to using in-house tools and dashboards, many airports also utilize the services of third-party contractors and consultants for data analysis and interpretation. These contractors and consultants bring specialized expertise and knowledge to the data analysis process, suggest- ing how the data can support decision-making processes. For example, a consultant may analyze passenger data to identify trends and patterns that can help airports understand what drives pas- senger behavior and make decisions that support growth and improve the passenger experience. Aside from these tools, some airports also use custom dashboards and reporting tools tailored to their specific needs and data sources. These dashboards can provide real-time data and insights critical for effective decision-making. For example, a custom dashboard can provide data on wait times at security checkpoints, which can help airports identify areas for improvement and make changes to reduce wait times for passengers. 5.3 How Data Are Used to Make Decisions Data plays a critical role in decision-making processes at airports. Data collection and analy- sis enable airport operators to make informed decisions about their operations, enhancing the passenger experience and overall airport efficiency. Through a combination of quantitative and qualitative data, airport operators can identify existing deficiencies and areas for improve- ment, which helps inform the types of solutions, strategies, and improvements that could be implemented. Data are used to measure various aspects of airport operations, including demand levels, delays, and service quality. By analyzing passenger activity and behaviors, airport operators can better understand their customers and the airport’s services. This information is crucial in deter- mining the quality of services offered at the airport and provides insight into passenger opinions, performance, and reliability. It helps airport operators assess existing conditions and predict future activity, which is important in informing the solutions, strategies, and improvements that need to be considered. One important use of data is in understanding the customer experience. By collecting and analyzing data on passenger activity and opinions, airport operators can better understand what their customers need and expect. This information then informs planning, operations, and

Figure 5-1. Sample dashboard from San Diego International Airport.

26 Airport Landside Data: Collection and Application design decisions. For example, data can be used to determine the locations where passengers are most likely to congregate, such as at security checkpoints or ticket counters, which in turn can inform the design and layout of the airport to ensure that these areas are easily accessible and well-staffed. Data also provide information for day-to-day operations and business processes, such as fees, revenues, and financial planning. By collecting and analyzing passenger demand and behavior data, airport operators can make informed decisions about allocating resources and improv- ing their operations. For example, ground access data can be used to determine the optimal fee structure for commercial ground transportation, ensuring that the fees reflect an operator’s use of airport facilities and the business benefit they derive from them and that the revenue gener- ated is sufficient to cover the costs of operating and maintaining the ground access facilities (e.g., roadways, curbsides, and hold lots). Figure 5-2 shows an example of how the Port Authority of New York and New Jersey used and distilled multiple data sources into a single page. These summaries were used to monitor traffic conditions and service levels on LaGuardia Airport’s ground transportation system while the airport was under continual disruption due to construction. The sheet includes information on total vehicle volumes at key locations, commercial ground transportation activity, public transit performance, public parking accumulations, construction activity, and airport conditions (e.g., weather and flight delays) that could have affected ground transportation activity. Data provide evidence and support airport staff ’s interactions with stakeholders, senior man- agement, the board, and the public. By analyzing data and providing clear evidence, airport operators can make a stronger case for implementing specific solutions, strategies, or improve- ments, which is especially important when dealing with complex issues or seeking to allocate resources effectively. Additional examples of how airports have used data-driven problem solving are provided in Section 6.4.

(Source: Port Authority of New York and New Jersey, presented at AAAE Parking and Landside Management Workshop, September 13, 2017.) Figure 5-2. Daily report summarizing key ground transportation data, LaGuardia Airport.

Next: Chapter 6 - Summary of Case Examples from Airports »
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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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