An exploratory data analysis of airport wait times using big data visualisation techniques

Sankaranarayanan, Hari Bhaskar; Agarwal, Gaurav; Rathod, Viral

doi:10.1109/csitss.2016.7779379

Cited by 8 publications

(3 citation statements)

References 7 publications

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“…Newer methods, such as machine learning, are constrained by the hidden knowledge and insights in the data [11]. To address these issues, it is proposed to adopt analytical methodologies such as descriptive statistics [13] and exploratory data analysis (EDA) [14].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Influence of the International Civil Aviation Organization on Aircraft Accident Rates and Fatalities: A Seven-Decade Historical Data Analysis

Passarella,

Veny,

Fachrurrozi

et al. 2023

ATAMS

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The advent of air travel, originally proposed by the Wright brothers, has led to a significant surge in aircraft usage for human transportation. In its nascent stages, this mode of transport was linked with a high frequency of accidents and consequent fatalities, placing it in the high-risk category. To counter these risks, the International Civil Aviation Organization (ICAO) was established in 1947 as a collaborative effort among numerous countries with the primary goal of enhancing aviation safety regulations. This study analyzed archival data from the Bureau of Aircraft Accidents Archives (B3A), covering a span of 72 years from 1918, the year of the first commercial airplane crash, until 2020. The objective was to understand the ICAO's impact on altering accident rates, fatalities, and underlying causes. Analytical methodologies encompassed both descriptive statistics-examining data distribution, central tendencies, and category frequencies-and exploratory data analysis (EDA) to identify variable relationships and outlier identification. The results indicated that ICAO's interventions have led to a notable decline in accident rates, with an annual average reduction of 70.9%, and a corresponding decrease in incidents attributed to technical factors. However, an unexpected trend was the increase in fatalities despite the drop in accident numbers, attributable to the introduction of larger aircraft designs carrying more passengers per flight. The findings underscore the ICAO's successful efforts in reducing aircraft accidents, but also suggest a need for further exploration into factors contributing to the rise in fatalities.

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Section: Introductionmentioning

confidence: 99%

Evaluating the Influence of the International Civil Aviation Organization on Aircraft Accident Rates and Fatalities: A Seven-Decade Historical Data Analysis

Passarella,

Veny,

Fachrurrozi

et al. 2023

ATAMS

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show abstract

“…Over the last few decades, academics have introduced various tools and techniques to visualize hidden correlations among data variables using simplistic diagrams and charts [2]- [8]. Visual data analysis aid domain-specific data interpretation such as analysis of CRISPR/Cas9 screens [2], analysis of container shipping slot bookings [9], analysis of executive functions during childhood [10], analysis of kindergarten students log data [11], sodium and potassium coronate stability [12], fault injection campaigns [13], employee demographics and earnings [14], airport waiting times [15], analysis of medical data [16] to perform analytics tasks, and analysis of Airbnb's super host profile [17]. Crime is a risk that must be faced and managed.…”

Section: Introductionmentioning

confidence: 99%

Exploratory data analysis of crime report

Setiawan

Suprihanto

2021

MATRIX

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Visualization of data is the appearance of data in a pictographic or graphical form. This form facilitates top management to understand the data visually and get the messages of difficult concepts or identify new patterns. The approach of the personal understanding to handle data; applying diagrams or graphs to reflect vast volumes of complex data is more comfortable than presenting over tables or statements. In this study, we conduct data processing and data visualization for crime report data that occurred in the city of Los Angeles in the range of 2010 to 2017 using R language. The research methodology follows five steps, namely: variables identification, data pre-processing, univariate analysis, bivariate analysis, and multivariate analysis. This paper analyses data related to crime variables, time of occurrence, victims, type of crime, weapons used, distribution, and trends of crime, and the relationship between these variables. As the result shows, by using those methods, we can gain insights, understandings, new patterns, and do visual analytics from the existing data. The variations of crime variables presented in this paper are only a few of the many variations that can be made. Other variations can be performed to get more insights, understandings, and new patterns from the existing data. The methods can be performed on other types of data as well.

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“…There has been little research in the systematic analysis of passenger wait times at customs across airports. Sankaranarayanan et al [10] performed an exploratory analysis of airport wait times on customs, border protection data taken from top 3 busiest airports (Atlanta, Chicago, and Los Angeles) from the United States, highlighting the effects of seasonality. Johnstone et al presented a dynamic queue controller to generate realistic queue formation and behaviour within a discrete event environment at airports in Australia [11].…”

Section: Introductionmentioning

confidence: 99%

Doorway to the United States: An Exploration of Customs and Border Protection Data

Monmousseau

Marzuoli

Bosson

et al. 2019

2019 IEEE/AIAA 38th Digital Avionics Systems Conference (DASC)

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This paper presents a data-driven study of wait time patterns for international arriving passengers across all 61 terminals from the 44 airports of entry of the United States. Each airport is an independent entity which operates with various airlines and handles demand volumes differently. This induces seasonal variation in service quality from one airport to another. Exploring six years worth of data, this paper investigates the current and long-term performance trends-an increasing number of flights versus a decreasing number of customs booths-of all airports of entry from a passenger perspective. A performance analysis is then conducted that compares average wait times of incoming passengers, considering incoming traffic ratios and allocated resources. Leveraging machine learning algorithms, six regression algorithms are trained and tested to accurately predict passenger wait times through customs at selected airports. An analysis of the performance of these models shows that the best approach-using a Gradient Boosting regressor for each terminal of entry-can capture the daily and seasonal variations of traffic patterns and immigration booth availabilities with a mean absolute error of less or equal to 5 minutes for twentyeight terminals of entry and less than 10 minutes for all terminals. Observations show significant disparities across airports that may be explained by the foreign/US passenger ratio and the quality of booth management.

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An exploratory data analysis of airport wait times using big data visualisation techniques

Cited by 8 publications

References 7 publications

Evaluating the Influence of the International Civil Aviation Organization on Aircraft Accident Rates and Fatalities: A Seven-Decade Historical Data Analysis

Evaluating the Influence of the International Civil Aviation Organization on Aircraft Accident Rates and Fatalities: A Seven-Decade Historical Data Analysis

Exploratory data analysis of crime report

Doorway to the United States: An Exploration of Customs and Border Protection Data

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