Forecasting Airport Passenger Flow during Periods of Volatility: Comparative Investigation of Time Series vs. Neural Network Models

Artificial Intelligence (AI) models, particularly neural networks, are infrequently utilized in the existing airport management literature for conventional forecasting of airport activities. The limited adoption of these models in the airport management literature might be influenced by their perceived complexity. This perception is likely derived from their common application in intricate tasks within the academic literature. Nevertheless, this research calls for a reevaluation of such perceptions and advocates for the inclusion of RNN and multivariate RNN in the forecasting toolkits of airport managers as credible alternatives to traditional time series models. This study endeavors to discern the forecasting performance of neural network models, providing insights into their effectiveness and applicability in addressing the complexities of passenger flow dynamics through a comprehensive evaluation of RNN, LSTM, GRU, Deep LSTM, BLSTM, multivariate RNN and multivariate LSTM, in comparison to standard time series models (ARIMA, SARIMA and SARIMAX). It was anticipated that the application of neural network techniques in TSA passenger flow v forecasting will yield heightened accuracy when compared to conventional standard time series models. Moreover, the integration of non-standard external factors was expected to enhance the forecasting performance of neural network-based models like RNN and LSTM, further distinguishing them from standard time series models. This investigation rigorously evaluates the robustness of these models by subjecting them to highly volatile historical data to forecast airport security checkpoint passenger flow at five prominent U.S. airports during the pandemic-induced challenges. At Atlanta\u27s Hartsfield-Jackson Airport (ATL), the forecasting precision of RNN notably exceeds that of SARIMA by 34% (DM= 3.44, p\u3c 0.01). This highlights the superior capacity of RNN to manage intricate interactions among variables, complex dependencies between factors and non-linear dynamics, thereby demonstrating its readiness for the emerging data-rich aviation environment. The incorporation of exogenous variables enhances the forecasting accuracies of the multivariate RNN/LSTM (DM=6.82,

Enhancing aviation safety: Uncovering human error patterns and mitigating risks

Human factors are the application of scientific insights concerning people and systems to optimize system performance. Given that human beings are integral to every aspect of systems, the potential for human errors stemming from stress, fatigue, or complacency is significant. In aviation, where errors can lead to catastrophic consequences, it is imperative to take comprehensive preventive measures. Our objective is to analyze major human factors issues contributing to aviation accidents, identifying patterns, and generating recommendations to enhance safety regulations and prevent future accidents. Additionally, we aim to assess the financial impact of accidents on the aviation industry. By thoroughly reviewing various accidents and their primary causes, the paper seeks to understand how to mitigate and reduce future incidents. Improved aviation safety benefits airlines, passengers, airports, governments, and regulatory agencies. They can utilize the findings of this paper to enhance aviation regulations and ensure a safer mode of transportation

Where in the World Am I?

Where in the World Am I? ️ Test your knowledge of famous monuments from around the globe in our exciting game, “Where in the World Am I?” Join us for three in-person sessions where you can compete to name iconic landmarks and win fantastic prizes. Challenge yourself, learn about different cultures, and have fun with friends. Don’t miss your chance to play and prove your global savvy

ERLI Virtual Open House

**Times are listed in U.S. Eastern Standard** Embry-Riddle Language Institute--ERLI--welcomes ERAU faculty, staff, and students to drop in to this Q&A session to learn how ERLI supports students, employees and their spouses who are speakers of other languages reach their academic goals

A Novel Approach To Modular Ramjet Inlets

Ramjet inlets are the main form of diffusion in supersonic engines. Meanwhile additive manufacturing is becoming to play a big role in the aerospace industry calling for both to be intertwined. This projects aims to test both a conventionally manufactured ramjet inlet and multiple additively ones to test flow separation and shockwave formation. The test section will also be modular to allow for easy swapping out of test objects and for future supersonic research

Tasty Treats from Around the World

Indulge in tasty treats from around the world! Stop by the atrium in the College of Business to sample a variety of candies that will tantalize your tastebuds

Jeopardy: International Edition

Think You Know the World? Prove It! at the International Jeopardy Challenge hosted by ERNIE Central! Enter for a chance to win an International Snack Box

Meet Exchange Students

Visit the Exchange Table during the Global Footprint Fair to meet our current exchange students and learn about how you could study overseas for a full semester or year

Open Doors 2024 Annual Data Release

Watch the release! The U.S. Department of State’s Bureau of Educational and Cultural Affairs and the Institute of International Education were pleased to release new findings from the Open Doors® Report on International Educational Exchange. Experts presented data on international student enrollments in the United States during the 2023/24 academic year and U.S. students studying abroad during the 2022/23 academic year. This event was held in conjunction with the worldwide observance of International Education Week and the 75th anniversary of the Open Doors report

Fostering Equity in Engineering Education

Students in introductory engineering courses face challenges communicating and integrating their ideas in team projects. Often, these challenges with team communication fall along gendered lines, where women students experience marginalization in team settings. This project builds from prior research in the field of engineering education, which integrated frameworks from the domains of engineering education and technical and professional communication to implement this research into a classroom intervention aimed at reducing the gendered disparity in these communication challenges. To help resolve these issues, this project utilizes a new research method called infrastructural rhetorical analysis to develop an educational intervention case study involving the experiences of women in the first-year engineering classroom to determine a concrete classroom intervention that aims to make the most difference with the least amount of resources needed to implement it