Project Abstract

This project aims to create a prototype of a departing passenger profile curve to help EWR Terminal B proactively manage its terminal frontage, baggage/check-in, and TSA queues. Using data from various stages in a passenger’s journey at our airport terminal, we hope to estimate when and where passengers will be throughout their airport journey. The model should consider industry knowledge of passenger dwell times and other passenger preferences. Understanding how passengers interact with our terminal will allow EWR Terminal B management to highlight pain points in their journey and plan for future improvements in design or technology.

Project Description & Overview

Air passenger travel behavior has become harder to predict following COVID-19. Having a clearer picture of when passengers arrive for departing flights, how long they wait for security inspections, and how they travel through the terminal will help airport operations team enact solutions (I.e. wayfinding, queue management, staff deployment, capital construction) to better improve customer experience at our airports. Prior to 2019, passenger show-up profiles, as well as general trends observed by industry experts, provided a reasonable model of how passengers flowed through the terminal. We hope this new prototype can help update these assumptions to reflect post-pandemic travel.

Using collected data from numerous points in the passenger journey through the airport (“from curb to gate”), we would like to build departing passenger profiles to create near-term and long-term passenger flow predictions and profiles. Our focus will be on EWR Terminal B (the only terminal that the Port Authority both manages and operates). We have insight into the use of terminal frontage, check-in counters, security checkpoints, and gates, but we have not been able to connect these disparate data sources to create an estimated passenger profile.

This prototype model and accompanying paper should answer the following questions:

• When do passengers arrive at the airport? Why?
  • How does seasonality or weather affect these times?
  • Does this change depend on the type of passenger or where they are flying?
• Where do passengers dwell in the airport? Why?
• Can we predict pain points in the airport based on who is departing/arriving?

Datasets

We will be using various datasets available and regularly used by the data analytics team. These include, official detailed airline flight schedules, PA operational historical flight data, TSA throughput and wait times, CBP throughput and wait times, FHV (for-hire vehicles) frontage data, NYC TLC taxi data, Newark Terminal B baggage scanner data, PA Air Train hourly data, historical passenger survey data, MTA subway, and bus data, official publicly available government data from the DOT and FAA, official reported PA passenger counts, and an internal departing passenger TSA throughput predictive model output.

All the following datasets are available in our centralized data warehouse or accessible through various teams across the department:

• Official airline flight schedules
  • Arrival/Departure times
  • Seat counts
• PA operations historical data
  • Delays, cancelations, taxi times
• PA TSA Throughput and Wait Times
  • Broken out by checkpoints
  • Official throughput values also included
• PA CBP Throughput and Wait Times
• FHV Frontage Data
• NYC TLC Taxi Data
• EWR TB Baggage Scanners
• PA AirTrain hourly usage
• ACI Surveys
• MTA Subway and Bus data
• DOT O-D data
• PA Official PAX counts
• PA Departing Passenger Predictive Model

Competencies

• GIS experience
  • We would like a prototype map/simulation of what this could look like. We have shapefiles to provide.
• Basic data analytics capabilities (in Python or R)

Learning Outcomes & Deliverables

• Week/Month long simulation of passenger throughput at the terminal. Highlight hotspots of passengers at the airport through a day.
• Analysis/paper into the different types of passengers and their interactions with our airport.
  • This analysis should help us determine where and how we can improve operations at the airport.
• Recommendations to applying similar methods to other terminals
• Learning outcome: Machine learning experience in a business setting, creating assumptions and backing them up with evidence and building a platform to carry to the rest of the department

Students

Junning Guo, Yuki Mitsuda, Gabe Pincus, Solomane Sirleaf