Nate Bailey

About Me

Hello! I am a recent Ph.D. graduate from the Interdepartmental Program in Transportation at MIT. During my time at MIT, I was a member of the JTL-Transit Urban Mobility Lab and collaborated on projects for Transport for London. My thesis advisor was Professor Jinhua Zhao, and the other members of my dissertation committee were Professor Patrick Jaillet and Dr. Paolo Santi.

I am actively seeking career opportunities in the transportation sector that enable me to harness my data science skillset and my domain knowledge of transportation to work on projects that create and enable more sustainable transportation systems. I have extensive experience working with large datasets and applying a variety of tools including optimization and simulation approaches as well as modeling and machine learning methods. Please email me at natekbailey [at] gmail [dot] com if you are interested in working together!

In my dissertation, I investigated how travel time uncertainty impacts dynamic ridesharing (DRS) systems (like UberPool or Lyft Shared). My research found that the lack of trust in DRS systems' travel time reliability is a major contributor to passengers' reluctance to use these services instead of their exclusive trip counterparts (UberX or regular Lyft). I also developed novel optimization methods for online assignment of passengers to vehicles under uncertain travel times, and demonstrated the improvement of this method over existing approaches using an agent-based simulation tool written in Python. research interests include shared mobility, autonomous vehicles, simulation, and optimization.

I received my Masters of Science in Transportation degree from MIT in 2016 for research using simulation to model the traffic impacts of autonomous vehicles and optimizing network supply to maximize the benefits of those impacts. Before MIT, I did my undergrad in Industrial Engineering and Operations Research at UC Berkeley. (Go Bears!) I graduated in May 2014. While at Berkeley, I did research with PATH, where I worked on projects relating to highway traffic estimation, simulation, and forecasting.

Research

Interests/Topics:

Sustainable transportation systems, especially public transit
Data science
Optimization methods in transportation
Simulation and simulation-based optimization
Behavioral modeling

Publications:

Ph.D. Dissertation (2022). Dynamic Ridesharing under Travel Time Uncertainty: Passenger Preference and Optimal Assignment Methods
Wang, S., Q. Wang, N. Bailey, & J. Zhao (2021). Deep Neural Networks for Choice Analysis: A Statistical Learning Theory Perspective. Transportation Research: Part B
Moody, J., N. Bailey, & J. Zhao (2019). International Comparison of Perceptions of Autonomous Vehicle Safety. Safety Science
Master's Thesis (2016). Simulation and Queueing Network Formulation of Mixed Automated and Non-automated Traffic in Urban Settings

Conference Presentation and Proceedings:

Bailey, N., P. Noursalehi, & J. Zhao. (2021). Ridehailing Preference under Travel Time Uncertainty. At Transportation Research Board 100th Annual Meeting (No. 21-02827).
Moody, J., N. Bailey, & J. Zhao (2019). Public Perceptions of Autonomous Vehicle Safety: An International Comparison. At Transportation Research Board 98th Annual Meeting (No. 19-05012).
Bailey, N., A. Rosenfield, & J. Zhao. (2018). Attitudes Towards Effective Time Use in Autonomous Mobility-on-Demand Services in Singapore. At Transportation Research Board 97th Annual Meeting (No. 18-00783).
Bailey, N., T. P. Doyle, T. Ogunbekun, & J. Zhao. (2016). A Ride to Remember: Experienced Vs. Remembered Emotion on Public Transit. At Transportation Research Board 95th Annual Meeting (No. 16-5341).
Bailey, N., C. Osorio, A. Antunes, L. Vasconcelos (2015). Designing Traffic Management Strategies for Mixed Urban Traffic Environments with Both Autonomous and Non-Autonomous Vehicular Traffic. At Mobil.TUM 2015

Other Work

Three-Point Shooting Analysis with NBA Movement Data (Poster, Report): Research project for MIT graduate course "Statistics, Computation, and Applications" (taught by Professors Stefanie Jegelka and Caroline Uhler) in which my teammates and I sought to use publicly available NBA player movement data from the 2015-16 season to determine what factors lead to more successful three-point shooting and what types of player archetypes we could identify by clustering players' aggregated movement.
SimuTix (Demo): Simulation project I worked on in undergrad which aimed to help airlines make staffing decisions for check-in counters based on their flight schedule for the upcoming day. It includes a basic algorithm for simulation-based optimization.
A Ride to Remember: Research project for MIT graduate course "Behavior and Policy: Connections in Transportation" (taught by Jinhua Zhao) which was submitted to TRB for presentation by Professor Zhao, Tolu Ogunbekun, Patton Doyle, and me. This paper focuses on surveys performed on the Red Line to analyze how peoples' memory of their emotions during public transit trips differs from what they report during the trip itself.