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The existing curriculum and models for civil engineering graduate programs assume that graduating Ph.D. students will primarily pursue career opportunities in research or academia. However, the number of civil engineering Ph.D. graduate students continues to increase, while the number of opportunities in academia for civil engineers remains stagnant. As a result, it is becoming increasingly apparent that the civil engineering graduate programs must be reevaluated to assist students entering industry after graduation. As part of a larger research study funded through the NSF Innovations in Graduate Education (IGE), we aim to answer the following research questions: 1) How can a research-to-practice model assist students in preparing for a transportation engineering career outside of academia?, 2) What impacts does the research-to-practice graduate model have on the development of transportation engineering doctoral students’ professional identity?, 3) How does the cognitive apprenticeship framework prepare doctoral students for professional practice in transportation engineering?, and 4) What influences does the research-to-practice model have on doctoral students’ motivation toward degree completion? As part of the first phase for the project, two surveys were developed: a graduate engineering student motivation survey based on Expectancy-Value-Theory, and an instrument based on the Cognitive Apprenticeship framework. The motivation survey was based on an instrument designed and validated by Brown & Matusovich (2013) which aimed to measure undergraduate engineering students' motivation towards obtaining an engineering degree. The survey prompts were reviewed and rewritten to reflect the change in context from undergraduate to graduate school. Revised survey prompts were reviewed with a group of graduate engineering students through a think aloud protocol and changes to the instrument were made to ensure consistency in interpretation of the prompts (Rodriguez-Mejia and Bodnar, 2023). The cognitive apprenticeship instrument was derived from the Maastricht Clinical Teaching Questionnaire (MCTQ), originally designed to offer clinical educators feedback on their teaching abilities, as provided by medical students during their clerkship rotations (Stalmeijer et al., 2010). To tailor it to the context of engineering graduate students, the MCTQ's 24 items were carefully examined and rephrased. A think aloud was conducted with three civil engineering graduate students to determine the effectiveness and clarity of the cognitive apprenticeship instrument. Preliminary results show that minimal clarification is needed for some items, and suggestions to include items which address support from their mentors. The other part of the project assessment involves students completing monthly reflections to obtain their opinions on specific events such as seminars or classes, and identify their perceptions of their identity as professionals, scientists, or researchers. Preliminary results suggest that the students involved place an emphasis on developing critical thinking and planning skills to become an engineering professional, but de-emphasize passion and enjoyment. This paper will report on initial findings obtained through this first phase of the IGE project. 

Phase of flight (POF) prediction estimates the future state of aircraft along planned trajectories, allowing the prediction of potential conflicts as well as optimization of air space, controlled by the Federal Aviation Administration. In this paper, we present a study conducted to develop three different POF forecasting machine learning models and a statistical regression model using four-dimensional GPS and RADAR Track data from 57 flights provided by an En Route Computer System. The investigated machine learning models include Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), Support Vector Machine (SVM), and Neural Ordinary Differential Equations (NODE). These were developed to forecast the horizontal and vertical POF of the current aircraft for the next time step. The results in this study indicate that LSTM-RNN models are more suitable for POF prediction than SVM and statistical regression models, with NODE being a promising model for future trajectory prediction research.