Lecturers for Transportation, Technology and Policy, Academic year 2021-2022
The UC Davis Institute of Transportation Studies seeks to recruit a pool of qualified lecturers to teach graduate-level courses through its Transportation Technology & Policy (TTP) graduate group during 2021-2022. Transportation Technology and Policy (TTP) is an interdisciplinary Graduate Group administered through the Institute of Transportation Studies (ITS). It offers three degrees: MS Plan I, MS Plan II, and Ph.D. The TTP program provides an opportunity to do interdisciplinary research to address pressing transportation, environmental, economic, policy and social challenges facing California, the United States, and the world with students coming from a variety of disciplines to pursue either a technology or policy track. Candidates must have an appropriate professional degree, e.g. doctorate degree; demonstrated teaching ability; a record of scholarly achievement in an area of expertise related to the subject area of transportation research and instruction. Transportation Technology & Policy (TTP) is an Interdisciplinary Graduate Group program offering Master's and Ph.D. degrees. The TTP graduate curriculum draws on a multitude of academic disciplines and the group utilizes participating faculty and temporary faculty to staff courses to maintain a top-quality academic program. Criteria for appointment and reappointment will be evidence of teaching excellence (or, for first-time, relatively inexperienced candidates, the potential for excellence) in terms of the ability to present course material effectively, e.g., stimulating interest in and critical thought regarding the subject matter. Expertise in the subject matter will be evaluated based on the candidates' letter of interest, current curriculum vitae, professional experience, teaching evaluations, training, and other evidence of professional attainment.
Courses open for recruitment, Fall 2021:
Course Title: Discrete Choice Modeling Course Description: The course will cover the behavioral, statistical, and econometric foundations for the formulation and estimation of discrete choice models, and will present a variety of discrete choice models and their application to travel demand forecasting and related subjects. Discussion of behavioral theories and random utility theory, econometric theory, hypothesis testing, maximum likelihood estimation, binary logit and probit models, multinomial logit model, nested logit, deterministic and probabilistic (e.g. latent-class) segmentation. Students will gain experience in the formulation, estimation, interpretation, and evaluation of discrete choice models using empirical data.
Courses Open for Recruitment, Winter 2022:
Course Title: Energy and Transportation Modeling for Policy Analysis Course Description: The course will familiarize students with building energy and transportation models for policy analysis. Energy systems modeling covers a wide gamut of energy sectors and some of the most important elements (transportation, electricity, fuels, resources, infrastructure) will be reviewed in the course. The primary aim of the course will be on understanding the elements and techniques for modeling energy and transportation systems as they relate to relevant policy actions. The students will be introduced to several genres of energy models and will be required to complete a number of model building exercises using Excel, other tools introduced in class, or developed by students based on his/her own skills (some level of computer programming would be helpful, but not absolutely required, for this class). Students will become familiar with forecasting energy use and demands, gain experience of building techno-economic models, and develop skills for policy analysis. Assignments will draw on real-life policy problems in addressing challenges in transportation and energy systems.
Course Title: Fundamentals of Transportation Technology Course Description: This course will cover the fundamentals of transportation technology: How the technology works and how we evaluate this technology. Topics include Engines and Drive Trains across different transportation modes and segments, Fuels and Fuel Pathways, Emissions and After-treatment technologies, Efficiency and the fundamental forces (f=ma, friction, drag, basic thermodynamics) that affect it, Electricity (AC/DC, sources of generation, charging, how the grid operates), Batteries and other forms of energy storage, Recycling and Waste Management. We will learn methods to evaluate technology, such as well-to-wheels Life Cycle Analysis, Total Cost of Ownership models, Fleet Turnover Models, Cost/benefit Analysis. This is not an engineering course. It presents a perspective on technology that is useful for understanding and addressing problems, without the hard math. We aim to understand how the technology works in different contexts and how it affects emissions, what are the strengths, the limitations, and the challenges of these technologies. What the tradeoffs are. This course will prepare the student for a research and/or decision making career in industry, academia, non-profit, or government. This is emphasized through case studies of the interface between policy and science and homework questions and class examples specifically geared toward the understanding of technical vs. political difficulties and the interface between them.
Courses open for recruitment, Spring 2022:
Course Title: Applied Data Analysis: Course Description: This course aims to provide students with the resources needed to examine, parse, and analyze datasets (with a specific aim for answering research questions). This data analysis course covers a variety of concepts across disciplines of economics, statistics, and machine learning but with a specific emphasis on application. The concepts in the class include the exploration of data, gathering and cleaning of data. The course delves into basic data analysis operations, including basics of examining and inspecting data (identifying data types, dealing with missing data and outliers, maintaining data integrity). We will cover a range of regression analysis including parametric (OLS), semi-parametric (logistic), and non-parametric (GLM, kernel regressions) regressions. Lastly, the students will apply the learned techniques to real data. The class will cover a variety of datasets as examples to demonstrate how to use the software tools.
UC Davis is the home of the Aggies — go-getters, change makers and problem solvers who make their mark at one of the top public universities in the United States. Since we first opened in 1908, we’ve been known for standout academics, sustainability and Aggie Pride as well as valuing the Northern California lifestyle. These themes are woven into our 100-plus-year history and our reputation for solving problems related to food, health, the environment and society.Our 5,300-acre campus is in the city of Davis, a vibrant college town of about 68,000 located in Yolo County. The state capital is 20 minutes away, and world-class destinations such as the San Francisco Bay Area, Lake Tahoe and the Napa Valley are within a two-hour drive.