CE5540

Objectives:

1. To identify data needs for various transportation engineering applications.
2. To apply mathematical concepts for analysing data from real-world applications.
3. To employ programming tools for developing, implementing, and evaluating models in transportation engineering.
4. To interpret model results for informed decision-making in transportation engineering.
5. To develop technical reports with compelling data analysis, sophisticated models, and compelling visualizations.

Course Content:

1. Transportation Probabilistic Analysis: Probability Theory – fundamental concepts, properties of common distributions observed in transportation engineering; Statistical Inference – hypothesis testing, statistical errors; Software – write your own code in R.
2. Transportation Data Analysis:– Foundations – data types, exploratory data analysis and data visualization; Regression – model estimation and diagnostics; Validation and Inference – model validation and interpretation of results; Case Studies – real-world applications in transportation engineering; Software – write your own code in R.
3. Computer Methods and Applications: Foundations – principles of simulation models, macroscopic and microscopic simulation models for transportation engineering; Modelling – data requirements, model calibration and validation, mathematical formulations, and solution approaches for simulating transportation models; Software – write your own code in Python/Julia.

Textbooks:

NA

Reference Books:

1. Washington et al. (2001). Scientific Approaches to Transportation Research Volumes 1 and 2. NCHRP 20-45.
2. Stark, P. B. SticiGui – Online Statistical Textbook.
3. Grimson, E. & Guttag, J. (2008). Introduction to Computer Science and Programming.
4. Sheffi, Y. (1985). Urban transportation networks (Vol. 6). Prentice-Hall, Englewood Cliffs, NJ.

Lectures: