CE5540 - Data Analysis and Computation Techniques for Transportation Engineering

CE5540 - Data Analysis and Computation Techniques for Transportation Engineering#

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

Module 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
Module 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
Module 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

Washington et al. (2001). Scientific Approaches to Transportation Research Volumes 1 and 2. NCHRP 20-45. http://onlinepubs.trb.org/onlinepubs/nchrp/cd-22/start.htm
Stark, P. B. SticiGui – Online Statistical Textbook. http://www.stat.berkeley.edu/~stark/SticiGui/Text/index.htm
Grimson, E. & Guttag, J. (2008). Introduction to Computer Science and Programming. http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00-introduction-to-computer-science-and-programming-fall-2008
Sheffi, Y. (1985). Urban transportation networks (Vol. 6). Prentice-Hall, Englewood Cliffs, NJ. http://web.mit.edu/sheffi/www/selectedMedia/sheffi_urban_trans_networks.pdf

S. No.	Topic	Session
01	Data	Lecture
02	Statistics	Lecture
03	Basics of R	Tutorial
04	Probability Theory	Lecture
05	Distributions	Lecture
06	Probability Analysis in R	Tutorial
07	Sampling	Lecture
08	Estimation	Lecture
09	Sampling in R	Tutorial
10	Hypothesis Testing	Lecture
11	Hypothesis Tests	Lecture
12	Assignment #1 Discussion
-	Quiz-I
13	Quiz-I Discussion
14	Multivariate Data	Lecture
15	Data Visualization	Lecture
16	Data Association	Lecture
17	Multivariate Data Analysis in R	Tutorial
18	Linear Regression - Foundations	Lecture
19	Linear Regression - Diagnostics	Lecture
20	Linear Regression in R	Tutorial
21	Logistic Regression - Foundations	Lecture
22	Logistic Regression - Diagnostics	Lecture
23	Logistic Regression in R	Tutorial
24	Symbolic Regression	Lecture
25	Assignment #2 Discussion
-	Quiz-II
26	Quiz-II Discussion
27	Setting up Python	Tutorial
28	Simulation Modeling	Lecture
29	Surplus Tutorial	Tutorial
30	Discrete Event Simulation	Lecture
31	Single-Server Queueing Systems	Lecture
32	Discrete Event Simulation in Python	Tutorial
33	Agent-based Simulation	Lecture
34	Car-Following Models	Lecture
35	Cellular Automata in Python	Tutorial
36	Digital Twin	Lecture
37	Surplus	Lecture
38	Introduction to Julia Programming Language	Tutorial
39	Assignment #3 Discussion
40	Surplus
-	End Sem

Grading for tutorials will be based on attendance, participation, and completion of tutorial problems.

S. No.	Content
1	Topics 01 - 11
2	Topics 14 - 24