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 Modelling	Lecture
29	Writing Simulations in Python	Tutorial
30	Consumer Behavior Theory	Lecture
31	Consumer Behavior Simulation	Lecture
32	Behavioral Simulation in Python	Tutorial
33	Macroscopic Traffic Theory	Lecture
34	Macroscopic Traffic Simulation	Lecture
35	Macroscopic Traffic Simulation in Python	Tutorial
36	Microscopic Traffic Theory	Lecture
37	Microscopic Traffic Simulation	Lecture
38	Microscopic Traffic Simulation in Python	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