Transportation Engineering 201

Course Curriculum

Chapter 1: PTV VISUM – Travel Demand Modeling

Lecture 1: Downloading Software

Lecture 2: Finding location using Map

Lecture 3: Creating Zones

Lecture 4: Practice Creating Zones

Lecture 5: Creating Links – Part 1

Lecture 6: Creating Links – Part 2

Lecture 7: Connectors

Lecture 8: Network Consistency

Lecture 9: Trip Generation

Lecture 10: Creating Skim Matrices

Lecture 11: Trip Distribution

Lecture 12: Trip Assignment

Chapter 2: Aimsun Traffic Simulation

Lecture 1: Getting Started with Aimsun

Lecture 2: Importing Network

Lecture 3: Importing Network – Part 2

Lecture 4: Creating Centroids

Lecture 5: Creating OD Matrix

Lecture 6: Creating Traffic Demand, Scenario, Replication, Fixing Errors

Lecture 7: Calibration & Validation

Chapter 3: Introduction to Linear Regression using MS Excel

Lecture 1: Problem for Data Modeling – Parking Duration vs Building Area & Number of Floors

Lecture 2: What is a Variable? What are Independent & Dependent Variables?

Lecture 3: Inserting and Interpreting Trendline in MS Excel

Lecture 4: Finding Residuals

Lecture 5: What is Mean, Variance and Standard Deviation?

Lecture 6: What are Distributions in Statistics?

Lecture 7: Variance of Residuals Equals Variance of Dependent Variable

Lecture 8: Distribution of Residuals – Is it Normal?

Lecture 9: How to find Confidence Interval for the Observed Values?

Lecture 10: What is Z Score?

Lecture 11: More about Z score

Lecture 12: Interpreting Linear Regression Model

Lecture 13: Hypothesis Testing

Lecture 14: Two Sided Hypothesis Testing Using Real Example of Road Traffic Injury

Lecture 15: One Sided Hypothesis Testing Using Real Example of India and Sri Lanka

Lecture 16: Conclusion of First Section

Chapter 4: Finding Linear Regression Model using R programming

Lecture 1: Downloading R programming and R Studio

Lecture 2: Importing and Reading the Data in R Studio

Lecture 3: Creating a Linear Regression Model in R

Lecture 4: Finding Fitted Values, Residuals and Variance & Creating Histograms

Lecture 5: Test whether dependent variable really depends on independent variable or not?

Lecture 6: 95% Confidence Interval of Beta 1

Lecture 7: Working on a Real Data and Interpreting it

Chapter 5: Identifying the Outliers in the Data

Lecture 1: What are Outliers?

Lecture 2: Finding Outliers in R using Stem Leaf Plot

Lecture 3: Finding Outliers in R using Box Plot

Lecture 4: Finding Outliers using Semi-Studentized Residuals

Chapter 6: Testing the Assumptions of Linear Regression

Lecture 1: Introduction

Lecture 2: Normal Q-Q Plot for Residuals

Lecture 3: Correlation Test in R to check Normality of Residuals

Lecture 4: Plot between Residuals and Independent Variables

Lecture 5: Plot between Residuals and Fitted Value

Chapter 7: Linear Regression with Multiple independent Variables

Lecture 1: Selecting the Parameters

Lecture 2: Creating a Multiple Variable Model

Lecture 3: Finding the Best Subset Model

Lecture 4: How to Check whether your Model is Correct or Not?

Lecture 5: Coming Back to Original Question – How to Design Optimal Parking Space?

Chapter 8: MNL Modeling

Lecture 1: Discrete Choice Theory

Lecture 2: Case Study of Carpooling in Bengaluru

Lecture 3: Designing the Survey

Lecture 4: How much data to be collected?

Lecture 5: Types of Variables

Lecture 6: Preparing data for analysis

Lecture 7: Role of Gender in Choosing Carpooling

Lecture 8: Role of Age in Choosing to Carpool

Lecture 9: Role of Belief in Choosing to Carpool

Lecture 10: Final MNL Model