Transportation Engineering 201

Transportation Engineering 201, available at $49.99, has an average rating of 4.45, with 67 lectures, 9 quizzes, based on 16 reviews, and has 166 subscribers.

You will learn about Important Properties of Residuals Linear Regression using MS Excel Hypothesis testing Linear Regression using R programming Finding outliers of the data Testing assumptions of Linear Regressions Linear Regression in Multiple Variables Multinomial logit modeling Traffic simulation travel demand modeling This course is ideal for individuals who are Transportation Planners or Beginners in R programming or Beginners in Data Science or Beginners in Linear Regression or Transportation Engineers or Civil Engineering Students It is particularly useful for Transportation Planners or Beginners in R programming or Beginners in Data Science or Beginners in Linear Regression or Transportation Engineers or Civil Engineering Students.

Enroll now: Transportation Engineering 201

Summary

Title: Transportation Engineering 201

Price: $49.99

Average Rating: 4.45

Number of Lectures: 67

Number of Quizzes: 9

Number of Published Lectures: 66

Number of Published Quizzes: 9

Number of Curriculum Items: 83

Number of Published Curriculum Objects: 82

Original Price: ?799

Quality Status: approved

Status: Live

What You Will Learn

Important Properties of Residuals

Linear Regression using MS Excel

Hypothesis testing

Linear Regression using R programming

Finding outliers of the data

Testing assumptions of Linear Regressions

Linear Regression in Multiple Variables

Multinomial logit modeling

Traffic simulation

travel demand modeling

Who Should Attend

Transportation Planners

Beginners in R programming

Beginners in Data Science

Beginners in Linear Regression

Transportation Engineers

Civil Engineering Students

Target Audiences

Transportation Planners

Beginners in R programming

Beginners in Data Science

Beginners in Linear Regression

Transportation Engineers

Civil Engineering Students

This course has been designed for Transportation Engineers, Planners, Traffic Engineers who are absolute beginners in Data Science field. You don’t need to have any background in Statistics or coding in order to take this course. In the first section, I have explained all the necessary terms related to linear regression through actual examples using data from a Parking Study. If you are not familiar with it, it is still okay as things are taught from scratch and emphasis has been given to data analysis.

Later, we discuss R programming commands to implement the same thing as taught in Section 1. I have assumed that you have a zero experience in R and hence I have explained even the most basic things.

Then, we deep dive into data analysis part and making sure that we can actually model it using linear regression. We also discuss several assumptions such as normality, linearity and constant variance of the error terms. I have shown how to check whether our model is satisfying those assumptions or not.

Lastly, we discuss models with more than one variables. I have given steps to identify suitable variables for the model.

The philosophy behind this course is to provide you with an introduction. As a Transportation Engineer, you might be curious to learn about Data Science but may not have been able to do so because of hard mathematics and coding requirements. I have broken down complex concepts and explained them here through real life applications from transportation industry to enable you to learn it.

This course helps you become strong in fundamental concepts of Linear Regression and Data Science in general. It is not very heavy in coding or mathematics.

From there onwards, we take a deep dive into Aimsun software which is used for traffic simulation. Learning this software is a skill which can be useful in the industry as well as research. I have also explained PTV VISUM basics using which 4-step modeling can be done.

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

Instructors

Transport Studies
Gives you edge over colleagues

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4 stars: 11 votes

5 stars: 5 votes

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