Transport Analytics
About Course
This course will provide students with a comprehensive understanding of transport analytics and its applications in the transportation industry. The course will cover various transportation modes, including road, rail, air, and maritime, and their respective analytics techniques. The students will learn how to analyze transportation data, identify patterns, and make informed decisions based on the insights gained. Additionally, students will explore the latest trends and emerging technologies in transportation analytics.
Course Outline:
- Introduction to Transport Analytics
- Definition of Transport Analytics
- Applications of Transport Analytics
- Importance of Transport Analytics
- Key Concepts in Transport Analytics
- Data Collection and Management for Transport Analytics
- Types of Data in Transport Analytics
- Data Collection Methods
- Data Preprocessing and Cleansing
- Data Storage and Retrieval
- Statistical Methods for Transport Analytics
- Descriptive Statistics and Exploratory Data Analysis
- Regression Analysis and Forecasting
- Time Series Analysis
- Machine Learning Techniques for Transportation Analytics
- Analytics for Different Transport Modes
- Road Transportation Analytics
- Rail Transportation Analytics
- Air Transportation Analytics
- Maritime Transportation Analytics
- Applications of Transport Analytics
- Route Optimization
- Capacity Planning
- Demand Forecasting
- Cost Optimization
- Customer Satisfaction
- Emerging Technologies in Transport Analytics
- Internet of Things (IoT) and Smart Transportation
- Artificial Intelligence (AI) in Transportation Analytics
- Blockchain in Transportation
- Predictive Analytics in Transportation
Prerequisites:
- Basic understanding of statistics and data analytics
- Familiarity with Excel and/or R programming language
Recommended Textbook:
- “Transportation Analytics: Tools and Applications” by Satish Ukkusuri.
Assessment:
- Midterm Exam (30%)
- Final Exam (40%)
- Group Project (30%)
Note: The course syllabus and assessment methods are subject to change based on instructor’s discretion.