Akademik Dersler - Tanıtım

Course Introduction Information


Code - Course Title	İNŞ4502 - Transportation Data Collection and Analysis
Course Type	Area Elective Courses
Language of Instruction	İngilizce
Laboratory + Practice	3+0
ECTS	4.5
Course Instructor(s)	ÖĞRETİM GÖREVLİSİ DOKTOR PINAR BİLGİN TENGİLİMOĞLU
Mode of Delivery	Face-to-face
Prerequisites	There are no prerequisites or co-requisites for this course.
Courses Recomended	Students who are taking or will take this course are also advised to take the course 'İNŞ 472 Introduction to Traffic Engineering'.
Required or Recommended Resources	Currin, T. R. (2013). Introduction to Traffic Engineering: A Manual for Data Collection and Analysis (2nd ed.). Cengage Learning.Washington, S., Karlaftis, M., Mannering, F., & Anastasopoulos, P. (2020a). Statistical and Econometric Methods for Transportation Data Analysis. Chapman & Hall/CRC. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics. McGraw-Hill.
Recommended Reading List	Field, A. (2013). Discovering statistics using SPSS.Emniyet Genel Müdürlüğü Trafik Başkanlığı, İstatistikler. Accessed at https://trafik.gov.tr/istatistikler37. Kuflik T; Minkov E; Nocera S; Grant-Muller S; Gal-Tzur A; Shoor I (2017) Automating a framework to extract and analyse transport-related social media content: The potential and the challenges, Transportation Research Part C: Emerging Technologies, 77, pp.275-291. doi: 10.1016/j.trc.2017.02.003Caceres, N., Romero, L.M., Benitez, F.G., del Castillo, J.M. (2012) ‘Traffic Flow Estimation Models Using Cellular Phone Data’, IEEE Transactions on Intelligent Transportation Systems, 13, (3), pp.1430-1441
Assessment methods and criteria	4 homeworks: 1. hw: Data collection, descriptive statistics and graphical presentation 2. hw: Analysis of data using linear regression models and reporting of results 3. hw: Analysis of data using non-linear regression models and reporting of results 4. hw: Analysis of panel data using panel data analysis methods and reporting of results1 mid-term (open-ended questions)1 final (open-ended questions)
Work Placement	No internship or practical training is required for this course.
Sustainability Development Goals

Content

Weeks	Topics
Week - 1	Description of transport data, Data collection methods, Data collection considerations, Data storage and management
Week - 2	Trip matrix data
Week - 3	Traffic data
Week - 4	Inventory and condition data, Environment and sustainability data
Week - 5	Introduction to data analysis (basic definitions, data types)
Week - 6	Descriptive statistics and graphical presentation
Week - 7	Hypothesis testing
Week - 8	Hypothesis testing
Week - 9	Linear regression models, fundamental assumptions, model diagnostic tests and applications in transport
Week - 10	Linear regression models, fundamental assumptions, model diagnostic tests and applications in transport
Week - 11	Non-linear regression models, fundamental assumptions, model diagnostic tests and applications in transport
Week - 12	Non-linear regression models, fundamental assumptions, model diagnostic tests and applications in transport
Week - 13	Introduction to advanced econometric modelling techniques and their applications in transport
Week - 14	Introduction to advanced econometric modelling techniques and their applications in transport

Learning Activities and Teaching Methods

Teaching Methods
Lecture
Discussion
Question & Answer
Team/Group Work
Case Study
Problem Solving
Report Preparation and/or Presentation
Competences
Productive
Questoning
Follow ethical and moral rules
Effective use of a foreign language
Work in teams
Use time effectively
Eleştirel düşünebilme
Abstract analysis and synthesis
Problem solving
Applying theoretical knowledge into practice
Concern for quality
Information Management
To work autonomously
Organization and planning
Elementary computing skills
Decision making

Assessment Methods

Assessment Method and Passing Requirements
	Quamtity	Percentage (%)
Toplam (%)		0