Yleinen
Welcome to Traffic Studies and Forecasting course!
The course belongs to the advanced module of transportation and highway engineering R207-3 and to the introductory module of Master's Programme in Managing Spatial Change.
The course includes contemporary approaches to travel studies and travel modeling, with special attention to the analytical methods.
Some of the content this course will have includes:
- Modeling paradigms
- Data collection, sampling, errors
- Networks and zoning
- Model aggregation and transferability
- Trip generation modeling
- Trip distribution modeling
- Discrete choice models
- Traffic assignment modeling
- Commercial software exercises
- Activity-based modeling
After taking this course, you will be able to:
- evaluate models for forecasting travel demand and travel volumes
- describe basics of behavioral theories used for model formulation
- describe mathematical techniques used for model formulation and solution
- evaluate data management tools and techniques
- apply basic and advanced software solutions
- interpret scientific literature in travel modeling
Location: R8
Lecturer: Assistant Professor Milos Mladenovic, R340, milos.mladenovic@aalto.fi
Assistant: University Teacher Jouni Ojala, R337, jouni.ojala@aalto.fi
Office hours: Tuesday, 13:00 - 15:00
Course will include guest lecturers on specific topics.
ASSESSMENT
The assessment will consist of the following main parts:
- Assignments (60%)
- Wiki reflection (20%)
- Final exam (20%)
More information on these components can be found in their subpages.
ESSENTIAL LITERATURE
Ortuzar and Willumsen, 2011, Modelling Transport, 4th Edition, Wiley.
Sheffi, Yosef, 1985, Urban Transportation Networks: Equilibrium Analysis with Mathematical Programming Methods, Prentice Hall. (http://sheffi.mit.edu/sites/default/files/sheffi_urban_trans_networks.pdf)
A note on mathematical background: As this course bases significantly on use and development of mathematical models for representing travel and traffic behavior, you should note a certain level of mathematical prerequisites. This includes the knowledge of algebra and functions, matrix algebra, elements of calculus (e.g., differentiation, integration, logarithmic and exponential functions, finding min or max function values, etc.), and statistics (e.g., probability theory, randomness, statistical distributions, hypothesis testing, etc.).
A note on professionalism: As this is a graduate level course, students are expected to act professionally by attending all class meetings, arriving on-time and prepared, actively participating, making logical arguments substantiated by evidence, and respecting others. All work is due at the beginning of the class. Unless you receive an extension from the instructor, you will lose one full grade per day for every late work. Moreover, as peers in this class, you will also evaluate each others work for some activities.
Course grading 2015 Tiedosto PDF document