for Graduate School of Civil Engineering in 2020 Autumn Semester 1 (A1)

- Prof. Eiji Hato (Professor, Transport Research and Infrastructure Planning (TRIP) Lab.)
- Prof. Takamasa Iryo (Professor, Transport Research and Infrastructure Planning (TRIP) Lab.)
- Assoc. Prof. Ryuichi Shibasaki (Associate Professor, Department of Systems Innovation, School of Engineering)
- Assist. Prof. Muhammad Awais Shafique (Assistant Professor, University of Central Punjab)
- Assist. Prof. Junji Urata (Assistant Professor, Transport Research and Infrastructure Planning (TRIP) Lab.)

Online, on Monday and Thursdays 13:00-14:45. Please check the online URL from ITC-LMS

This course focuses on learning some of methodologies to analyze transportations and regions, which are sometimes vulnerable to natural hazards. In addition to it, getting used to the essence of the basic way of theoretical and mathematical thinking in planning is another main target. For fulfilling these purposes, we choose four topics: 1) Traffic flow modelling, 2) Logistics management and analysis, 3) Statistics and Machine Learning, 4) Travel behavior modelling.

Syllabus

- Sep. 28 Introduction [PDF]

You will use your laptop which are installed R in Topic 1. Please download R from the R's official website.

- Oct. 01 Travel Behavior Modelling (1)
- Oct. 05 Travel Behavior Modelling (2)
- Oct. 19 Presentation from students

- Oct. 08 Traffic Flow Theory and Modelling (1)
- Oct. 12 Traffic Flow Theory and Modelling (2)
- Oct. 15 Traffic Flow Theory and Modelling (3) and Final Exercise

- Oct. 22 Introduction to Machine Learning
## [Abstract]

We will study what Machine Learning is. Where is it used and how it is affecting transportation engineering sector. Some of the popular algorithms will be discussed in detail such as Neural Networks, Support Vector Machines, Decision Trees, Random Forest etc. R packages linked to such algorithms will also be discussed. - Oct. 26 Machine Learning vs. Discrete Choice Modelling
## [Abstract]

In this lecture, we will review some of the studies which have done work on the comparison of Machine Learning with conventional Discrete Choice Models to predict travel behavior. Specifically, we will discuss the use of logit models for mode choice modelling. Class exercise will follow. - Oct. 29 Travel Mode Detection using Machine Learning

- Nov. 02 Global Logistics Modelling & Analysis (1)
- Nov. 05 Global Logistics Modelling & Analysis (2)
- Nov. 09 Presentation from students

Assignments in each of the three topics (22 points @ 4), and attendance points for classes (12 points)

Your attendance is confirmed using the history function of zoom.

- Train, K., Discrete Choice Methods with Simulation Second edition, Cambridge University Press, 2009.
- Sheffi, Y., Urban transportation network, Pretince Hall, 1985.
- Venables, W. N., Smith, D. M., and the R Core Team, An Introduction to R, 2017.
- Paradis, E., R for Beginners, 2005.

- If you have a question, please ask Prof. Urata (urata{at}bin.t.u-tokyo.ac.jp).
- We can provide video which is recorded the talk when your attendance is interrupt by internet-connection problem. Please contact to Prof. Urata as soon as possible.