2016 Mathematical Modeling of Individual Choice Behavior

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Academic unit or major
Graduate major in Civil Engineering
Instructor(s)
Fukuda Daisuke 
Course component(s)
Lecture
Day/Period(Room No.)
Mon9-10(緑が丘M5-会議室)  Thr9-10(緑が丘M5-会議室)  
Group
-
Course number
CVE.D401
Credits
2
Academic year
2016
Offered quarter
1Q
Syllabus updated
2016/4/27
Lecture notes updated
2016/6/6
Language used
English
Access Index

Course description and aims

(1) To study the theory of Discrete Choice Model (DCM, 離散選択モデル), which is one of the most popular methods of market demand analysis.
-Theoretical Basis: Microeconomics, Applied Statistics, Optimization Theory, Simulation
-Applications: Predicting future demands in transportation or other markets, Economic evaluation of transport infrastructures

(2) To learn knowledge on practical applications of DCM through some exercises and assignments (model estimations with some dataset).
-``BIOGEME": Free software for estimation and simulation
-Computer laboratories with the dataset from various research fields such as transportation, telecommunication, energy and marketing.

Student learning outcomes

(1) To study the theory of Discrete Choice Model (DCM, 離散選択モデル), which is one of the most popular methods of market demand analysis.
(2) To learn knowledge on practical applications of DCM through some exercises and assignments (model estimations with some dataset).

Keywords

Travel behavior analysis, Discrete choice model, Applied statistics, Applied econometrics, Simulation, Probability model, Marketing science, Individual decision-making

Competencies that will be developed

Intercultural skills Communication skills Specialist skills Critical thinking skills Practical and/or problem-solving skills
- - - -

Class flow

While teaching theoretical foundations of DCM, five computer exercises will be conducted. Also, five writing assignments corresponding to each computer exercise will be given.

Course schedule/Required learning

  Course schedule Required learning
Class 1 Choice Behavior and Binary Choice Models (BCM) 1 Simple Example 2 Framework of Discrete Choice Models 3 Random Utility Model 4 Binary Choice Model
Class 2 Estimation of BCM 1 Deviation of Binary Choice Models 2 Estimation of Binary Choice Models 3 Evaluation and Interpretation of Estimation Results
Class 3 Computer Lab. (1): Estimation of BCM Estimating binary choice models
Class 4 Multinomial Choice Models: Logit and Probit 1 Review of Random Utility Model 2 Multinomial Probit 3 Multinomial Logit 4 IIA Property of Logit
Class 5 Specification and Estimation of Multinomial Logit Models (MNL) 1 Specification Issues of Logit Model 2 A Case Study of Specification Issues 3 Estimation Issues of Logit Model
Class 6 Computer Lab. (2): Estimation of MNL Estimating multinomial logit models
Class 7 Statistical Tests of Discrete Choice Models 1 Introduction: What is "statistical test"? 2 Informal Tests 3 Classical Statistical Tests 4 Likelihood Ratio Test 5 Goodness-of-Fit of the Entire Model 6 Advanced Tests
Class 8 Independent from Irrelevant Alternatives, Forecasting and Microsimulation 1 Introduction 2 IIA (Independence from Irrelevant Alternatives) property of Logit 3 IIA Tests 4 Forecasting and Microsimulation
Class 9 Computer Lab. (3): Statistical Testing and Forecasting 1 Testing IIA 2 Forecasting and Microsimulation
Class 10 Nested Logit Model (NL) 1 Review of IIA Property 2 Correlation among Choice Alternatives 3 Nested Logit Model 4 A Case Study: Choice of A Residential Telephone Service
Class 11 Issues on Sampling 1 Introduction: Why focus on sampling? 2 Examples of Sampling Strategies 3 Formulation of Sampling Strategies 4 Estimation Considering Sampling
Class 12 Computer Lab. (4): NL and Sampling Issues 1 Estimating Nested Logit Model 2 Estimation considering sampling
Class 13 Mixed Logit Model (MXL) and Simulation-based Estimation 1 Introduction 2 Mixed Logit Model: Formulation 3 Mixed Logit Model: Estimation
Class 14 Computer Lab. (5): Estimation of MXL Estimating Mixed Logit Models
Class 15 Advances of DCM in Transportation Studies Introducing the state-of-art of DCM in transportation science

Textbook(s)

Lecture materials will be provided at OCWi.

Reference books, course materials, etc.

Ben-Akiva M. & Lerman S. (1985) Discrete Choice Analysis: Theory and Applications to Travel Demand, MIT Press.
Train K. (2003) Discrete Choice Methods with Simulation, Cambridge University Press.

Assessment criteria and methods

- Five assignments (75%)
- Correspondence/reaction to the questions during the class (25%)

Related courses

  • CVE.D230 : Urban and Transportation Planning Project
  • CVE.D301 : Traffic and Transportation Systems
  • CVE.D311 : Public Economics
  • CVE.D402 : Transportation Network Analysis
  • CVE.D403 : Transportation Economics
  • GEG.P502 : Project Management and Evaluation for Sustainable Infrastructure
  • CVE.D201 : Fudamentals of Infrastructure Planning
  • CVE.D210 : Planning Theory for Civil and Environmental Engineering
  • UDE.E402 : GIS and Digital Image Processing for Built Environment
  • IEE.B336 : Applied Econometrics

Prerequisites (i.e., required knowledge, skills, courses, etc.)

Students are recommended to take the above-mentioned relevant courses.

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