WSQN2801 Smart Urban Development

Hiroshima University Syllabus

Japanese

Academic Year 2024Year School/Graduate School Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Transdisciplinary Science and Engineering Program

Lecture Code WSQN2801 Subject Classification Specialized Education

Subject Name Smart Urban Development

Subject Name
（Katakana）

Subject Name in
English Smart Urban Development

Instructor FENG TAO

Instructor
(Katakana) フェン　タオ

Campus Higashi-Hiroshima Semester/Term 1st-Year, First Semester, 2Term

Days, Periods, and Classrooms (2T) Tues1-4：IDEC 203

Lesson Style Lecture Lesson Style
(More Details) 　

This course introduces the concepts within smart city and society and fundamental theory-driven and data-driven approaches for urban research, such as transportation, policy analysis, environmental science. Students can learn beyond the definition and concept and practice using example data the various research methods. Research methods will be introduced by incorporating the core concept with weekly topics. Guidance on utilizing the research methods will be provided.

Credits 2.0 Class Hours/Week 　 Language of Instruction E : English

Course Level 6 : Graduate Advanced

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 13 : Civil Engineering

Eligible Students Master students, doctoral students

Keywords Smart mobility, smart energy, health, data mining, choice models, machine learning

Special Subject for Teacher Education 　 Special Subject 　

Class Status
within Educational
Program
(Applicable only to targeted subjects for undergraduate students) Urban Mobility Science
Criterion referenced
Evaluation
(Applicable only to targeted subjects for undergraduate students)

Class Objectives
/Class Outline - To understand the concept of smart urban development
- To understand the health issues in urban research
- To understand the decision making in mobility analysis
- To understand the concept of data mining approaches
- To understand the advantages/disadvantages of theory-driven and data-driven approaches
- To understand the concept in optimal policy decision making
- Understand the principle of the research methods
- Being able to apply different models with data
- Being able to interpret the model results

Class Schedule Introduction [1] Smart urban development: concept of smart society, energy, mobility and health issues
Introduction [2] Smart urban development: concept of smart society, energy, mobility and health issues
Health in urban research [1]
Health in urban research [2]
Smart mobility in the built environment [1]
Smart mobility in the built environment [2]
Energy innovation and adoption [1]
Energy innovation and adoption [2]
Knowledge extraction through data mining [1]
Knowledge extraction through data mining [2]
Theory driven and data driven approaches [1]
Theory driven and data driven approaches [2]
Optimal policy decision making [1]
Optimal policy decision making [2]
Summary and questions

No written exams, weekly assignments/reports are required to be submitted and will be evaluated.

Text/Reference
Books,etc. A list of literatures will be given in the study guide.

PC or AV used in
Class,etc. 　

(More Details) It is recommended to bring or use own laptop/PC during the practical hours.
Lecture slides, documents, data and materials will be shared via Microsoft Teams.

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review A list of literatures will be given in advance. Studying on these literature before the lectures is recommended.

Requirements

Grading Method Assessment criteria is defined for each assignment in the study guide. The final grade will be determined according to the averaged weekly points and participation.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other https://home.hiroshima-u.ac.jp/taofeng

Please fill in the class improvement questionnaire which is carried out on all classes.
Instructors will reflect on your feedback and utilize the information for improving their teaching.

Academic Year	2024Year	School/Graduate School	Graduate School of Advanced Science and Engineering (Master's Course) Division of Advanced Science and Engineering Transdisciplinary Science and Engineering Program
Lecture Code	WSQN2801	Subject Classification	Specialized Education
Subject Name	Smart Urban Development
Subject Name （Katakana）
Subject Name in English	Smart Urban Development
Instructor	FENG TAO
Instructor (Katakana)	フェン　タオ
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, 2Term
Days, Periods, and Classrooms	(2T) Tues1-4：IDEC 203
Lesson Style	Lecture	Lesson Style (More Details)
This course introduces the concepts within smart city and society and fundamental theory-driven and data-driven approaches for urban research, such as transportation, policy analysis, environmental science. Students can learn beyond the definition and concept and practice using example data the various research methods. Research methods will be introduced by incorporating the core concept with weekly topics. Guidance on utilizing the research methods will be provided.
Credits	2.0	Class Hours/Week		Language of Instruction	E : English
Course Level	6 : Graduate Advanced
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	13 : Civil Engineering
Eligible Students	Master students, doctoral students
Keywords	Smart mobility, smart energy, health, data mining, choice models, machine learning
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)	Urban Mobility Science
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	- To understand the concept of smart urban development - To understand the health issues in urban research - To understand the decision making in mobility analysis - To understand the concept of data mining approaches - To understand the advantages/disadvantages of theory-driven and data-driven approaches - To understand the concept in optimal policy decision making - Understand the principle of the research methods - Being able to apply different models with data - Being able to interpret the model results
Class Schedule	Introduction [1] Smart urban development: concept of smart society, energy, mobility and health issues Introduction [2] Smart urban development: concept of smart society, energy, mobility and health issues Health in urban research [1] Health in urban research [2] Smart mobility in the built environment [1] Smart mobility in the built environment [2] Energy innovation and adoption [1] Energy innovation and adoption [2] Knowledge extraction through data mining [1] Knowledge extraction through data mining [2] Theory driven and data driven approaches [1] Theory driven and data driven approaches [2] Optimal policy decision making [1] Optimal policy decision making [2] Summary and questions No written exams, weekly assignments/reports are required to be submitted and will be evaluated.
Text/Reference Books,etc.	A list of literatures will be given in the study guide.
PC or AV used in Class,etc.
(More Details)	It is recommended to bring or use own laptop/PC during the practical hours. Lecture slides, documents, data and materials will be shared via Microsoft Teams.
Learning techniques to be incorporated
Suggestions on Preparation and Review	A list of literatures will be given in advance. Studying on these literature before the lectures is recommended.
Requirements
Grading Method	Assessment criteria is defined for each assignment in the study guide. The final grade will be determined according to the averaged weekly points and participation.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other	https://home.hiroshima-u.ac.jp/taofeng
Please fill in the class improvement questionnaire which is carried out on all classes. Instructors will reflect on your feedback and utilize the information for improving their teaching.

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