| 年度 |
2026年度 |
開講部局 |
先進理工系科学研究科博士課程前期先進理工系科学専攻理工学融合プログラム |
| 講義コード |
WSQN2801 |
科目区分 |
専門的教育科目 |
| 授業科目名 |
Smart Urban Development |
授業科目名
（フリガナ） |
|
| 英文授業科目名 |
Smart Urban Development |
| 担当教員名 |
馮 涛 |
担当教員名
(フリガナ) |
フェン タオ |
| 開講キャンパス |
東広島 |
開設期 |
1年次生 前期 ２ターム |
| 曜日・時限・講義室 |
(2T) 火1-4：国際204号 |
| 授業の方法 |
講義 |
授業の方法
【詳細情報】 |
対面, オンライン（同時双方向型） |
| This course introduces theory- and data-driven approaches for urban research, e.g., transportation, policy analysis, environmental science, emphasizing key aspects, 1) fundamental theory underlying models/algorithms applicable in multidisciplinary research, 2) practices using data. Students learn beyond definitions/concepts but also practices using example data. Research methods will be introduced with specific topics. Guidance on utilizing research methods will be provided. |
| 単位 |
2.0 |
週時間 |
4 |
使用言語 |
E
:
英語 |
| 学習の段階 |
6
:
大学院専門的レベル
|
| 学問分野（分野） |
25
:
理工学 |
| 学問分野（分科） |
13
:
土木工学 |
| 対象学生 |
|
| 授業のキーワード |
Smart mobility, smart energy, health, data mining, choice models, machine learning |
| 教職専門科目 |
|
教科専門科目 |
|
プログラムの中での
この授業科目の位置づけ
（学部生対象科目のみ） | |
|---|
到達度評価
の評価項目
（学部生対象科目のみ） | |
|---|
| 授業の目標・概要等 |
- To understand the concept of smart urban development
- To understand the decision making fundamentals
- 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 fundamental research methods
- Being able to apply different models with data
- Being able to interpret the model results |
| 授業計画 |
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
Basic urban research method [1]
Basic urban research method [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
The order of lectures may be changed. No written exams. Weekly assignments/reports are required to be submitted and will be evaluated. |
| 教科書・参考書等 |
A list of literatures will be given in the study guide. |
授業で使用する
メディア・機器等 |
配付資料, 映像資料, Microsoft Teams, Zoom |
| 【詳細情報】 |
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. |
授業で取り入れる
学習手法 |
ディスカッション, PBL（Problem-based Learning）／ TBL（Team-based Learning）, 授業後レポート |
予習・復習への
アドバイス |
A list of literatures will be given in advance. Studying on these literature before the lectures is recommended. |
履修上の注意
受講条件等 |
|
| 成績評価の基準等 |
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. |
| 実務経験 |
|
実務経験の概要と
それに基づく授業内容 |
|
| メッセージ |
|
| その他 |
https://home.hiroshima-u.ac.jp/taofeng |
すべての授業科目において，授業改善アンケートを実施していますので，回答に協力してください。
回答に対しては教員からコメントを入力しており，今後の改善につなげていきます。 |