WSQN2901 Agent-based Transport Simulation

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 WSQN2901 Subject Classification Specialized Education

Subject Name Agent-based Transport Simulation

Subject Name
（Katakana）

Subject Name in
English Agent-based Transport Simulation

Instructor FOURIE PIETER JACOBUS

Instructor
(Katakana) フーリー　ピーター　ヤクバス

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

Days, Periods, and Classrooms (Int) Inte：IMC-Main 2F PC Rm

Lesson Style Lecture Lesson Style
(More Details) 　

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

Course Level 7 : Graduate Special Studies

Course Area（Area） 25 : Science and Technology

Course Area（Discipline） 13 : Civil Engineering

Eligible Students Master / PhD students

Keywords

Special Subject for Teacher Education 　 Special Subject 　

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

Class Objectives
/Class Outline The class objectives of the Agent-based transport simulation course are to introduce students to the philosophy and theory behind modeling social systems, specifically in the context of transportation systems. The course aims to provide students with a comprehensive understanding of agent-based modeling approaches, as well as practical experience in using software tools such as NetLogo, Intellij IDEA, and MATSim to build and analyze transportation models.
Throughout the course, students will learn about a range of topics, including transportation networks and land use, population synthesis, primary activity location choice, activity scheduling, big data-driven approaches, and MATSim co-evolutionary engine. By the end of the course, students should be able to apply these concepts to develop and analyze their own agent-based transport simulations.

Class Schedule Lecture 1,2
Theory:
Philosophy of modeling social systems, intro to agent-based modeling. Thomas Hobbes and assumptions about human nature. Emergence and path-dependence. The Law of Large Numbers. Scale-free phase transitions. Aggregate versus disaggregate models. Agent-based modelling approaches. Cellular automata. Object-oriented programming. Spatial models.
Practical:
Installation of NetLogo, toy model exploration. Experimentation and modification of NetLogo toy models.
Lecture 3
Theory:
Overview of agent-based transport simulation central topics. Four-step modelling vs activity-based travel demand modelling. Microsimulation vs. meso-scale.
Practical:
Installation of Intellij IDEA, MATSim, QGIS. Exploring and running sample MATSim data sets. Visualisation of agent-based simulation output.
Lecture 4
Theory:
Transportation networks and land use. Data sources. Accessibility. Space syntax, network topology. Modeling transit.
Practical:
Generating MATSim network, facilities and transit input files for a medium-size city, e.g. Sioux Falls.
Lecture 5,6
Theory:
Population synthesis: Data sources and formats. Imputing missing attributes. Generalized raking. Household construction. Land use and spatial distribution of households. Discrete choice modeling introduction. Modelling car ownership and licensure.
Practical:
Population synthesis in R and Java.
Lecture 7
Theory:
Primary activity location choice: data sources and formats. Predictors of activity location choice. Comparing random forest and discrete choice modeling of location choice.
Practical:
Modeling primary activity location choice in R and Java.
Lecture 8,9
Theory:
Activity scheduling. Overview of activity scheduling approaches, e.g. TASHA, ALBATROS. Secondary activity accessibility in activity scheduling. Space-time prisms and travel time budgets. Secondary activity location choice.
Practical:
Modeling activity scheduling in R and Java.
Lecture 10, 11
Theory:
Big data-driven approaches. Synthesizing individual travelers from big data aggregates. Generative graphical models. Remaining challenges to data-driven demand generation.
Practical:
Day traveler synthesis from aggregated big data histograms.
Lecture 12,13
Theory:
MATSim co-evolutionary engine. Replanning modules. Time mutation. Least-cost routing. Subtour mode choice. MATSim configuration. MATSim utility function. Transport mode and activity participation utility parameters.
Practical:
Running, visualizing and analyzing MATSim runs of previously generated travel demand.
Lecture 14, 15
Theory:
MATSim events. Simple events analysis code. Overview of important MATSim ‘contrib’ modules: autonomous vehicles, emissions, dynamically routed transit.
Practical:
Develop a customized replanning module for MATSim and an events handler to analyze its effects.

【Schedule】 Aug. 19-23 from 8:45-14:20

Text/Reference
Books,etc. Ball, Philip. Critical mass: How one thing leads to another. Macmillan, 2004.
Axhausen, Kay Werner, Andreas Horni, and Kai Nagel. The multi-agent transport simulation MATSim. Ubiquity Press, 2016.

PC or AV used in
Class,etc. 　

(More Details)

Learning techniques to be incorporated 　

Suggestions on
Preparation and
Review Please bring your own laptop in every lecture.

Requirements

Grading Method Each topic covered will present a small coding challenge. The aggregate grade will be computed from these challenges, each weighted by the number of lectures consumed by the topic.

Practical Experience

Summary of Practical Experience and Class Contents based on it

Message

Other

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	WSQN2901	Subject Classification	Specialized Education
Subject Name	Agent-based Transport Simulation
Subject Name （Katakana）
Subject Name in English	Agent-based Transport Simulation
Instructor	FOURIE PIETER JACOBUS
Instructor (Katakana)	フーリー　ピーター　ヤクバス
Campus	Higashi-Hiroshima	Semester/Term	1st-Year, First Semester, Intensive
Days, Periods, and Classrooms	(Int) Inte：IMC-Main 2F PC Rm
Lesson Style	Lecture	Lesson Style (More Details)

Credits	2.0	Class Hours/Week		Language of Instruction	E : English
Course Level	7 : Graduate Special Studies
Course Area（Area）	25 : Science and Technology
Course Area（Discipline）	13 : Civil Engineering
Eligible Students	Master / PhD students
Keywords
Special Subject for Teacher Education		Special Subject
Class Status within Educational Program (Applicable only to targeted subjects for undergraduate students)
Criterion referenced Evaluation (Applicable only to targeted subjects for undergraduate students)
Class Objectives /Class Outline	The class objectives of the Agent-based transport simulation course are to introduce students to the philosophy and theory behind modeling social systems, specifically in the context of transportation systems. The course aims to provide students with a comprehensive understanding of agent-based modeling approaches, as well as practical experience in using software tools such as NetLogo, Intellij IDEA, and MATSim to build and analyze transportation models. Throughout the course, students will learn about a range of topics, including transportation networks and land use, population synthesis, primary activity location choice, activity scheduling, big data-driven approaches, and MATSim co-evolutionary engine. By the end of the course, students should be able to apply these concepts to develop and analyze their own agent-based transport simulations.
Class Schedule	Lecture 1,2 Theory: Philosophy of modeling social systems, intro to agent-based modeling. Thomas Hobbes and assumptions about human nature. Emergence and path-dependence. The Law of Large Numbers. Scale-free phase transitions. Aggregate versus disaggregate models. Agent-based modelling approaches. Cellular automata. Object-oriented programming. Spatial models. Practical: Installation of NetLogo, toy model exploration. Experimentation and modification of NetLogo toy models. Lecture 3 Theory: Overview of agent-based transport simulation central topics. Four-step modelling vs activity-based travel demand modelling. Microsimulation vs. meso-scale. Practical: Installation of Intellij IDEA, MATSim, QGIS. Exploring and running sample MATSim data sets. Visualisation of agent-based simulation output. Lecture 4 Theory: Transportation networks and land use. Data sources. Accessibility. Space syntax, network topology. Modeling transit. Practical: Generating MATSim network, facilities and transit input files for a medium-size city, e.g. Sioux Falls. Lecture 5,6 Theory: Population synthesis: Data sources and formats. Imputing missing attributes. Generalized raking. Household construction. Land use and spatial distribution of households. Discrete choice modeling introduction. Modelling car ownership and licensure. Practical: Population synthesis in R and Java. Lecture 7 Theory: Primary activity location choice: data sources and formats. Predictors of activity location choice. Comparing random forest and discrete choice modeling of location choice. Practical: Modeling primary activity location choice in R and Java. Lecture 8,9 Theory: Activity scheduling. Overview of activity scheduling approaches, e.g. TASHA, ALBATROS. Secondary activity accessibility in activity scheduling. Space-time prisms and travel time budgets. Secondary activity location choice. Practical: Modeling activity scheduling in R and Java. Lecture 10, 11 Theory: Big data-driven approaches. Synthesizing individual travelers from big data aggregates. Generative graphical models. Remaining challenges to data-driven demand generation. Practical: Day traveler synthesis from aggregated big data histograms. Lecture 12,13 Theory: MATSim co-evolutionary engine. Replanning modules. Time mutation. Least-cost routing. Subtour mode choice. MATSim configuration. MATSim utility function. Transport mode and activity participation utility parameters. Practical: Running, visualizing and analyzing MATSim runs of previously generated travel demand. Lecture 14, 15 Theory: MATSim events. Simple events analysis code. Overview of important MATSim ‘contrib’ modules: autonomous vehicles, emissions, dynamically routed transit. Practical: Develop a customized replanning module for MATSim and an events handler to analyze its effects. 【Schedule】 Aug. 19-23 from 8:45-14:20
Text/Reference Books,etc.	Ball, Philip. Critical mass: How one thing leads to another. Macmillan, 2004. Axhausen, Kay Werner, Andreas Horni, and Kai Nagel. The multi-agent transport simulation MATSim. Ubiquity Press, 2016.
PC or AV used in Class,etc.
(More Details)
Learning techniques to be incorporated
Suggestions on Preparation and Review	Please bring your own laptop in every lecture.
Requirements
Grading Method	Each topic covered will present a small coding challenge. The aggregate grade will be computed from these challenges, each weighted by the number of lectures consumed by the topic.
Practical Experience
Summary of Practical Experience and Class Contents based on it
Message
Other
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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