Introduction to Transportation Informatics | Lecture 1, 2026 | Masaki Ito | The University of Tokyo

The University of Tokyo, Grad. School of Info. Science & Tech. / Creative Informatics AY2026 "Transportation Informatics" Lecture 1 April 8, 2026 Yayoi Campus, I-REF Bldg., Hilobby Introduction to Transportation Informatics Masaki Ito Social ICT Research Center / Dept. of Creative Informatics (concurrent) Graduate School of Information Science and Technology The University of Tokyo

Masaki Ito • • • Associate Professor, Social ICT Research Center, Graduate School of Information Science and Technology, The University of Tokyo Research Interests – – Ubiquitous computing Transportation informatics – – – – Born in Kakegawa, Shizuoka 2002 B.A. in Environmental Information, Keio University 2009 Ph.D. in Media and Governance, Keio University (Supervisor: Prof. Hideyuki Tokuda) 2008-2010 Research Asst. Prof., Grad. School of Media and Governance, Keio University 2010-2013 Assistant Professor, Graduate School of Engineering, Tottori University 2013-2019 Assistant Professor, Institute of Industrial Science, The University of Tokyo 2019-2021 Project Lecturer, Institute of Industrial Science, The University of Tokyo 2021-present Current position – Certified Transportation Manager (Passenger) Career – – – – • Qualification 2

This Course Is Conducted in English • Per the policy of the Graduate School of Information Science and Technology, this course is now conducted in English starting this academic year. • Course materials are also prepared primarily in English. • Students may submit reports and questions in either English or Japanese. • Transportation data used in this course are primarily from Japan and therefore often written in Japanese. While instruction will be given in English, some ability to read Japanese and kanji will be helpful for understanding the data. • Selected lectures from the 2023 edition of this course are available on YouTube. Note that some content will be revised for the current academic year.

How to Attend the Course • In person: Held at Hilobby, I-REF Building, Yayoi Campus • Online: Available via Zoom. Find the Zoom link on UTAS/UTOL (same link used each session). • Lecture recordings: Sessions are recorded and posted by around the weekend — usable for review or as an attendance alternative. Note: timely posting is not guaranteed due to possible technical issues.

Career and Research History 2000 2008 Student, Tokuda Lab Research Asst. Prof., Tokuda Lab Keio University 2010 Assistant Professor, Dept. of Engineering 2013 2019 Research Associate, Sezaki Lab 2021 Project Lecturer, Oguchi Lab Tottori University Assoc. Professor, Social ICT Research Center The University of Tokyo Ubiquitous Computing (IoT): Pervasive Environments of Computers and Sensors Human-Computer Interaction (HCI), UI/UX, and Social Acceptance of Computing IT-enabled Public Transit & Community Transport Spatial / Map Information Environmental Sensing Transportation Planning Pedestrian Flow Sensing Traffic Engineering ITS & Traffic Control

Ubiquitous Computing & IoT • A world where computers are seamlessly embedded in physical objects and the environment. Computers form networks and support our lives without explicit user commands. • My interest is a software architecture for geospatial information and navigation, enabled by the convergence of the Cyber World and Physical World.

History of Computers: Miniaturization and Mass Adoption Mainframe (1950s–) Workstation (1980s–) Laptop (1990s–) Tablet PC (2000s–) Minicomputer (1970s–) IBM System/360 UNIX, the Internet, and more begin Personal Computer (1980s–) PDA (1990s–) Smartphone (2000s–)

BusNet: a Regional Bus/Rail Journy Planner, Tottori University (2010–2013) • Over 40,000 unique users per year • Over 300,000 searches per year • Minister for Internal Affairs Award (Industry-Academia-Govt. Collaboration), 2009 • Minister for Internal Affairs Award, U-Japan Grand Prize (Regional Revitalization), 2008; and more

Behavioral Analysis of BusNet Users • Revealing travel demand for public transit through big data analysis of web and app usage Rank Origin Destination 1 Tottori Station AEON Tottori Kita Aeon Tottori Kita Tottori Station Tottori Station Prefectural Office /Red Cross Hospital 3 4 5 目的地設定 500 450 400 350 300 Tottori Sho (High school) mae AEON Tottori Kita Tottori Station Tottori Sand Dunes 250 利用数 2 出発地設定 200 150 100 50 0 0 2 4 6 8 10 12 14 16 18 20 22 24 時間帯 h Tottori Station Bus Stop Demand by Route Segment Demand Distribution by Area Boarding/Alighting Patterns by Bus Stop

Development of an Access Log Analysis System • Development of a web interface for intuitive analysis – Big-data analysis through distributed processing with Hadoop – Selected for MIC Strategic Information and Communications R&D Promotion Programme (SCOPE)

Mobility Infrastructure in the Age of Ubiquitous Computing

Mobility Infrastructure in the Pre-Computer Era • Transportation developed and grew long before computers appeared. • Even today, computers are often seen as just an add-on to transportation — not as a core part of it.

Autonomous Driving as Real Infrastructure • United States – Waymo (Google): the only fully driverless paid robotaxi service in the world — 100,000 rides/week as of Aug. 2024 – 1,400+ autonomous vehicles tested across multiple states, with Arizona, Texas, and California leading deployment – Cruise (GM), Zoox (Amazon), and Tesla FSD are also advancing AV technology across the country • China – 32,000 km of roads open for AV testing; Beijing demo zone expanding to 3,000 km² – Key players: Baidu Apollo Go, Pony.ai, WeRide — expanding globally via Uber partnership https://www.wired.com/story/robotaxis-cruise-waymo-san-francisco/

• https://www.wired.com/story/robotaxis-cruise-waymo-san-francisco/

Uber • Uber could not exist without smartphones, GPS, and real-time data. Computers are not an add-on — they are the foundation. • • • • 2010 Founded in San Francisco 2011 Expanded to New York and Paris 2013 Launched taxi-hailing service in Tokyo 2015 Recruited 40 researchers from Carnegie Mellon University 2015 Ride-sharing pilot in Fukuoka halted by Ministry of Land, Infrastructure, Transport and Tourism 2016 Partnership with Toyota • •

Uber Pool: Shared Rides Optimized by Algorithms • Passengers walk to a pickup point and wait — in return, fares drop and routes become more efficient. This trade-off is managed entirely by real-time algorithms. • Short History – Nov 2017 Pilot launched in San Francisco as “Uber Express Pool” – Feb 2018 Officially launched; later merged into Uber Pool https://techcrunch.com/2018/02/21/uber-officially-launches-uber-express-pool-a-new-twist-on-shared-rides/

• https://techcrunch.com/2018/02/21/uber-officially-launches-uber-express-pool-a-new-twist-on-shared-rides/

Micro-Mobility: IT-Enabled Urban Movement • Electric scooters and shared bikes — collectively called “micro-mobility” — are ITnative by design. • Every trip is tracked by GPS and managed through smartphone apps — the entire operation is data-driven. • Major players (US cities, as of 2025): – Lime (dominant), Bird (reorganized after 2023 bankruptcy), Lyft (Citi Bike / docked systems), Spin (acquired by TIER, Germany)

Transportation Informatics • Transportation informatics is not simply about applying IT to transportation. It is the discipline of understanding, analyzing, designing, and implementing transportation systems built on IT as their foundation. • In this course, students learn methodologies for treating mobility and transportation as data — analyzing, designing, and operating systems through a data-centric approach. • Particular emphasis is placed on hands-on skills: finding diverse datasets, extracting and shaping the relevant elements, and visualizing them in a variety of ways.

Transportation is becoming a software-defined system.

What Is Changing in Transportation Today? • More data is being generated than ever before • Software has moved to the core of transportation systems • AI is now embedded in operations and analysis • The shift: from mode-by-mode to network-wide optimization

Four Major Trends Reshaping Transportation Today • Trend 1: Autonomous Driving and Its Impact on Transportation Systems • Trend 2: Data-Driven Redesign of Local Transportation • Trend 3: Digital Mediation of Travel Behavior • Trend 4: AI in Practice

Trend 1: Autonomous Driving and Its Impact on Transportation Systems

City of Tomorrow with Autonomous Vehicles (Drive Sweden) • A vision of how autonomous vehicles will transform our cities – From streets designed for cars to spaces designed for people • • • • • • Road signs become unnecessary More efficient road use means wider sidewalks No need for parking lots in city centers Vehicles waiting at the station — no waiting for passengers Autonomous truck platoons for efficient freight movement Scheduled loading and unloading reduces parking demand https://www.youtube.com/watch?v=WmYsWYDQxuI

• https://www.youtube.com/watch?v=WmYsWYDQxuI

Impact 1: From Private Cars to Shared, Integrated Mobility • Autonomous vehicles may shift mobility from ownership to access. • In that case, the main unit is no longer the privately owned car, but the mobility service. • This connects naturally to the logic of MaaS: – seamless connection between rail, bus, and on-demand vehicles – unified booking, payment, and navigation – flexible first/last-mile services • In this view, autonomous vehicles become part of the publictransport ecosystem rather than a simple replacement for private cars.

Impact 2: From Vehicles to Software-Defined Mobility Platforms • Autonomous vehicles depend on software, connectivity, sensors, maps, and cloud-based coordination. • This means that transportation increasingly becomes a platform-based system. • The core value shifts from the vehicle itself to: – real-time data collection – fleet coordination – routing and dispatch – integration with traveler information and payment systems • In this sense, autonomous mobility is part of a broader transition toward software-defined transportation systems.

Impact 3: From Vehicle Automation to Urban System Reorganization • Autonomous mobility may reshape the city itself. • If shared autonomous services reduce the need for long-term parking, urban land can be used differently. • Impacts include: parking demand, curbside use, street design, and transit hubs. • The impact is not limited to passenger transport: – logistics and last-mile delivery may also be integrated into the same digital coordination logic • The larger issue is system optimization: – matching demand and supply more dynamically – coordinating passenger and freight movement together

TESLA • An electric vehicle company founded by Elon Musk – Founded in 2003 • Autonomous driving hardware comes standard • Algorithm improves through real-world driving data from users Customization and ordering available online • – Cameras, ultrasonic sensors, and radar for environment recognition – Autopilot feature provided – Not fully autonomous yet, but full self-driving capability planned – New features added via over-the-air software updates

Tesla FSD (Full Self-Driving) • Starting with FSD v12.5.5, released in 2024, Tesla switched to an end-to-end AI approach, significantly improving autonomous driving performance. – Previous approach: camera and sensor inputs were interpreted by rulebased programs to determine driving actions • e.g., "The arrow signal ahead points right, no obstacle in front — enter the intersection and turn right at current speed" – E2E approach: a neural network directly learns the mapping from sensor inputs and spatial context to driving actions, without intermediate rulebased interpretation. Training data is collected from a large fleet of Tesla vehicles on the road.

Trend 2: Data-Driven Redesign of Local Transportation

Data-Driven Redesign of Local Transportation • Local transportation is no longer only a matter of maintaining existing bus and rail services. • It is increasingly a matter of redesigning mobility systems under demographic, financial, and operational constraints. • In many regions, the challenge is not how to optimize one operator, but how to reorganize the entire local mobility ecosystem. • This includes: – bus services – rail connections – taxis and on-demand services – first/last-mile access – coordination among public and private actors

“Transportation Desert” Elimination and FullScale Re-Design Project (Ministry of Land, Infrastructure, Transport and Tourism・MLIT) https://kotsu-kuhaku-r8.jp

• https://kotsu-kuhaku-r8.jp/

Re-Design: Four Key Targets • ① Eliminating “Transportation Deserts” – Supporting locally-led systems such as Japanese-style ride-sharing and demandresponsive transit • ② Promoting Consolidation and Collaboration – Supporting joint operations among transport operators (buses, taxis) to improve efficiency • ③ Advancing Digital Transformation (DX) in Local Transportation – Supporting data and system integration for advanced services through digital technology • ④ Developing Mobility Experts and Organizational Capacity – Supporting local governments in data-driven reviews of regional transportation with stakeholders

Local Transportation Is a Governance Problem as Much as a Technical One • Local transportation is rarely managed by a single actor. • In practice, it involves: – local governments – transport operators (railway, bus, taxi, etc.) – police – residents and community representatives • Redesign is not only a technical problem but also a problem of coordination, negotiation, and public decision-making.

From Experience-Based Operations to Data-Driven Redesign • Local transportation has often relied on experience, intuition, and practical judgment. • This knowledge remains valuable and often indispensable. • But redesign now requires decisions that are more transparent, shareable, and responsive to change. • Data does not replace experience. • It makes judgment more visible, testable, and discussable.

Example of a Recent Policy Initiative in Japan • The Japanese government is moving to make local transportation data easier to share among operators and local governments, in order to support route redesign and the sustainable provision of local mobility services. https://www.nikkei.com/article/DGXZQOUA206FO0Q6A220C2000000/

• https://www.nikkei.com/article/DGXZQOUA206FO0Q6A220C2000000/

Why Data Matters in Local Transportation Redesign • Data helps reveal problems of local transportation: – Coverage gaps — areas and users left without adequate service – Service gaps — mismatches in timing, frequency, and connections – Demand gaps — where supply no longer matches actual travel needs • Data also creates a shared basis for discussion. – When stakeholders work from the same maps and data, discussion can move from opinion to problem-solving. • The goal is to support informed decision-making, not only analysis.

Data as a Starting Point for Dialogue — Kumamoto • Representatives from Kumamoto City, bus operators, and university researchers meet regularly to plan and discuss field trials in the Kumamoto area. • Shared data on the screen gives everyone a common ground to work from. 41

17年7月 17年9月 17年11月 18年1月 18年3月 18年5月 18年7月 18年9月 18年11月 19年1月 19年3月 19年5月 19年7月 19年9月 19年11月 20年1月 20年3月 20年5月 20年7月 20年9月 20年11月 21年1月 21年3月 21年5月 21年7月 21年9月 21年11月 22年1月 22年3月 22年5月 22年7月 22年9月 22年11月 23年1月 23年3月 23年5月 23年7月 23年9月 23年11月 24年1月 24年3月 24年5月 24年7月 24年9月 24年11月 25年1月 25年3月 25年5月 25年7月 25年9月 25年11月 Growth in Public Transit Open Data Providers in Japan (2017–) オープンデータ提供事業者数 800 700 600 500 400 300 200 100 0

The GTFS Format • A globally adopted open data standard for public transit • A file format that bundles all information needed for transit trip planning: stops/stations, routes, schedules, and fares Stops/Stations + Routes Schedules Fares

Trend 3: Digital Mediation of Travel Behavior

Transit Apps in Japan 駅すぱあと 駅探 乗換案内 ジョルダン 乗換案内 Yahoo!乗換案内 NAVITIME Google Maps Apple Maps

Digital Mediation of Travel Behavior • Travel behavior is increasingly shaped through digital interfaces. • People are no longer interacting only with vehicles, roads and physical signboards. • They are also interacting with software, rankings, recommendations, alerts, and platform logic.

The Growing Role of Smartphones in Travel Behavior • Why Did It Fail? Transit Apps on a Snowy Day – Misled by the app — missed the bus three times – The bus I planned to take disappeared from the app – Kept searching on the taxi app — no luck at all • → Users now expect apps to work not just in normal conditions, but during emergencies too – “Bad weather means disruption” is no longer an acceptable excuse NHK NEWS Web, January 19, 2016 48

SEO Thinking in Public Transportation Nikkei MJ, October 19, 2015 / Interview with the President of Keihan Electric Railway When planning a train journey, most passengers turn to a transit app on their smartphone. No matter how much a railway promotes its appeal, most riders simply take the first option the app shows. Ranking at the top of transit search results is becoming the single most important factor in winning passengers. This is something we cannot ignore. That is why we work hard to shave off even one or two minutes of travel time. 49

Digital Information Does Not Only Support Travel — It Shapes It • Digital tools do more than provide information. • They actively influence: – which mode people choose – which route they take – when — or whether — they travel • Mobility platforms don’t just respond to demand. They can reshape it.

From Guidance to Steering • Digital systems can now actively steer travel behavior at scale. • New possibilities: – peak spreading, congestion mitigation, mode shift, demand management • New questions: – Who decides what gets recommended — and on what basis?

石村怜美, 梶原康至, 太田恒平: 「乗換検索サービス の経路選択データを用いた公共交通の経路選択行 動分析」, 第49回土木計画学研究発表会, 2014. • x

• x

太田恒平, 渡部啓太, 小竹輝幸, 梶原康至: 「カーナビ が 経路選択を左右する」, 第53回土木計画学研究発表 会, 2016年. • x

Why This Matters for Transportation Informatics • Transportation design must now include: – information design, interface design, ranking logic, incentive design, platform governance • Key insight: • Transportation informatics is also about how digital systems shape perception, choice, and movement.

Trend 4: AI in Practice

AI in Practice • • • • AI in transportation is no longer only a research topic. It is increasingly used in practical, task-specific ways. In most cases, AI does not replace the whole system. It supports particular functions such as: – prediction – monitoring – anomaly detection – decision support – communication and user assistance • Key point: • The most realistic role of AI today is not full automation, • but practical support for specific transportation tasks.

OECD Report: AI in Mobility • The mobility sector is foundational to the EU economy — but faces growing pressure to adapt. • AI is emerging as a key enabler of smarter, more sustainable, and resilient mobility • Three application fields examined in depth: – Automated driving – Public transport – Fleet management (freight) • Based on literature review + interviews with EU businesses (Dec 2024 – Apr 2025) https://www.oecd.org/en/publications/progress-inimplementing-the-european-union-coordinated-plan-onartificial-intelligence-volume-2_3ac96d41-en/full-report/ai-inmobility_3606a201.html February 18, 2026

• https://www.oecd.org/en/publications/progress-in-implementing-the-european-union-coordinated-plan-on-artificial-intelligence-volume-2_3ac96d41-en/full-report/ai-in-mobility_3606a201.html

Application Areas of Generative AI in Transportation Planning • Descriptive Tasks for Data Fusion and Analytics • Predictive Tasks in Transportation Planning • Generative Tasks: Data Synthesis and Scenario Generation • Simulation Tasks for Mixed Traffic Environments – Collecting, processing, integrating, and analyzing transportation data to extract actionable insights – Understanding system status, identifying patterns, and detecting anomalies – Forecasting traffic flow, arrival times, travel demand, and infrastructure performance using historical and real-time data – Potential to capture dynamic, multi-dimensional patterns beyond regression models and rule-based simulations – Creating synthetic datasets for data-scarce scenarios; running hypothetical simulations – Addressing: data collection costs, privacy concerns, and rare events – Enabling complex, high-fidelity traffic simulations • Including mixed scenarios with human-driven vehicles (HVs) and autonomous vehicles (AVs) • Trustworthiness in GenAI-Based Transportation Systems • Source: Da, L., Chen, T., Li, Z., Bachiraju, S., Yao, H., Li, L., Dong, Y., Hu, X., Tu, Z., Wang, D., et al.: Generative AI in Transportation Planning: A Survey, arXiv preprint arXiv:2503.07158, 2025. – Six key challenges: privacy, security, fairness, accountability, explainability, and reliability

Where AI Is Already Becoming Useful • AI is being applied in areas where transportation systems generate large amounts of operational data. • Realistic application areas include: – predictive maintenance of vehicles and infrastructure – video-based safety monitoring – traffic and demand prediction – dynamic routing and dispatch – customer support and passenger information • These applications are practical because they: – use existing data streams – solve narrow but important problems – can be introduced without redesigning the whole system

How Well Can LLMs Answer Questions in Transportation Planning? • GPT-4 and Phi-3-mini evaluated across three areas: 1. Geospatial processing capabilities 2. Domain-specific knowledge in transportation 3. Real-world problem-solving in congestion pricing scenarios • GPT-4 outperformed Phi-3-mini across all levels: 86% on GIS tasks, 81% on MATSim comprehension, and 91% on real-world transportation decision support. “Beyond Words: Evaluating Large Language Models in Transportation Planning”, Ying, Shaowei, Zhenlong Li, and Manzhu Yu, Geo-Spatial Information Science 1, 23 (2025).

Feeding Transportation Data to AI Agents

Person Trip Survey Data as an MCP Server • Downloaded the 6th Tokyo Metropolitan Person Trip Survey data (Heisei 30 / 2018) – Cross-tabulated data from multiple perspectives is publicly available • Distributed Excel files imported into PostgreSQL, then exposed as an MCP server https://www.tokyo-pt.jp/data

• https://www.tokyo-pt.jp/data

Transportation Data Analysis: A Demo • Starting from understanding the entire database, then locating and interpreting the needed data – Runs autonomously for several minutes to over 10 minutes per question • Uses SQL, Python, etc. to retrieve data and generate charts • Presents findings not only as charts but also as written insights 10x speed playback

QGIS Control: A Demo • • • Instructed to draw a Shinkansen route map Started with major stations; added all stations upon further instruction For some reason, the data got corrupted midway 25x speed playback

What This Means for Transportation Informatics • AI does not eliminate the need to understand data, systems, and context. • In fact, AI becomes useful only when: – data are well structured – operational goals are clear – outputs can be verified – human judgment remains involved • For this course, AI should be treated as a practical tool for: – coding support – exploratory analysis – data cleaning assistance – visualization support – explanation and communication

Section Summary: The SoftwareDefined Transportation System • Software-defined Transportation System • • • • More data is being generated than ever before Software has moved to the core of transportation systems AI is now embedded in operations and analysis The shift: from mode-by-mode to network-wide optimization • Major Trends – Trend 1: Autonomous Driving and Its Impact on Transportation Systems – Trend 2: Data-Driven Redesign of Local Transportation – Trend 3: Digital Mediation of Travel Behavior – Trend 4: AI in Practice

Transportation Informatics

Objectives and Scope of Transportation Informatics • • • This course covers the transportation domain, which is rapidly evolving through integration with information technology. Students will acquire fundamental and practical skills in transportation data analysis, geospatial information processing, traffic simulation, and transportation service design. In transportation engineering and planning, leveraging diverse data on road and public transit systems to achieve safer, smoother, more convenient, and efficient transportation infrastructure and services is increasingly important. Advances in GIS, databases, simulation, machine learning, and AI have greatly expanded and enhanced the methods available for transportation analysis and design. Through this course, students will explore the latest case studies and research on transportation data collection, visualization, analysis, and societal applications. Working hands-on with real transportation data, students will develop practical skills in programming, data analysis tools, and AI — building a foundation of transferable competencies applicable to both research and professional practice.

Course Schedule (13 Lectures) 1. Introduction to Transportation Informatics 2. GIS and Spatiotemporal Databases 1 3. GIS and Spatiotemporal Databases 2 4. Public Transit Data Analysis with PostgreSQL + PostGIS + QGIS 1 5. Public Transit Data Analysis with PostgreSQL + PostGIS + QGIS 2 6. Public Transit Data Analysis with PostgreSQL + PostGIS + QGIS 3 7. AI and Data Analysis 8. Network Search and Road Traffic 9. Introduction to Microscopic Traffic Simulation: SUMO 10. Advanced Microscopic Traffic Simulation: SUMO Applications 11. Urban Transportation Planning and Data 12. AI and Transportation Simulation 13. The Future of Transportation Informatics (Discussion)

Geographic Information Systems (GIS) • Maps are essential for understanding and analyzing transportation. • We will develop skills for working with spatial data: – Visualization • How to represent the information you want to communicate – Spatial operations • e.g., Can you calculate the distance between 2 points from their lat/lon coordinates? • QGIS Exercise – An open-source GIS tool • Where to find spatial data – Census data, mesh data, and more

Public Transit Data Analysis with PostgreSQL + PostGIS + QGIS • Learning SQL for spatial data management and analysis – Managing large datasets with files or Excel alone is inefficient – SQL is a transferable skill — applicable to personal PCs, enterprise databases, and big data platforms • SQL: A programming language for relational databases – Used with PostgreSQL, Oracle Database, MySQL, Google Cloud BigQuery, and more • PostgreSQL + PostGIS – Open-source RDBMS with spatial data extension – Enables geospatial queries and analysis on transportation data

Metropolitan Transportation Census • Conducted every five years by MLIT in the three major metropolitan areas (Tokyo, Nagoya, Osaka) to survey the actual usage of mass public transit (railways, buses, etc.) • The most recent survey was conducted in FY2021. • Survey Methods – Up to the 12th survey: paper questionnaires distributed at stations, returned by mail and statistically expanded (sample survey: 320,000 responses) – 13th survey: aggregated from railway IC card data — contactless, full census (19.15 million records) https://www.mlit.go.jp/sogoseisaku/transport/sosei_transport_tk_000007.html

• https://www.mlit.go.jp/sogoseisaku/transport/sosei_transport_tk_000007.html

Microscopic Traffic Simulation: SUMO • Models individual vehicles and places them in a simulated road environment – Vehicle behaviors modeled: carfollowing, lane-changing, etc. • Reproduces traffic conditions under specific scenarios – Inputs: road network, traffic volume, signal timing, etc. • Types of traffic flow simulators – Also includes macro- and mesosimulators

Generative AI and AI Agents • We plan to use these in this course as well. • Details will be worked out as the course progresses…

Course Materials and Lecture Videos • Slides will be shared on UTOL before each lecture. • Lecture videos will be shared: – After minor editing, uploaded to YouTube by around Friday – Please use them for review before assignments

Part of the Course Is Open to the Public • The parts where the instructor is speaking (excluding student discussions) are made publicly available. – Course materials and exercise data are also publicly available. https://itolab.t.u-tokyo.ac.jp/education/

• https://itolab.t.u-tokyo.ac.jp/education/

Assignments and Grading • Attendance comment (each lecture) – Submit via UTOL (including today) – Feedback and discussion during class – Deadline: 24:00 one week later • Midterm report • Final report • Grading – Attendance 2 : Midterm 3 : Final 4 – Submitting either report prevents a grade of "not attempted"

AY2025 Midterm Report Assignment • Prompt: Using the Metropolitan Transportation Census, identify a transportation phenomenon you find interesting and explain it using maps and charts. • Length: 1,000+ characters (Japanese) or 500+ words (English) + 2 or more figures/tables • SQL: If you use SQL, include it in the report (appendix is fine). Use of SQL earns bonus points. • Generative AI: If used, describe how in the report (appendix is fine). No penalty for any use. • Any questions about the assignment? Please leave them as today’s attendance comment.

AY2025 Final Report Assignment • Length: – 1,500+ characters (Japanese) or 750+ words (English) + 3 or more figures/tables – Attach SQL, Python, or other code used as an appendix • Prompt: Based on the content of Transportation Informatics (Advanced), identify a topic that could contribute to the future of transportation and discuss it. • Example topics (you are free to choose your own; originality of the topic itself is not required): – Policy recommendations based on transportation data – Explaining transportation phenomena using data and simulation – Cross-regional comparison of transportation using GIS – Survey of tools for transportation data analysis and their applicability • Bonus points (hands-on use of IT and data is valued): – SQL, transportation big data, GIS, traffic simulation – Analysis combining multiple datasets – Use of data or IT tools not covered in class – Programming

Today's Assignment • Please share your impressions of the course and what you hope to learn through it. • Submit via UTOL – Deadline: April 15 (Wed) 24:00

Preparation for Next Class • Install QGIS – Please install QGIS on your own PC before the next class. • Version – 4.0.0: Latest release – 3.44.8 Long Term Release (LTR) — recommended for stability

Q&A