PORTFOLIO Syuan Bo Huang
Selected Projects 2022-2025
Syuan-Bo Huang is an urban researcher specializing in Urban Analytics and Planning. Addressing the challenges of modern urban mobility and the limitations of traditional top-down planning, he leverages Urban Science to bridge the gap between human needs and spatial policy. His research currently focuses on the intersection of Civic Technology and AI Agents. Through "Sensing," he employs Large Language Models to democratize urban intelligence, enabling natural language interaction with city data. Through "Simulating," he bridges scales—ranging from micro-agent models of pedestrian nuances to macro-level forecasting for sustainable transit networks Alongside this computational expertise is a solid foundation in physical planning. In his "Planning" practice, Syuan-Bo demonstrates the ability to tackle large-scale spatial regeneration. His master plan for the Kaohsiung Port utilized urban regeneration strategies to dissolve industrial boundaries, transforming a severed waterfront into a permeable interface for public life. At MIT DUSP, Syuan-Bo aims to merge these distinct disciplines. He envisions a future where advanced computation serves as a catalyst to solve emerging urban challenges—fostering cities that are adaptable, resilient, and truly sustainable.
01 SENSING & BRIDGING CITY AI
Democratizing Urban Analytics with AI Agents
INSIGHT NAVIGATOR
Turning Thousands of Citizen Voices into Actionable Urban Insights
02 SIMULATING & PREDICTION MICRO SIMULATION: URBAN MOBILITY SIMULATOR AI-Driven Behavioral Simulation for Human-Centered Planning and Decision Support
MACRO PREDICTION: YOUBIKE OPTIMIZATION
Predicting Hidden Demand to Expand Taipei’s Bike-Share System
03 PLANNING & DESIGN KAOHSIUNG PORT REGENERATION Master Plan for Kaohsiung Port Tourism Loop & Love River Bay
CITY AI Democratizing Urban Analytics with AI Agents Keywords Generative AI, Multi-Agent System (MAS), Discrete Global Grid System (DGGS), Human AI Interaction Project Overview CityAI is an AI-powered platform that democratizes advanced geospatial analysis for non-experts. By combining a standardized H3 Discrete Global Grid System (DGGS) pipeline with a decentralized Multi-Agent System driven by Generative AI, it enables users without technical backgrounds to perform complex spatio-temporal queries and visualizations using natural language—bridging the gap between exploding urban data and inclusive, evidence-based decision-making. Contribution • Designed the overall system architecture (Multi-Agent System + human-AI interaction) • Led full-stack development of the platform • Built the H3-based geospatial data pipeline • Integrated Generative AI for natural language-driven analysis members Year 2025 Role Project Manager, Core Developer (Full Stack) Team 4 members Organization City Science Lab @ Taipei Tech Funding Partially supported by the National Science and Technology Council (NSTC) of Taiwan Publication Presented at ACM SIGSPATIAL GeoGenAgent '25 Workshop
ChatGPT Meets a Map — Your City Starts Talking Back
What If Any Citizen Could Query Their City’s Data Just by Asking Questions? CityAI delivers this through a seamless three-tier architecture that democratizes urban analytics: • Data Foundation: Unifies messy urban data into an accessible H3 grid. • Multi-Agent System: Specialized AI agents that collaboratively tackle complex spatial queries. • Human-AI Interface: Natural language input with responses in text, interactive maps, and charts.
How Do We Simplify Complex Urban Data for AI Agents? By converting it into the H3 hexagonal grid—a clean, tablelike structure that AI agents can query and reason over effortlessly. • Consistency: Hexagonal cells unify diverse data for seamless multi-scale analysis. • Performance: Fast index operations replace slow geometric calculations. • Speed: Instant retrieval enables real-time, scalable queries.
A Collaborative Team of AI Agents That Thinks and Acts Like Experts Like a multidisciplinary team of human specialists, CityAI’s agents divide labor and share memory to solve complex urban queries far beyond a single model. • Data Retriever: Gathers relevant H3-indexed spatiotemporal data. • Data Analyst & Operator: Performs comparisons, filtering, and trend analysis for key insights. • Map Visualizer & Chart Generator: Creates interactive maps and clear charts. • Data Scientist: Runs predictive models and future forecasts.
SYSTEM ARCHITECTURE
Ask Your City a Real Question — and Get a Real Map Back. User Query: "Within a 10-minute drive from Beitou Shilin Tech Park, identify areas (1) within 800m of an MRT station and (2) adjacent to parks, providing their 2024 average housing price."
04 EVENTS MICROSOFT DEVDAYS ASIA 2025
CityAI turns everyday questions into evidence-based answers through fully automated workflow: 1. Understanding (Images 01) Coordinator agent breaks the natural-language query into spatial sub-tasks. 2. Geoprocessing (Images 02–03) Instantly computes drive-time areas, MRT buffers, park adjacency, and intersections on H3 grids — no coding 3. Insights (Images 04) Pulls 2024 housing prices and delivers interactive maps plus clear charts.
ACM SIGSPATIAL '25 (Minneapolis)
Each point represents a citizen report
TRAFFIC
[ .......... ] [ .......... ] [ .......... ]
CITIZEN REPORTS DATASET
40,000+ data
WORD EMBEDDING
K-MEANS CLUSTERING
TOPIC DECISION
Embed text description into high-dimentional vectors.
Conduct clustering algorithm to group vectors.
Leverage LLM to comprehend descriptions of each group.
UMAP Visulization (Cluster View)
NEW GROUP
INPUT QUERIES
SIMILARITY SEARCH
COMMUNITY DETECTION
INSIGHT SUMMARY
e.g. sidewalk
Input queries are embedded into textual data to enable relevant information retrieval
Represent the search result as a graph and re-cluster it in new groups.
Leverage LLM to summarize the information of each group to obtain insights.
Graph Visualization (Graph View)
CLUSTER VIEW How Do Public Issues Cluster Across The City? Baseline Overview: UMAP visualizes the initial K-Means clustering of all reports to reveal global macrostructures and hidden correlations.
GRAPH VIEW What Hidden Connections Tie Complaints Together? For a Local Deep Dive, Community Detection reclusters search results , breaking down complex issues into precise sub-categories with LLM-generated summaries.
MAP VIEW Where Are The Real Problem Hotspots On The Ground? Links semantic insights to Geospatial Reality , identifying problem hotspots to support informed resource allocation and policy adjustments.
Pedestrain Agent Agent Agent
Agent
Agent
What If We Could Preview the Impact of Urban Policies At Micro and Macro Scales, Before Implementation? 02 SIMULATING & PREDICTION From individual choices to city-wide outcomes, this section explores urban dynamics at two scales: • Micro-simulations that model human behavior in detail. • Macro-predictions that forecast system-level change. Both create virtual testing grounds for policies — safer, smarter decisions for more resilient and equitable cities.
Agent
MICRO SIMULATION: URBAN MOBILITY SIMULATOR AI-Driven Behavioral Simulation for Human-Centered Planning and Decision Support Keywords Agent-Based Modeling, AI for Urban Planning, Human-Centered Project Overview The Human-Centered Urban Mobility Simulator is a data-driven platform that shifts urban mobility planning from vehicle-centric to pedestrian-focused priorities. Using Agent-Based Modeling (ABM) enhanced by Generative AI, it simulates daily travel patterns of over 2,300 virtual university affiliates in Hsinchu City’s dense university district. By integrating historical traffic accidents, infrastructure data, and YouBike usage logs, the platform generates dynamic visualizations and an interactive web map to identify high-risk intersections, optimize transfer nodes, and support evidence-based policies for safer, more sustainable urban mobility. Contribution • Collected and integrated multi-source urban datasets (historical traffic accidents, infrastructure details, and YouBike transaction logs) • Assisted in designing and refining the overall simulation workflow and data pipeline. • Prompt engineering for generating agent profile and iteniraty. Year 2024-2025 Role Developer Team 5 members Organization City Science Lab @ Taipei Tech Collaboration with Hsinchu City Government
Can AI Bring 2,300 Virtual Students to Life—and Reveal Hidden Paths in a Real City? Human Centered AI Agents: Reviving Hsinchu Streets to Preview Urban Policies In Hsinchu's Guangfu Business Area, surrounding NTHU and NYCU, this student-dense hub is perfect for simulating human-centered mobility. We deployed ~2,300 AI agents (90% students, 10% faculty/staff) to model daily behaviors, integrating censuses, 3-year accidents, YouBike logs, and POIs
Guangfu Business Area
National Tsing Hua University (NTHU)
National Yang Ming Chiao Tung University (NYCU)
Guangfu Business Area
How Do We Turn Data into Living Virtual Students—Step by Step?
01 DATA COLLECTION
02 PROFILE & SCHEDULE
03 ALLOCATION
04 ROUTING
Gather demographic and mobility data from censuses and Google EIE for Hsinchu's university area.
Guide LLMs with stats to create student profiles (department, nationality, gender) and daily schedules.
Assign residences, campuses, and POIs using distributions, similarity search, and Huff model.
Compute shortest paths with multi-modal options (walking, biking, buses), assigned by student proportions.
Meet a Virtual Student School Name National Tsing Hua University College College of Science Department Department of Mathematics Program Bachelor's Program Identity Local Student Gender Male Next Stop Student Activity Center
Identifying High-Risk Intersections— Overlaying Simulated Agent Flow with 3-Year Historical Accident Data
Average Traffic Accidents
Enhancing YouBike Transfer Experience— Overlaying 2024 Usage Data with Simulated Pedestrian and Cycling Paths at High-Volume Stations
Greening Coverage Rate
Bike-Sharing Station Usage
MACRO PREDICTION: YOUBIKE OPTIMIZATION Predicting Hidden Demand to Expand Taipei’s Bike-Share System Keywords Urban Analytics, Spatial Modeling, Urban Data Foundation, Public Policy Project Overview In support of Taipei’s 2050 Net Zero roadmap, this project addressed supply-demand imbalances in the YouBike 2.0 public bike-sharing system through data-driven spatial analysis. A highdimensional dataset with over 100 engineered urban features—integrated from heterogeneous sources such as transaction records, telecom signaling, and land-use data onto a 250m × 250m grid—enabled demand prediction models to achieve R² ≈ 83% accuracy. The analysis revealed an 18.6% service gap and recommended targeted expansion of 4,518 bikes, directly informing Taipei’s sustainable transportation policy. Contribution • Aggregated and engineered 100+ urban features from heterogeneous sources • Performed data cleaning, preprocessing, and quantification of complex indicators • Supported modeling and policy recommendation phases Year 2023 Role Data Analyst Intern Team 3 members Organization Taipei Urban Intelligence Center Collaboration with YouBike Co., Ltd. / Department of Transportation, Taipei City Government
From Policy Ambition to Overwhelming Demand — Framing Taipei’s Bike-Share Challenge
POLICY GOAL Net Zero 2050 / Green Transport 70%
>
CATALYST Free 30-min Ride Policy
>
PROBLEM Demand Surge > Supply
>
INTERVENTION Dynamic Rebalancing & Strategic Expansion
Why Did Free Rides Trigger Such a Massive Surge in Short? —YouBike Became the Perfect Last-Mile Fix
Origin-Destination (OD)
WEEKDAY ORIGIN-DESTINATION MOBILITY PATTERNS Weekday mobility is dominated by short-distance, last-mile travel focused around subway station entrances and exits, revealing a highly concentrated commuting pattern.
How Do We Ease Bike Shortages When Individual Stations Run Dry? —Group Stations That Riders Actually Treat As Alternatives.
Transactions (Few to Many)
CLUSTER-BASED REBALANCING
Stations are grouped by transaction data showing riders treat them as interchangeable alternatives — not just geographic proximity. In rebalancing, the goal is to ensure at least one station in each cluster has bikes available, maintaining regional accessibility within walking distance as a practical interim strategy.
A City Grid That Reveals Where Bikes Will Be Needed Next POINT (POI, trees, stations, ....)
A Unified City Grid Built From Over 100 Urban Layers Aggregated more than 100 urban features into a consistent 250m grid to capture environmental complexity:
LINESTRING
• Points — Commercial POIs and transit nodes • Lines — Roads, bike lanes, and pedestrian paths • Polygons — Buildings, land use zoning, and green coverage
(roads, bike lanes, pedestrian path, ...)
POLYGON (landuse, buildings, . ...)
83% Accurate Demand Forecasts From a LightGBM Model LightGBM model predicts latent ridership in unserved areas using grid features and historical rides: • Objective — Estimate demand from urban environment and past usage • Performance — R² ≈ 83%, average error ~5 bikes
GRID 100+ Urban Features 250 m 250 m
Turning High-Demand Predictions Into Buildable Station Sites Layered filters ensure only viable grids remain for new stations: • Physical Constraints — Exclude mountains, rivers, parks, airports, and institutional land • Service Saturation — De-prioritize areas within 200m of existing stations • Manual Checks — Remove highways, military bases, and other restricted zones
CONSTRAINT MASK
Bikes Demand (Low to High)
03 PLANNING & DESIGN Context-Sensitive Design & Spatial Regeneration Data acts as a guide, but design must respect the land. This section focuses on ContextSensitive Design, ensuring that urban interventions—specifically in Kaohsiung Port—are not just abstract lines on a map, but responsive strategies that honor the site's industrial heritage and reconnect the waterfront to its people.
KAOHSIUNG PORT REGENERATION Master Plan for Kaohsiung Port Tourism Loop & Love River Bay Year 2022 (Graduation Project) Role Team Lead Team 5 memebers Organization National Cheng Kung University
Project Overview For decades, heavy industry severed the connection between Kaohsiung City and its harbor, creating a hard boundary of exclusive production. Responding to the southward migration of industrial logistics and the rise of the cruise economy, this project proposes a holistic regeneration strategy for the vacated waterfront. The plan envisions a continuous "Tourism Loop" that integrates fragmented public lands with urban transit networks. The core intervention—the "Ocean Gateway" at Piers 16 and 17—utilizes landscape urbanism to transform the docklands into a permeable interface. Ultimately, the project dissolves the port-city divide, reclaiming the waterfront for public life and ecological resilience.
B | Yancheng & Pier-2
Cultural & Creative Waterfront Corridor
A | Hamasen
Historic Heritage Quarter
B-2
Love River Bay
C-2
F-2
C | Cruise Terminal
New Maritime Gateway & Landmark
F | North Cijin
Low-Carbon Mobility & Tourism Hub
D | 5G AIoT D-2
E | South Cijin
New Gateway for Art & Tourism
REGULATORY FRAGMENTATION VS. UNIFIED VISION Six Detailed Plans fragment the waterfront. An overlay Master Plan is proposed to stitch the regulatory silos.
How Do We Turn Fragmented Waterfront Sites Into a Vibrant, Connected Destination? — A Continuous Tourism Loop With Six Cores and Four Axes The vision stitches isolated sites into a vibrant, continuous system: • Six Thematic Cores — Heritage, Art, Cruise, Tech, Eco, Low-Carbon anchors that define local identity and complement each other • Four Connecting Multimodal Axes — Blue Highway (ferry & cruise routes), Green Corridor (light rail, cycling, pedestrian network) Hot zones energize cold zones, while integrated ticketing (MaaS) and unified wayfinding ensure temporal and functional continuity along the entire harbor edge.
Tech Innovation & Experience Zone
Cijin LRT Construction Project
Anchor Project Cores LRT Cycling Network Cruise Route Ferry Network
Strategic Rezoning And Unified Codes — Stitching the Waterfront Into Public Realm Targeted policy tools ensure integrated redevelopment: Rezoning • Hamasen/Yancheng: Preservation overlays with adaptive reuse • Cruise/5G Districts: Industrial to mixed-use for commercial synergy • Cijin Island: Eco-tourism zones with low-carbon mobility Design Codes • Continuity: Mandatory waterfront setback for uninterrupted pedestrian loop • Permeability: Public access corridors linking city and harbor • Identity: Coordinated landscape standards across districts
OCEAN GATEWAY AT PIERS 16–17 The Catalyst Bridging Cruise Terminal And City Core
MUSIC CENTER
PIER-2 ART CENTER
SITE
Why Piers 16-17? Selected as the priority prototype from six anchor projects, Piers 16–17 sit at the mouth of Love River Bay — the critical link between the new Cruise Terminal and the urban commercial core. Activating this site reconnects isolated port infrastructure with city life.
Reprogramming the Buffer — From Warehouses To Vibrant Public Edge The site, once occupied by warehouses and port facilities, severed the city from the sea. The design replaces storage with public programs — retail, hospitality, civic plazas — directly connected to the adjacent Cruise Terminal. This vibrant edge draws cruise visitors into Love River Bay while inviting locals to the water, supported by commercial synergy.
YACHT CLUB
LOVE RIVER BAY
OCEAN GATE (Pier 16-17) CRUISE TERMINAL
LRT
Pier 16-17 Current Status: Warehousing Facilities
Form Shaped By Regulation — Views, Flows, Porosity Zoning mandates translated into spatial strategies: • Bridged Flows Elevated pedestrian path from Cruise Terminal slopes down to waterfront, linking LRT and sea. • Maximized Porosity Lifted ground plane creates permeable Waterfront Plaza for direct public access to the water’s edge. BUBBLE DIAGRAM
FLOOR PLAN
1F-6F (Retail)
7F-36F (Hotel)
37F-41F (Hotel Amenities)
SITE PLAN