HackStack

Project Info

Public Transit AI

Devpost

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Inspiration

Transit agencies drown in citizen complaints but lack staff to sift patterns fast enough to act. We wanted an AI co-pilot that surfaces the "why" behind the numbers -- not just another dashboard. At the same time, we believe in transparency: riders should be able to make their complaints public so everyone can stay informed, empowered, and safe together.

What it does

Ingests complaint records - we created a synthetic mirror of SFMTA public feedback data Slices data automatically into actionable segments: rush hour vs. off-peak, equipment failures in heat waves, concert/sports event days, high-incident stops, and more Plots each slice with Chart.js for intuitive exploration Summarizes the story using Claude 4 Opus. Our prompt includes examples and special logic (e.g., merging adjacent stations so that insights move beyond single stops) to generate clear, high quality insights

How we built it

✨ Synthetic data: Generated 1,000 realistic complaints mirroring public transit patterns across five years. 🛢️ Supabase Postgres stores incidents; Claude adds auto-tags to help organize them. 🧠 Claude 3 Opus summarizes each chart with markdown insights. Prompt includes few-shot examples, adjacent-station logic, and chart JSON. 📈 Next.js 13 + React-ChartJS-2 render the dashboard. Plugins add baselines, hotspots, and annotations. 🧊 Caching with Supabase: Summaries are stored under chart_id so we don’t hammer Claude every time.

Challenges we ran into

No public datasets or PII allowed: We built a synthetic dataset statistically resembling real complaints while preserving privacy LLM anchoring bias: Claude hallucinated "no reports" when the JSON started with multiple zeroes (midnight-4 AM where no reports are made). Solving via pre-filtering and prompt tuning to prevent early-token bias from dominating.

Accomplishments we're proud of

🌍 Aligned with UN SDGs: SDG 11 (Sustainable Cities & Communities): boosts trust and usage of public transit SDG 5 (Gender Equality): safer commutes through proactive harassment alerts SDG 3 (Good Health & Well-being): crowding insights help riders avoid unhealthy over-packed vehicles SDG 9 (Industry, Innovation & Infrastructure): AI-driven, low-cost analytics for civic infrastructure. SDG 11 (Sustainable Cities & Communities): boosts trust and usage of public transit SDG 5 (Gender Equality): safer commutes through proactive harassment alerts SDG 3 (Good Health & Well-being): crowding insights help riders avoid unhealthy over-packed vehicles SDG 9 (Industry, Innovation & Infrastructure): AI-driven, low-cost analytics for civic infrastructure. ⚡ From CSV → dashboard → AI summary in ~3 s average round-trip 💸 Entire stack runs on Supabase free tier plus < $5/month for Claude API calls

What we learned

Synthetic data can be just as powerful for prototyping real-world civic tech Few-shot prompting (feeding 5-7 formatted examples) gave Claude a consistent executive tone; zero-shot was hit-or-miss. Don’t feed your LLM raw zeros without thinking about positional anchoring bias.

What's next

for Transit AI 📱 Public-facing dashboard for real-time safety & crowding info - riders can choose to make complaints public or anonymous. Women and vulnerable riders get real-time flags for trains/stations with rising harassment reports from others (crowd-effort). Live crowding + peak-hour heat-map, letting anyone plan a quieter or less-packed trip. 🗓️ Event integration (Eventbrite, city APIs) to detect cleanliness/crowding issues around big venues. 🔄 Swap in real 311 data from public transportation agencies - exact schema, just a simple Supabase import. Capture more insights. 📩 Share weekly PDF insight packets with the city’s transit ops team. 💬 Community feedback loop. Open source the API so advocacy groups can build add-ons (e.g., accessibility widgets, language-specific safety tips). 🧠 RAG-based querying (Retrieval-Augmented Generation) - use Supabase pgvector to push raw reports into a vector store and answer questions like, "Why are failures high at 85 °F?" by doing a similarity search for relevant historical incidents 🛠️ Trip-planner plugin — let journey apps like Google Maps pull in safety & crowding scores. 📊 ML classifiers — small models (logistic regression, XGBoost) to auto-detect likely harassment reports or maintenance issues.

Analysis

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Technology

Found in codeNot checked
  • AnthropicUnchecked
  • Next.jsUnchecked
  • Node.jsUnchecked
  • OpenAIUnchecked
  • ReactUnchecked
  • SupabaseUnchecked
  • Tailwind CSSUnchecked
  • VercelUnchecked

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