AR for Urban‑Transit Planning

**Case Study: AR for Urban‑Transit Planning**

Our team aimed to explore how augmented reality could aid planners and citizens in visualising and evaluating new public‑transit proposals. We built an interactive AR experience for Meta Quest that allows users to draw proposed lines, place stations, and see the impact of those decisions on neighbourhood access.

**Project Overview**

- **Motivation:**  Boston’s transit system is complex; existing tools make it hard to communicate how new lines reshape access.  AR offers an intuitive way to visualise network flow and neighbourhood effects.

- **Scope:**  Develop a tabletop AR application that ingests real-world data (MBTA API and city GIS layers), simulates ridership and accessibility, and visualises results with heatmaps and charts.

**Methodology & Implementation**

- **Infrastructure & Data Integration:**  Built a three‑layer architecture that combines MBTA v3 APIs, Boston geospatial datasets, and a Unity/Quest 3 MR front‑end.  Set up data parsers to pull schedules, station coordinates, and demographic layers.

- **Core Simulation Engine:**  Implemented algorithms to compute travel times, walking-distance accessibility, and potential demand for each candidate station.  Generated ridership and accessibility scores on the fly as users draw lines.

- **Interactive Design:**  Created an intuitive drawing loop with controller ray‑casting and hover highlighting.  Users can sketch proposed transit lines on a tabletop city model, place candidate stations, and immediately see neighbourhood-level impact bars and heatmaps update.

- **Visualisation Layers:**  Added world-space UI panels for reading accessibility scores, bar graphs representing ridership potential, and colour‑graded heatmaps for walking accessibility.  Built a scenario comparison mode allowing users to flip between multiple proposals.

- **Performance Optimisation:**  Refactored geometry processing to improve frame rates; leveraged occlusion culling and asynchronous data loading to ensure smooth interaction on Quest hardware.

**Results & Impact**

- Demonstrated a fully functional AR transit-planning tool at the course finale.  Users could propose new lines and instantly see how neighbourhood accessibility scores changed, fostering a deeper understanding of transit equity.

- Feedback from testers emphasised the intuitive interaction pattern and the value of visualising competing scenarios side by side.

- The project illustrates the potential of AR to democratise urban planning discussions by making complex spatial data accessible to non-experts.

**Lessons Learned**

- Integrating live APIs and GIS data into AR experiences requires careful architecture; caching and pre‑processing can mitigate performance bottlenecks.

- Clear, minimal UI elements (hover feedback, legends, timeline scrub) are crucial for readability in passthrough environments.

- Scenario comparison and data layers empower users to explore design trade‑offs rather than just passively view a single outcome.