Design-Based Research Capstone

Design-Based Research Project Brief: SD Auto — Intelligent Routing Platform

Enhance daily commutes in San Diego through real-time traffic data, community hazard reporting, and AI-driven route optimization — making transportation more efficient, reliable, and adaptive.

View Frontend View Backend Active Full-Stack Development

Project Leads: Ahaan, Arnav

SD Auto

AI-Driven Intelligent Routing for Smarter Commutes

  • ML-Powered Smart Route Finder (Scikit-learn)
  • Real-Time Hazard Reporting & Map Visualization (Leaflet)
  • Daily Routine Planner with Automated Scheduling
  • Favorite Locations with Quick-Access Saved Routes
  • Location-Verified User Authentication System
  • Community-Driven Crowd-Sourced Traffic Data
Flask & Python Backend TailwindCSS & JavaScript Frontend Leaflet Interactive Maps Scikit-learn ML Models SQLAlchemy & REST APIs Pandas Data Processing

About

SD Auto is a full-stack intelligent routing platform designed to enhance daily commutes in San Diego, California. By combining real-time traffic data, community hazard reporting, and machine learning-driven route optimization, SD Auto helps commuters navigate congestion using smarter tools. The platform features an ML prediction engine built with Scikit-learn that analyzes historical traffic trends, time-of-day patterns, and crowd-sourced hazard data to deliver optimal routes. Built with a Flask/Python backend, Leaflet-based interactive maps, and a TailwindCSS frontend, SD Auto transforms unstructured traffic information into actionable routing intelligence.

Impact

  • Reduces Commute Times with AI-Optimized Routes
  • Improves Road Safety via Community Hazard Alerts
  • Automates Daily Trip Planning & Scheduling
  • Provides Real-Time Traffic Visualization
  • Enables Crowd-Sourced Hazard Intelligence
  • Location-Verified Secure User Experience

Interested Contacts

Arlene Mordeno Accenture arlene.mordeno@accenture.com
Bhanu Karuturi Mod Rail Systems bkaruturi@modrailsystems.com
Dr. Douglas Fenner 858-207-7948

CTE Expo Reflection

May 21, 2026 — Room A101
Preparation

Before attending, we expected a more formal demo presentation. We organized our files, made a script, and rehearsed, including practice going through each feature in an organized way.

Goals
  • Showcase the project and receive meaningful feedback
  • Gather new ideas to improve the platform
  • Form connections with industry experts
Key Results
  • Try to partner with San Diego County
  • Use traffic data to inform people of dangerous roads and intersections
  • Improve website security, especially for future expansion
Takeaways
  • Joint presentation experience is valuable for team growth
  • Presentations were conversational and discussion-like rather than formal
  • Time management during presentations is an area for future improvement
Panelists
Arlene Mordeno Accenture & CCOE
Bhanu Karuturi Women in Transportation
Dr. Douglass Fenner Modern Railway Systems
Instructor Feedback
The project needs to be more future-thinking and integrated with Infotainment in future vehicles. Consider what hardware would be needed in a future car.
↗ View full reflection on GitHub