Project
🛺 Ride-Share🗺️
🦋
Smarter Routing, Shorter Rides, Optimized Resources
Project Ride-Share addresses a major inefficiency in community-based transportation: outdated and suboptimal scheduling. While numerous open-source ride-share platforms exist, most prioritize the first available fit—not the best possible route. This wastes time, fuel, and driver effort.
This project builds on earlier prototype algorithm testing by integrating that work into a real-world, open-source ride-share scheduling system. It specifically targets shared community fleets—such as mini-buses or vans serving 10–14 passengers—operating across multiple zones and accommodating complex pick-up and drop-off logistics.
🧠Overview & ⚙️Technical Highlights
Core Components:
âś… Initial scheduling Algorithms has been tested and rated.
- Open-Source ride-share:
â—¦ Not reinventing the wheel. Find and review existing packages to work with. Update this section once specific modules are selected - Middleware Layer:
â—¦ GitHub split-repo structure to manage OSS base + custom modules
â—¦ Clean API interfaces between scheduler, and ride database - Backend Logic:
â—¦ Custom optimization algorithms for stop packing and route scoring
â—¦ Zone-based heuristics for multi-pickup scenarios
â—¦ Metrics engine for tracking gains (time saved, distance reduced, etc.)
✍️ Creator’s Note
From personal experience driving a small transit bus that fills in the gaps left by traditional public transport, I’ve seen how outdated scheduling systems impact real people—especially those with disabilities or those living far from fixed routes.
Most current systems in use (and many OSS ride-share platforms) don’t evaluate the route itself. Instead, they check only whether a ride “fits” in the schedule, often at the expense of efficiency. This results in nonsensical routing—e.g., a two-block ride turning into a 45-minute trip across town and back—based solely on request order rather than route quality.
My goals:
Plug in my scheduler as a modular upgrade to what already exists, and publish the results as a case study in open-source collaboration.
Extend my experience in working with GitHub OpenSource coding.
🚧 Development Timeline
âś… Extensive testing of different scheduling algorithms.
âž–Phase 1: OSS Audit & Codebase Selection
• Review and test leading open-source ride-share systems
• Select a baseline codebase with accessible integration points
• Create GitHub split-repo structure for core vs. custom logic
âž–Phase 2: Algorithm Integration & MVP
• Embed custom scheduling algorithms into the selected OSS base
• Compare legacy vs. optimized route outputs using synthetic test cases
• Build route visualizer to inspect logic and inefficiencies
âž–Phase 3: Real-World Simulation & UI Additions
• Simulate passenger flow and request patterns from actual routes
• Add frontend tools to monitor scheduling decisions in realtime
• Begin tuning algorithms based on performance metrics
âž–Phase 4: Documentation, GitHub Sharing & Community Involvement
• Write full setup documentation and scheduler architecture breakdown
• Publish “lessons learned” blog posts and walkthrough videos
• Open repository for contributions and review at least one pull request
• Measure KPIs: GitHub stars, merged PRs, and scheduler performance gain