Hi pgRouting community,
My name is Abhinesh Dahal, a CS and Applied Math student at Texas State
University (graduating May 2027). I’m interested in applying for GSoC 2026
with pgRouting and wanted to introduce myself and share my idea early to get
feedback.
Idea: AQI-Weighted Routing
I’d like to add a new routing function to pgRouting that incorporates air
quality index (AQI) data as an edge cost modifier. The goal is to let users
find the lowest-pollution path between two points which is useful for pedestrians,
cyclists, runners, athletes rather than just the shortest or fastest route. The
approach would pull real-time data from the OpenAQ API, map readings to
geographic edges, and integrate as a cost parameter alongside distance in
existing shortest-path functions like pgr_dijkstra.
Background:
- Comfortable with C++, Python
- Experience with graph algorithms (Dijkstra, BFS/DFS)
- Built ML models on real world datasets to help foster children of Texas during an internship
- Currently setting up pgRouting locally and exploring the codebase
I’m aware this is an original idea not on the existing ideas list. I reviewed the 2026 ideas list at ( GSoC Ideas: 2026 · pgRouting/pgrouting Wiki · GitHub ) and didn’t see an exact match, so I’m proposing an original idea, I’d be more than happy to anchor it to an existing one if mentors prefer.
Looking forward to the community’s feedback.
Abhinesh (GitHub: DahalAb1)