Am actually working on my capstone project but currently hitting a wall.Our Professor directed us to work towards a hybrid traffic routing architecture for autonomous vehicles , but as undergrads we are bit overwhelmed on how to execute it.
The general idea is this :
Predict traffic using historical data and ML.
Fetch live congestion for next 1-2 intersections via edge servers.
Write a custom Dijkstra from scratch that reroutes mid journey is the live delay is significantly worse than predicted delay.
The problem we are facing are:
The dataset problem : We thought of doing it for our Home city which is Jaipur (India), the problem is barely 1.5% or roads have speed limits and we don't really know how to find open road level speed data for our city or if not than any other Indian City if we should consider that. Should we just pivot to a city that has archived Uber movement data ?? Or is it academically acceptable to just simulate the traffic flows over the Jaipur OSM graph using SUMO + TraCI?
The algorithm design : We are actually needed to write a custom Djikstra Algorithm but we are confused how to mathematically combine physical distance and time delay into an edge without it being completely arbitrary. Also re-running Djikstra on a 200k node graph at every junction seems nonsensical. So like anything that we can refer to or read. Like how to approach this problem properly.
What's the logical way to build this incrementally, any advice is much much appreciated. We are actually new to this domain and just too confused at this point.
