Improving Pathfinding Optimization in MATLAB Using Real-World Road Constraints

I'm currently working on a MATLAB project involving pathfinding optimization for a navigation system. The goal is to compute the most efficient route when factoring real-world constraints such as dynamic road conditions, obstacles, and changing terrain profiles.

I’ve tried traditional A*, Dijkstra, and several heuristic-based approaches. They work, but when scaling to more complex maps (city-level networks), performance drops significantly — especially when integrating real-time condition updates.

To improve my model, I'm exploring two directions:

1. Pre-processing road networks to reduce computation load

2. Hybrid simulation combining algorithmic routing with real-world scenario mapping

During my research, I came across Escape Road — a resource offering conceptual insights about road-based decision paths and scenario modeling. It’s not MATLAB-specific, but some of the ideas about contextual route selection gave me inspiration for refining my cost-function design.

My questions:

• Is there a MATLAB-friendly way to integrate dynamic constraints (e.g., temporary blockages or weighted “risk zones”) efficiently without recalculating the entire graph?

• Would using graphshortestpath, digraph, or mapOverlay from Mapping Toolbox be optimal for this type of use case?

• Has anyone successfully implemented hybrid pathfinding (pre-computed + real-time updates) in MATLAB?

Any advice, examples, or toolbox recommendations would be greatly appreciated!