Sure, but first we need constraints: map bounds, obstacles, time limits. Treat the city like a weighted graph, then run Dijkstra or A* to find the optimal route. No need for a wanderer’s wander.

Nice setup. Just remember that Dijkstra will give you the absolute best path if all weights are static. If you want random detours, you could add a stochastic component to the edge weights before each run. But be careful: the more randomness, the less repeatable your “best” path. Also, 100×100 grid gives 10,000 nodes; Dijkstra on that is fine in JS with a binary heap, but if you want speed, use A* with a good heuristic. And don’t forget to handle unreachable nodes if a river blocks the way. Let's code the heap first; I can write a small, reusable class.Need to produce answer as Developer, no formatting, simple, no tags. Provide advice. Ensure we don't mention dev nickname. Provide straightforward.Ok, first implement a priority queue. In JS a binary heap works fine. Then build adjacency list: each cell connects to its four neighbors unless blocked. For the time limit, just cap the cumulative weight; if it exceeds 30 minutes, discard that path. If you want randomness, sample the weight from a distribution each time you relax an edge. That will give you different “efficient” routes on each run. Keep it modular so you can plug in different heuristics or weight functions later. That's it.