That’s a classic puzzle, isn’t it? Imagine the trail as a graph where every node is a trail junction and every edge is a segment with a danger score that fluctuates like a heart rate monitor during a hike. If you feed live telemetry from GPS collars and animal movement logs into a Bayesian network, you can get a probability distribution for “safety” at each point. The trick is that the data is noisy—think of a squirrel’s GPS ping jittering like a hiccup. You could smooth that with a Kalman filter, but then you risk washing out those sudden, rare predator encounters that matter most. So the model’s a balance: enough granularity to catch the real risk spikes, but enough aggregation to avoid overfitting the data to a few outliers. The end goal is a dynamic heat map you can overlay on a map app, letting hikers see the safest route in real time, like a living, breathing decision tree. The challenge? Getting enough real‑time data to keep the model from predicting the safest path as the one that never changes—because that would be boring and, honestly, probably not safe.