3 posts tagged with "valhalla"

A walking route across a city and a hiking route up a mountain should not share the same clock.

Routing engines are good at distance, turn costs, access rules, and road classes. Hiking time is harder. A two-kilometer trail can be easy, slow, dangerous, or all three, depending on slope, descent, surface, visibility, and how the local trail network publishes reference times.

Globus routing is built on Valhalla, and Valhalla tiles already contain the graph, the route shape, and elevation. We wanted to know whether that information was enough to make hiking ETAs less optimistic, without breaking tile compatibility and without a formula that only works in one mountain range.

We built a corpus of reference hiking times, tested several formulas, rejected most of them, used a deliberately expensive research hook to find what worked, and then moved the result into production Valhalla code.

The first Globus predicted-traffic release for Valhalla had a deliberately simple shape: coverage everywhere, and richer temporal detail only where the data could support it.

The June navigation data update keeps that shape. There is still no new API, no separate traffic overlay, and no integration change for SDK customers; the traffic layer is still baked into the regular map and navigation data update.

The changes are in the model underneath. It now handles local time correctly, treats recent evidence more carefully, and does not widen weekly profiles where freshness already explains the signal better.

At 08:12 on a Monday, a delivery driver leaves the depot on a route built on Sunday afternoon. The routing engine that built it does not know that the arterial out of the warehouse jams between 08:00 and 08:30. The driver knows, and so does anyone who lives nearby.

Most people assume predicted traffic already works in their routing engine. Valhalla supports it, but only if someone feeds it good data. Over the last several months at Globus, we have been doing that work.

This post covers what we shipped, what we threw away, and why a deliberately small layer turned out to matter more than a more ambitious one.