Five Road-Data Problems Every North American Fleet Team Knows Too Well

If you build routing, dispatch, or telematics software for North American trucking, your roadmap has a recurring tax line on it — road data. Not the routing algorithm, not the UI: the unglamorous job of knowing what’s actually happening on the pavement, in fifty states and a dozen provinces that each run their own system their own way. Below are the five problems that come up in nearly every conversation we have with fleet and logistics-software teams. None of them are exotic. All of them quietly burn engineering time.

1. Fifty States, Fifty Dialects

There is no single “US traffic API.” There are dozens of state and provincial 511 systems, and they agree on almost nothing — different formats (some JSON, some XML, some an ArcGIS layer, some a scraped map endpoint), different field names for the same concept, different authentication, different refresh rates, different ideas of what “severity” even means. A team that wants national coverage ends up writing and maintaining a separate integration per jurisdiction, then babysitting all of them as feeds silently change shape.

This is the problem that eats the most time, because it never finishes. A feed that worked in March drops a field in September; a state migrates its portal and the old endpoint 404s. We wrote up how we normalize 30+ of these systems into one schema — the short version is that the per-jurisdiction mess becomes one consistent events and features contract, and keeping the adapters alive becomes our problem instead of yours.

2. The Bridge Your Truck Doesn’t Fit Under

A consumer nav app routes a 13′6″ trailer under a 12′8″ bridge without a second thought, because it has no idea a truck is driving. Low-clearance strikes are expensive, dangerous, and almost entirely preventable with the right data on the route — but that data is scattered across state bridge inventories and the federal National Bridge Inventory, in formats never meant for real-time routing.

The operational need isn’t “a database of bridges.” It’s “tell me about the low ones on this corridor, before the driver gets there.” That’s a join between clearance data and the route geometry, and it’s exactly the kind of thing teams underestimate until the first claim comes in. More on that in bridge clearances for fleet routing.

3. The Limits That Appear Every Spring

Seasonal weight restrictions — spring thaw load limits, frost laws — are one of the most under-served data problems in North American trucking. They’re posted by states and counties on their own schedules, in their own formats, sometimes as a PDF, and they change as the ground does. A load that’s legal one week is over the limit the next, on the same road.

For a planner, the cost of missing one is a fine, a forced reroute, or a refused load. For a software team, the cost is trying to track a hundred separate posting authorities by hand. We pulled this together so the restriction shows up as structured, queryable data alongside everything else — see spring load limits as an API.

4. Where the Clock Runs Out — and Whether There’s Parking

Hours-of-Service compliance and the chronic truck-parking shortage are really one problem wearing two hats. Knowing a driver will hit the 11-hour driving limit at 6:40 PM is only half of it. The half that strands a truck is whether there’s a legal place to stop when the clock runs out — and that answer needs both the HOS projection and live parking data on the same corridor.

Most teams solve the first half and discover the second half at the roadside. We tied them together: send the driver’s clock with the route and get back every required break and stop, each paired with the parking reachable before the deadline and a feasible flag when none is. That’s the subject of HOS break planning on the route.

5. The Data Is Stale the Moment You Cache It

Incidents, closures, and work zones are only useful while they’re current. A closure feed you poll once an hour is a closure feed that’s wrong for up to an hour — and a routing decision made on a stale closure is a driver sitting in a backup your system told them to enter. But polling every state’s feed aggressively, parsing it, deduping it, and aging out the resolved events is its own piece of infrastructure that has nothing to do with your product.

The honest answer here isn’t “real-time magic” — it’s that the freshness and lifecycle work should already be done by the time you query. Events carry their own start and end times and move from active to archived on their own; you ask for what’s current on a corridor and get the current answer, with the polling cadence handled upstream. Pair it with the camera feeds for visual confirmation (traffic camera feeds) and the dispatcher has eyes on the corridor without standing up a single scraper.

The Common Thread

Every one of these is the same shape of problem: the data exists, but it’s fragmented across authorities, published in incompatible formats, and only useful once it’s joined to a route and kept fresh. Each one is solvable in-house — and each one, solved in-house, becomes a permanent maintenance commitment that competes with the product you actually sell.

That’s the entire premise of Road511: do that aggregation once, for everyone, and hand you one normalized API — events, features, bridge clearances, restrictions, routing with corridor hazards — instead of fifty integrations to keep alive. You spend your engineering time on the part that makes your product yours.

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• API docs — the events, features, and truck-routing surface in one reference
• Live map — see the normalized data across states and provinces
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