Know the Risk
Before You Route

Llano is route safety scoring for trucking fleets. Real crash history, live weather, and road geometry — ranked alternatives in a single API call.

Try the Demo Get Free Access API Docs

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THE PROBLEM

Route Choice Is the Blind Spot

700+

Texans killed in commercial truck crashes in 2023. Texas leads the nation — every year.
Source: TXDOT Crash Statistics

The industry monitors drivers. Not routes.

Dashcams, ELD compliance, driver scoring — fleets have invested heavily in safety technology. But the route itself goes unscored. Two roads between the same cities can carry crash rates that differ by 3× or more depending on geometry, traffic, and time of day.

Conditions change between dispatch and delivery.

A route that looked fine at 6 AM looks different at noon in a thunderstorm. Real-time weather, congestion, and departure time all shift crash probability on specific segments — and none of that is visible to a dispatcher today.

When something goes wrong, the record matters.

Fleets that dispatched safely and can prove it — with scored alternatives, logged conditions, and a documented choice — are in a fundamentally different position than those that can't. The paper trail is a byproduct of doing the right thing.

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HOW IT WORKS

From Route to Risk Score

A statistical model fit to years of TXDOT crash records, adjusted in real time for weather and departure time.

01 — BASELINE

Crash rate per segment

A mixed-effects negative binomial model estimates expected crashes per vehicle-mile on every road segment in Texas — controlling for road class, curvature, grade, traffic volume, and urban density.

02 — DYNAMIC

Real-time multiplier

An XGBoost case-crossover model estimates how current conditions (wind, precipitation, congestion) elevate crash probability relative to the same segment on a clear, free-flow traffic day.

03 — OUTPUT

Trips per crash, ranked

Alternatives are scored and ranked by expected trips between crashes. The safest route is returned first, with hotspot locations and a weather multiplier for each alternative.

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EXAMPLE

Laredo → Dallas

Three alternatives scored and ranked by safety. The faster route carries meaningfully higher risk — a trade-off Llano surfaces before dispatch.

★ RECOMMENDED

SH-130 Toll around Austin

Drive Time

6h 29m +10m

Relative Risk

0.62×

Dynamic Mult

1.15×

Absolute Risk

1 in 667 trips

154 miles: I-10 merge

⚡ FASTEST

I-35 N through Austin

Drive Time

6h 19m

Relative Risk

1.0×

Dynamic Mult

1.32×

Absolute Risk

1 in 415 trips

235 miles: I-35 construction

ALTERNATIVE

US-281 N rural cutoff

Drive Time

6h 53m +34m

Relative Risk

1.11×

Dynamic Mult

1.22×

Absolute Risk

1 in 373 trips

219 miles: Curve/grade

Snapshot values. Risk scores update continuously as weather, traffic, and incident conditions change.

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LIVE DEMO

Try It — Texas Routes

Enter a route below. Geocoding and routing are live.

||| llano

llano>

score --from "San Antonio" --to "Round Rock, TX"

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SAFETY AUDIT

Built for the Broker

The median nuclear verdict in a trucking case now exceeds $22M — up from under $1M two decades ago. Fleets that can demonstrate a documented, data-driven safety process are in a fundamentally different position when it matters most.

Auto-generated quarterly reports your fleet can hand directly to underwriters — third-party scored, not self-reported. Reports are tamper-evident, cryptographically signed at generation so the route, score, and timestamp can't be altered after the fact.

acme-logistics-safety-audit-q1-2026.pdf

Fleet Safety Audit Report Q1 2026 · January 1 – March 31

Acme Logistics Inc. Generated April 2, 2026

71/100

Good ↑ +4 pts vs Q4 2025

Overall Safety Score

4,821

Routes Scored

87%

Compliance Rate

2.1M

Miles Covered

FACTOR BREAKDOWN

Weather Exposure

74

Road Quality

81

Traffic

58

Incident History

79

Certified by Llano · Third-party scored · Not self-reported

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⚖️

Auditable for Litigation

Every routing decision is timestamped, scored, and logged at dispatch. If an incident leads to litigation, you can show exactly what safety data was available, what alternatives were considered, and what choice was made — and why it was reasonable.

📋

Insurer-Ready Format

Third-party certified scores and methodology summaries designed to satisfy underwriter requirements — not self-reported safety claims.

📈

Trend Over Time

Quarterly safety score history and period-over-period improvement give insurers the longitudinal data they need to price risk accurately.

✅

Compliance Tracking

Track how often dispatchers act on Llano recommendations. Demonstrated safety culture is more credible than any self-reported metric.

📍

Hotspot Attribution

Recurring risk concentrations mapped across your full route network — with incident correlation that builds actuarial credibility over time.

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API

Bring Your Own Router

Built around how fleet operations actually work — dispatch approves a load, the route is set, and no one has time to export geometry. Llano fits into that moment: a single API call returns a risk score before the driver rolls, with no changes to your TMS or existing workflow.

# Option A — pass your router key, Llano calls it on your behalf
curl -s -X POST https://api.llano.io/score \
  -H "X-API-Key: llno_..." \
  -H "Content-Type: application/json" \
  -d '{
    "waypoints":    [[-99.5075, 27.5035], [-98.4936, 29.4241]],
    "arrive":       "2026-04-20T18:00",
    "alternatives": 3,
    "router": { "provider": "tomtom", "api_key": "YOUR_KEY" }
  }'

# Option B — pass raw GeoJSON from your existing pipeline
curl -s -X POST https://api.llano.io/score \
  -H "X-API-Key: llno_..." \
  -H "Content-Type: application/json" \
  -d '{
    "route":  { "type": "LineString", "coordinates": [...] },
    "arrive": "2026-04-20T18:00"
  }'

# Option C — no router, Llano plans and scores alternatives
curl -s -X POST https://api.llano.io/score \
  -H "X-API-Key: llno_..." \
  -H "Content-Type: application/json" \
  -d '{
    "waypoints":    [[-99.5075, 27.5035], [-98.4936, 29.4241]],
    "arrive":       "2026-04-20T18:00",
    "alternatives": 3
  }'

import requests

# Option A — pass your router key, Llano calls it on your behalf
resp = requests.post(
    "https://api.llano.io/score",
    headers={"X-API-Key": "llno_..."},
    json={
        "waypoints":    [[-99.5075, 27.5035], [-98.4936, 29.4241]],
        "arrive":       "2026-04-20T18:00",
        "alternatives": 3,
        "router": {
            "provider": "tomtom",  # "tomtom" | "pcmiler" | "here"
            "api_key":  "YOUR_TOMTOM_KEY"
        }
    }
)

# Option B — pass raw GeoJSON from your existing pipeline
resp = requests.post(
    "https://api.llano.io/score",
    headers={"X-API-Key": "llno_..."},
    json={"route": your_linestring_geojson,
          "arrive": "2026-04-20T18:00"}
)

routes = resp.json()   # sorted safest first
best   = routes[0]

print(f"{best['name']}")
print(f"1 in {best['trips_per_crash']:,} trips")
print(f"dynamic mult: {best['dynamic_mult']}")

library(httr2)

# Option A — pass your router key, Llano calls it on your behalf
resp <- request("https://api.llano.io/score") |>
  req_headers("X-API-Key" = "llno_...") |>
  req_body_json(list(
    waypoints    = list(c(-99.5075, 27.5035), c(-98.4936, 29.4241)),
    arrive       = "2026-04-20T18:00",
    alternatives = 3L,
    router       = list(provider = "tomtom",  # "tomtom" | "pcmiler" | "here"
                        api_key  = "YOUR_TOMTOM_KEY")
  )) |>
  req_perform() |>
  resp_body_json()

# Option B — pass raw GeoJSON from your existing pipeline
resp <- request("https://api.llano.io/score") |>
  req_headers("X-API-Key" = "llno_...") |>
  req_body_json(list(route  = your_linestring_geojson,
                     arrive = "2026-04-20T18:00")) |>
  req_perform() |>
  resp_body_json()

best <- resp[[1]]   # sorted safest first

cat(best$name, "\n")
cat("1 in", format(best$trips_per_crash, big.mark=","), "trips\n")
cat("dynamic mult:", best$dynamic_mult, "\n")

ROUTER AGNOSTIC

Pass your PC*Miler, TomTom, or HERE API key and we'll call your router on your behalf. Or send raw coordinates, or let Llano route from scratch. The scored response is identical either way.

RESPONSE FIELDS

trips_per_crash number Expected trips between crashes on this route under current conditions. Higher is safer. Ranked descending.

relative_risk number Crash probability relative to the fastest available route. 1.0 is the baseline. 0.62 means 38% safer; 1.3 means 30% riskier.

dynamic_mult number Real-time multiplier applied to the base crash rate. Reflects current weather, time-of-day, and incident conditions. 1.0 = clear baseline.

duration_secs number Estimated drive time in seconds for this route and departure time.

distance_mi number Route distance in miles.

hotspots array High-risk segments along the route.

route object GeoJSON LineString of the scored path. Drop directly into Mapbox, Leaflet, or any GeoJSON-compatible renderer.

label string Human-readable route name inferred from major roads (e.g. "I-35 N through Austin").

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Ready to score your routes?

Llano is in early access. Texas coverage available today.

We'll send a magic link — no password needed Check your email ✓

Llano provides probabilistic risk scores based on historical crash data and real-time conditions. Scores are statistical estimates and do not guarantee safe passage. Always exercise independent judgment. Not a substitute for professional safety consultation.