Motor Truck (For-Hire) Carrier Ranking Model
This model produces a single, auditable safety ranking for for-hire property carriers. It combines four components—BASIC-based safety, crash rate, critical violation rate, and experience— into a combined_score (0–100), then ranks carriers and assigns a percentile-based grade (A+ to F).
The score is a relative safety indicator within the eligible population. It is not a loss predictor, a guarantee of future performance, or a replacement for underwriting judgment. It is designed to be transparent, stable for small fleets, and repeatable.
The model uses publicly available FMCSA data (conceptually):
The latest BASIC record is selected deterministically by date (never an arbitrary row).
The model targets for-hire, property/cargo motor truck carriers with active operating authority and at least one power unit. Exclusions are applied to avoid non-comparable operations:
Carriers with authorized or exempt for-hire authority (including US Mail and governmental authority when present) remain in-scope.
Events are counted over a fixed evaluation window of 24 months. Exposure is defined as window mileage (annual mileage × 24/12) and expressed in 100k-mile units for rate calculations.
Exposure-adjusted rate:
$$r_i = \frac{y_i}{E_i}$$Where $y_i$ is event count and $E_i$ is exposure (in 100k miles).
To stabilize rates for small fleets, crash and critical violation rates are shrunk toward the fleet mean using a Poisson–Gamma empirical Bayes model (credibility weighting).
Likelihood:
$$y_i \mid \lambda_i \sim \text{Poisson}(E_i \lambda_i)$$Prior:
$$\lambda_i \sim \text{Gamma}(\alpha, \beta)$$(shape $\alpha$, rate $\beta$)
Posterior mean (Empirical Bayes rate):
$$\hat{\lambda}_i = \frac{\alpha + y_i}{\beta + E_i}$$Fleet-level method of moments (trimmed for robustness):
$$\mu = \text{mean}(r_i) \qquad \sigma^2 = \text{var}(r_i)$$ $$\alpha = \frac{\mu^2}{\sigma^2} \qquad \beta = \frac{\mu}{\sigma^2}$$Extreme rate outliers are trimmed before estimating $\mu$ and $\sigma^2$ to reduce the impact of data anomalies.
Worked Example
Suppose a carrier has 2 crashes in 200k miles. Exposure $E_i = 2.0$.
If $\alpha = 1.2$ and $\beta = 3.0$, then:
Uses the latest BASIC snapshot and active alerts across the 7 categories. Alerts reduce a 0–100 starting score, capped to avoid over-penalization.
Uses EB-shrunk crash rate per 100k miles and compares to fleet mean.
Uses EB-shrunk critical/severe violation rate per 100k miles and compares to fleet mean.
Years since authority date mapped to 0–100 via a saturating curve.
Rate ratios are converted to scores with a smooth, monotone sigmoid. This avoids hard clamps and makes score changes gradual and defensible.
Sigmoid Score Mapping:
$$\text{score} = \frac{100}{1 + \left(\frac{RR}{k}\right)^p}$$Where $RR = \dfrac{\hat{\lambda}_i}{\bar{\lambda}}$ is the rate ratio, with $k = 1.0$ and $p = 1.5$
Illustrative curve: RR=1 yields a mid‑range score, RR<1 increases scores, RR>1 decreases scores.
The combined score is a fixed weighted average:
Only eligible carriers receive a rank and grade. Eligibility requires window miles ≥ 100k or at least 1 inspection in the 24-month window. Low‑credibility carriers are flagged but not ranked.
Rankings are deterministic: sort by combined_score (descending), then by window miles (descending), then by DOT (ascending). Grades are assigned based on the carrier's combined score value:
In addition to the FRED Score, we provide the Inspection Selection System (ISS) score, implementing the FMCSA December 2012 ISS-CSA Safety Algorithm. ISS is used by roadside inspectors to prioritize which carriers to inspect.
High-priority carriers with multiple BASIC alerts or high-risk indicators. OOSO carriers receive ISS=100.
Moderate-risk carriers with some alerts. Inspection at officer discretion.
Low-risk carriers with no active alerts. Lower inspection priority.
ISS Algorithm Details
FRED vs ISS: FRED Score is our comprehensive proprietary risk assessment using EB-shrunk rates and multiple factors. ISS is the official FMCSA inspection prioritization score. Both are valuable — FRED for underwriting decisions, ISS for regulatory compliance context.
Governance: Model inputs, weights, and thresholds are versioned. Recalibration is recommended on a regular cadence (e.g., quarterly or when data distributions shift), with back-testing and documentation of changes.