TransX Risk Predictor
Reefer Shutdown Probability Model
TransX Reefer Analytics
Fleet-wide analytics platform for reefer shutdown risk prediction, cost modeling, maintenance optimization, and repair pattern analysis.
Quick Stats
Unit Selection
Trip Details
Maintenance History
Auto-filled from unit dataSelect a unit and trip details,
then click Calculate
Training Data Context
Risk Tiers
Filters
Risk Tier Distribution
fleet baseline belowGroup By
Repair & Maintenance Pattern (current slice vs fleet baseline)
per-unit averages| Metric | Slice Avg | Fleet Avg | Δ vs Fleet | Distribution |
|---|
PM Delay vs Risk & Shutdown Rate (current slice)
| PM Status | Units | Avg Risk % | Shutdown Rate % |
|---|
Fleet Risk Ranking
click column header to sort| # | Unit | Trailer | Model · Yr | Make | Last Lane | Svc | Risk % | Tier | Trips | SDs · Rate | 90d Reps | Comp Codes | Last PM | PM Overdue | Total Repair $ |
|---|
Make × Model × Year × Season
Risk is re-scored for each unit under each of the 4 seasons (other features held constant). Highlights which cohorts are most weather-sensitive.
Fleet Seasonal Baseline (avg risk if every unit ran in that season)
Cohort × Season Heatmap
| Make | Model | Year | Units | Winter | Spring | Summer | Fall | Swing max−min |
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Most Weather-Sensitive Cohorts (largest gap between worst and best season)
| Cohort | Units | Worst Season | Worst Risk | Best Season | Best Risk | Swing |
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Reefer Model Deep-Dive
Per-model history: shutdown rates, age cohorts, component patterns, and action items. Uses observed historical trip data.
Worst Performing Cohorts
Model + year combinations ranked by shutdown rate — identifies specific vintages to target
| Model | Year | Age | Units | SD Rate % | Avg Risk % | Cost/Trip | Elevated+ | Top Component Issue |
|---|
Component Vulnerability by Model
Avg repairs per unit — darker = more repairs. Identifies which components fail on which models.
Risk by Model × Year
Full breakdown showing age effect per model
| Model | Year | Age | Units | SD Rate % | Avg Risk % | Cost/Trip |
|---|
Actionable Recommendations by Model
Component Group Failure Rates
Trips with this component group repaired in the pre-trip window — sorted by failure rate. Lift = how many times riskier vs baseline.
| Component Group | Trips | Shutdowns | Failure Rate | Lift | Risk Bar |
|---|
Repair-to-Trip Turnaround
How soon after the last repair the unit was dispatched — shorter gaps = higher risk
Diagnosis Resolution
Was the diagnosis followed by an actual component fix before the trip?
Failure Rate by Repair Reason
Was the pre-trip shop visit a PM, general repair, warranty, etc.?
| Reason | Trips | Failure Rate | Lift |
|---|
Description Keywords
Keywords in repair descriptions (14d window) and their associated failure rates
| Keyword | Trips | Failure Rate | Lift |
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Dangerous Repair Combinations
Two component groups repaired within 14 days before a trip — combinations with highest failure rates
| Component A | Component B | Trips | Shutdowns | Failure Rate | Lift | Risk Bar |
|---|
Repair Sequences (Order Matters)
Last two work orders before a trip, in chronological order. The sequence reveals whether issues escalated or went unresolved.
| First Repair | Second Repair | Trips | Shutdowns | Failure Rate | Lift |
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Repeat Repair Impact
Same component group repaired 2+ times in 30 days before a trip vs single occurrence — repeat = unresolved issue
| Component Group | Pattern | Trips | Shutdowns | Failure Rate | Lift |
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Detailed Comp Code Breakdown
Individual repair codes with 5+ occurrences in the pre-trip window
| Code | Group | Trips | Shutdowns | Failure Rate | Lift |
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Select Reefer
PM Cost Breakdown by Code
Each PM track predicted independently, costs summed
Scenario Planner (New Reefer)
Predict PM and GENREP cost/hr for a hypothetical new reefer. Assumes perfect maintenance behavior (0 neglect, 0 delays, healthy telematics).
Asset Details
Maintenance Plan
Expected Usage
Select Unit
SHAP Cost Drivers
Per-unit SHAP values in CPM units. Positive = increases cost, negative = reduces cost.
What-If Analysis
Adjust feature inputs and get predicted GENREP CPM, PM CPM, and Total CPM instantly.
Field definitions
CPM Ranking (Lowest to Highest)
Fleet-wide CPM ranking with HIGH/MEDIUM/LOW bands (P20/P80 thresholds)
| # | Unit | Pred CPM | Pred Cost | Total Miles | Rail Miles | Hwy Miles | Band |
|---|
GENREP Features
PM Features
Validation Checks
Global SHAP importance shows average CPM contribution magnitude by feature across the fleet. Higher mean |SHAP| = stronger average influence on CPM prediction.
GENREP Driver Pattern
PM Driver Pattern
Feature Glossary
| Feature | Description |
|---|
3-Month Pilot Validation
Capture fleet-risk snapshots, compare predictions vs observed outcomes, and track intervention impact on fill-rate over the pilot window.
Snapshot Library
Snapshots persisted in webapp/data/snapshots/ are loaded from the repo. Click "Save Current Snapshot" to download a JSON you can commit there.
| Captured | Label | Source | Units | Avg Risk | Shutdowns | Tier Mix | Notes | Actions |
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Compare Snapshots
| Unit | Group | Baseline Risk | Current Risk | Δ Risk | Tier (Base → Now) | New SDs | 90d Reps Δ | Repair $ Δ |
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Pilot Groups
Assign units to Intervention (receiving the proactive action) or Control. Stored in your browser; export to JSON to share with the team.
| Unit | Tier | Risk % | Last Lane | Shutdowns | 90d Reps | Fillable | Assignment |
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Data Refresh & Model Retrain
Stage updated source files for the next model refresh. This page validates that all required files are present and flags filename / extension mismatches before the project team runs the Python retraining pipeline.
Required Source Files
Files staged here are kept in your browser session only. The project team runs python scripts/refresh.py server-side to retrain.
| File | Target Path | Source | Uploaded Name | Size | Uploaded | Status | Action |
|---|
Optional Files (pipeline continues without them)
| File | Location | Impact if Missing |
|---|---|---|
| Quarterly Telematics Q1–Q4 | data/telematics/quarterly/Daily Telematics Update -YTD 2025 Q*.xlsx | No alarm / restart / low-fuel features |
| Fuel Data (per year) | data/fuel data/MMA 20XX Fuel Data.xlsx | No fuel-fill features |
| Weather Cache | data/training/weather_cache.csv | Auto-fetched from Open-Meteo API |
Pipeline Steps
What scripts/refresh.py does once the project team runs it. Typical runtime: ~10–15 minutes.
- 1Validate data files exist — confirms all required files are at their expected paths.
- 2Feature engineering (
build_trip_features_v3.py) — reads raw data, builds 199-column trip-level feature CSV →data/training/trip_features_v3.csv. - 3Model training (
build_risk_engine.py) — calibrated XGBoost on temporal train/test split (Jan–Sep / Oct–Dec) →data/risk_engine/*. - 4Webapp export (
export_webapp.py) — converts model artifacts to webapp-compatible JSON →webapp/data/*. - 5Repair pattern analysis (
repair_pattern_analysis.py) →webapp/data/repair_patterns.json. - 6Validation & smoke test — model loads and scores a sample trip; AUC ≥ 0.70 expected.