USDA reports are careful and essential, but they are snapshots of yesterday. Rail flows are the movie of today. When you convert live rail movement into structured signals, you can see basis risk and logistics pressure build in near real time. Below is how we think about it at RailWatch, with definitions and examples you can use immediately.

What the rail tells you that reports cannot

1. Set-size pressure
   A run of 85–110 car covered hopper sets on specific corridors is an early tell for export pull. Public reports aggregate across time and region. Rail shows the clustering and cadence that precede price action.

2. Composition shift
   A rising share of covered hoppers relative to tank and mixed manifest on corridors that terminate at export or river loadouts points to grain allocation changes. You are not just counting trains. You are watching the portfolio mix of the network shift.

3. Turn velocity
   Time between first reads of the same car IDs across spaced checkpoints reveals network velocity. Shortening intervals signal tighter turns and mounting elevator or terminal pressure.

4. Directional bias
   Imbalances in loaded eastbound or southbound movement versus empties returning signal whether pressure sits inland or at the water. We track direction at the checkpoint level and roll it up by corridor.

5. Dwell and requeues
   Repeated reads of the same set within a small geography indicate congestion or requeues. That shows up in our data hours or days before it filters into anecdotes or weekly summaries.

Three proprietary signals we publish

These are simple by design, auditable by users, and built from first principles.

• Load Index (LI)
  Definition: The proportion of covered hopper counts over a corridor that meet a unit-set threshold within a rolling window, weighted by set size.
  Use: When LI lifts above its corridor baseline, export or river pull is strengthening.
  Why it works: It ignores noise from mixed manifests and reflects real commitment of cars to grain.

• Pace Index (PI)
  Definition: The median minutes per mile for trains between two checkpoints compared to the last 30 days on the same segment.
  Use: Sudden negative deltas often precede arrival bunching at the downstream terminal.
  Why it works: You do not need timetables. You learn true speed from observed movement.

• Terminal Pressure Score (TPS)
  Definition: Inbound unit trains within a corridor lookahead window divided by historical unload capacity at the terminal cluster, adjusted for recent dwell.
  Use: When TPS rises, basis volatility and staffing strain usually follow.
  Why it works: It aligns what is inbound with what the terminal historically digests.

How we track manifests and car IDs at scale

• First read
  A credit is only consumed on the first read of a listed car ID within a 24 hour window. This gives you car-level coverage without penalty for multi checkpoint corridors.

• Set reconstruction
  We build train sets from contiguous reads and camera timestamps, then attach a consistency score based on gap length, frame quality, and read agreement. You can filter by that score in the API.

• Duplicate control
  The one per day rule suppresses double counting across checkpoints. You still see every crossing in your feed with timestamps, but you do not burn extra credits on the same ID that day.

• Missed read backfill
  If a single car is obscured at one checkpoint but appears cleanly at the next, we infer continuity based on surrounding IDs and timing. The inference is labeled so you can accept or ignore it.

Forecasting terminal arrivals without fragile inputs

We do not scrape timetables or assume dispatcher bulletins. ETA forecasts learn from:

• Observed segment speeds by subdivision and time of day

• Meet and pass patterns inferred from headway changes

• Crew change and fueling yards identified by persistent slow zones

• Recent dwell history at the target terminal or cluster

Output is an ETA with a confidence band and a reason code such as congestion, weather visible in the frame, or prolonged siding dwell. You can choose conservative or aggressive bands per corridor.

A worked example you can replicate

Scenario: A trader monitors Central Illinois to Gulf export corridors in the first half of October.

1. On Monday morning, LI for the corridor climbs above its 30 day baseline. The mix shifts toward unit hopper sets, and PI between two mid corridor checkpoints improves by several minutes per mile.

2. TPS rises because three inbound unit sets stack inside the lookahead window for a two terminal cluster.

3. By Tuesday afternoon, ETA bands suggest overlapping arrivals late Wednesday.

4. Actionable use: Buy basis ahead of the bunching or adjust loadout staffing at the inland elevator to clear bins before the empties recycle.

5. Verification: When USDA inspection and shipment summaries print later, they confirm the direction but not the timing that drove the decision.

What good looks like in a dashboard or API

You do not need a new platform if you do not want one. Most teams start with alerts and a thin layer of metrics.

Useful queries

• All first reads for listed car IDs since a timestamp, grouped by corridor

• LI, PI, and TPS timeseries for a corridor with 7, 14, and 30 day baselines

• Sets that missed a read at one checkpoint but completed at the next

• Inbound ETAs for a terminal cluster with confidence bands

Operational playbooks that compound

• Trader playbook
  Watch LI and TPS together. Rising LI with rising TPS signals stronger export pull and near term congestion. Use the window between the signal and public summaries to position.

• Elevator operations
  Use PI deltas and inbound ETA bands to stage crews and bins. The cost of one misstaffed shift usually exceeds the cost of a checkpoint.

• Leasing and asset turns
  Track time between first reads across spaced checkpoints for your listed IDs. Shorten that interval to lift effective turns without adding cars.

  
  

Where we are live and how expansion works

Coverage today includes Iowa, Illinois, Nebraska, Arkansas, and Mississippi. Alabama and Georgia are scheduled to come online in 2025. We can light additional corridors or private crossings on request, and we can make a site exclusive to your team.

What this is not

It is not a commodity sentiment score. It is not a scraped dataset. It is movement you can audit, with car IDs and timestamps that align to real steel on real tracks.