How CropSafe Forecasts Terminal Arrivals Without Timetables
Most rail forecasting tools lean on timetables, dispatcher reports, or scraped bulletins. The problem is those inputs are often outdated, incomplete, or not available to commercial users. Terminals still end up reacting instead of preparing.
RailWatch takes a different approach. We forecast arrivals directly from observed movement. No fragile timetables, no black box feeds. Just checkpoint reads, car IDs, and the real physics of trains in motion.
Step One: Measure Segment Speeds
Every train has a velocity fingerprint. By measuring minutes per mile between checkpoints, RailWatch builds speed baselines for each subdivision and time of day. Grain unit trains often move at 25–32 mph on open track but slow to 8–12 mph through crew change zones. When that baseline shifts, it signals upcoming arrival windows more reliably than published schedules.
Step Two: Detect Meet and Pass Patterns
When headways between trains shrink or stretch, dispatchers are making meet-and-pass decisions. In our data, a sudden increase in spacing is the signature of one train being held in a siding. That signature repeats across corridors. A southbound set held twice on the same subdivision will typically slip six to eight hours behind the historical average arrival at its terminal cluster.
Step Three: Map Crew and Service Choke Points
Crew swaps, fueling stops, and yard work create recurring slowdowns. By tagging these choke points from observed data, we know where dwell almost always appears. For example, sets entering the Memphis area consistently show 90–120 minutes of delay, even in light traffic. Instead of treating this as noise, we bake it into ETA forecasts.
Step Four: Track Dwell History at Terminals
Arrivals depend on more than track speed. They also depend on how fast terminals unload. If the last three days show inbound sets stacking with unload times above 36 hours, our model widens ETA bands for the next inbound trains. This turns terminal dwell into a predictive factor instead of a lagging statistic.
The Forecast Output
We do not produce single timestamps. Instead, each arrival forecast has:
ETA bands that reflect corridor-specific speeds, choke points, and recent dwell.
Confidence scores based on how similar the train’s path is to historical runs.
Reason codes that flag what is driving uncertainty, such as congestion, yard dwell, or weather visible in the checkpoint frame.
Users can pick conservative or aggressive bands depending on whether they are scheduling crews, trading basis, or managing leased assets.
A Practical Example
In early October, RailWatch tracked a 95-car corn unit train departing central Illinois bound for the Gulf. Checkpoints showed it clearing two mid-corridor sites faster than its 30-day average pace, but headway data revealed it had been held once near Memphis. Terminal dwell data also showed a cluster of arrivals already backing up downstream. The resulting ETA forecast was a Wednesday night arrival window, plus or minus four hours, with a congestion reason code. The USDA report released two days later confirmed rising Gulf loadings, but users of the RailWatch feed had actionable information nearly 48 hours earlier.
Why It Matters
Accurate terminal ETAs two to three days ahead of a bottleneck let elevators clear bins before congestion, traders price basis risk earlier, and leasing firms predict car turns more tightly. This is the kind of ground-truth edge timetables never deliver.
