Intermodal Volume Rose 6.9%: Find the Rail Conversion Lanes Hidden in Weekly Data

One strong week of rail traffic does not justify moving an entire truckload portfolio to intermodal. It does justify asking whether the network has changed enough to revisit lanes that failed the test a year ago.

For the week ending September 19, U.S. railroads moved 301,456 intermodal containers and trailers, up 6.9% year over year, according to Logistics Management's report on Association of American Railroads data. FreightWaves put total U.S. rail traffic for the same week at 535,663 carloads and intermodal units, up 4.9%.

Those figures are not a universal buy signal. They are a capacity and demand signal that should trigger a structured lane screen. The prize is not the lowest rail linehaul quote. It is a repeatable door-to-door move that reduces total cost without breaking the customer's delivery promise.

Start with lanes, not an enterprise-wide target

A mandate such as “convert 15% of truckload volume to rail” starts at the wrong level. Intermodal suitability depends on the specific origin-destination pair, shipment cadence, terminal access, equipment needs, and service window.

Build a lane table from the previous 12 months of truckload history. At minimum, include origin and destination postal codes, loaded miles, weekly loads, pickup day and time, requested delivery, actual transit, weight, cube, commodity, equipment, accessorials, and claims. Then add the nearest viable origin and destination ramps, estimated dray miles, rail schedule, cutoff, availability, and free-time rules.

Distance matters because the rail linehaul needs enough miles to offset two dray moves and two terminal handoffs. Yet total lane geometry matters more than a simple minimum-mile rule. A long highway route with a 300-mile destination dray can be weaker than a shorter route connecting two dense intermodal markets. Inbound Logistics notes that shippers within roughly 150 miles of a metro area typically have a viable option because most drayage networks are designed around that radius.

Use that as a screening guide, not a promise. Congestion, appointment constraints, driver supply, chassis availability, and terminal hours can make 50 difficult dray miles more expensive than 100 predictable ones.

Filter for schedule, density, and equipment fit

After geography, test whether the freight can support the operating pattern.

Schedule fit: Map the truck's actual departure and arrival pattern against rail cutoffs and availability. A nominal three-day rail service is useless if a late pickup misses the Friday cutoff and adds a weekend. Favor freight with delivery windows wide enough to absorb normal terminal and linehaul variation.

Density: Consistent volume improves dray procurement, equipment positioning, and recovery options. A lane with five loads every week is a better pilot candidate than a lane with 20 loads in one promotion week and none afterward. Use weekly volume distributions rather than monthly averages, which can hide volatility.

Shipment profile: Dry, non-urgent, full-container freight with stable dimensions is the cleanest starting point. Validate weight distribution, blocking and bracing, temperature control, cargo value, hazardous-material rules, and damage sensitivity. Intermodal introduces additional lifts and different in-transit forces; packaging that works in a truck is not automatically rail-ready.

Network balance: Examine return demand and empty repositioning. Even if the shipper buys a one-way service, imbalanced equipment markets can surface as higher rates, poor availability, or inconsistent acceptance.

Compare the complete door-to-door cost

Intermodal can create meaningful savings. An Inbound Logistics analysis cites an 8% to 18% cost advantage versus over-the-road trucking. But a percentage benchmark is not a business case.

Calculate the expected cost for each candidate lane as:

Origin dray + origin terminal charges + rail linehaul + destination terminal charges + destination dray + fuel and accessorials + expected exception cost

Compare that with the truckload invoice, including fuel, detention, layover, stop-off, and recurring service recovery. Add inventory carrying cost for any extra transit time. A lower transportation rate can be erased if an added day requires more safety stock or creates frequent expedites.

Model reliability as money as well. Estimate the cost of late delivery, missed appointments, production disruption, customer penalties, and manual intervention. Use a range rather than a single optimistic number: expected cost under normal operation, a realistic disruption case, and a severe but plausible case.

The decision metric should combine savings and service. A lane offering 12% modeled savings with fragile cutoffs may be worse than one offering 7% with stable schedules and easy recovery.

Run a measured pilot with explicit thresholds

Select a small group of lanes with strong geography, recurring density, compatible freight, and enough delivery flexibility. Run the pilot long enough to encounter normal weekly variation—typically several shipment cycles rather than one showcase load.

Set the scorecard before the first tender:

• Tender acceptance: the percentage of planned conversions the provider accepts without repricing or changing the schedule.
• Door-to-door transit: actual pickup-to-delivery hours, not published ramp-to-ramp service.
• ETA variance: the spread between promised and actual delivery, including both average and worst-case performance.
• Origin and destination dwell: time spent waiting for ingate, availability, outgate, and final delivery.
• Damage and claims: frequency, severity, cause, and packaging observations.
• Total landed transportation cost: every dray, terminal, rail, accessorial, detention, storage, and recovery charge.

Define go, adjust, and stop thresholds. For example, a pilot might require at least 95% tender acceptance, no increase in damage, a stated on-time floor, and net savings after inventory and exception costs. A missed threshold should open a diagnosed exception—not an automatic conclusion that all intermodal service is unreliable.

Turn weekly data into a repeatable conversion process

The 6.9% intermodal increase shows that shippers are putting more freight into the rail network. The useful response is not to chase the headline; it is to maintain a conversion pipeline that connects market signals to shipment-level evidence.

Refresh the lane screen monthly, revisit rejected lanes when rates or schedules change, and compare pilot assumptions with actual invoices and events. Over time, the organization develops its own evidence about which distances, ramps, commodities, and service windows work—far more valuable than a generic mileage rule.

CXTMS helps freight teams analyze lane history, compare multimodal costs, monitor milestones, and manage exceptions from tender through delivery. Request a CXTMS demo to uncover rail-conversion opportunities and test them with operational control.