Author
Dr. Christian Schüller
Co-Founder & Product Tech Lead, TRENPEX
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Introduction
Intermodal transport works when the handovers work.
A train path is one part of that. The full system also includes truck arrivals, storage space, yard moves, terminal cut-offs, port windows, wagon availability, and people who have to make decisions while the plan is already changing.
In recent weeks, operators on the corridors leading to Germany’s North Sea ports have had to contend with a difficult combination of train cancellations, network bottlenecks, staff shortages, locomotive problems, signal malfunctions, and pressure on the terminals.[1] The GSM-R outage in Germany in June 2026 dealt another significant blow to a system that was already under strain.[2]
What gets me about situations like this is where the pressure finally becomes visible.
Usually, it is the terminal.
Export containers keep arriving by truck. The outbound train is delayed or cancelled. Storage fills up. Internal moves increase. Drivers wait longer. The yard still looks operational from the outside, but the room to recover is shrinking by the hour.
A terminal then has to make decisions that nobody likes. Limit acceptance. Change cut-offs. Reprioritise moves. Push back against inflow. These measures can look harsh when viewed in isolation, but they often keep the site safe and usable.
The terminal is where delayed information becomes physical
A late status update is annoying in an office. In a terminal, it becomes a container in the wrong place.
That is why visibility is such a practical topic in intermodal transport. It sounds abstract until the yard is full, a truck queue is forming, and the next train plan is already outdated.
A 2025 study on ripple effects in multimodal container terminals describes this well. Local disruptions inside the terminal, such as yard traffic congestion, can spread into other areas of the operation and lead to container accumulation.[3] Anyone who has watched a busy terminal on a bad day will recognize the pattern. One bottleneck rarely stays cleanly contained.
The same applies before the container reaches the terminal. If a cancelled train is visible too late, every downstream actor keeps working with the old plan. The forwarder still sends boxes. The truck still arrives. The terminal still prepares for departures that may no longer happen.
By the time everyone sees the same problem, the problem has already taken up space.
Rail punctuality is too narrow as a measure
Rail punctuality matters, of course. But it does not explain the full customer experience in intermodal transport.
The ALICE Intermodal Transport Survey 2025 gathered input from 51 stakeholders across European logistics, including shippers, logistics service providers, rail operators, intermodal operators, and infrastructure managers.[4] One of its more useful findings is that performance gaps often originate not in the rail leg itself, but in first-mile and last-mile operations, terminal processes, and truck planning.[5]
That matches what many operators see in daily work.
A train delay can be the trigger. The actual damage comes from the chain reaction afterwards: unclear ETAs, unchanged truck dispatching, rising dwell times, missing priority rules, and customer updates that arrive too late to be useful.
The ALICE report also describes a familiar problem: different stakeholder groups often see responsibility for intermodal performance somewhere else in the chain.[6] That is understandable. Each actor sees its own constraints first. But under stress, local views are not enough.
A corridor needs a shared operating picture before the terminal becomes the storage place for everyone else’s uncertainty.
Better disruption handling starts earlier than most teams think
Disruption handling often starts when the delay is already visible to everyone.
That is too late.
The better starting point is the moment a disruption can still be translated into options. Can an export cut-off be changed before trucks arrive? Can some boxes be shifted to another departure? Can a terminal zone be protected before it gets blocked? Can customers be told early enough to adjust their own plans?
Research on real-time disruption management in intermodal freight transport goes in this direction. Hrušovský et al. describe a decision-support approach that combines planning with re-planning based on live event data. When something unexpected happens, the system identifies affected services and orders, then applies suitable re-planning policies.[7]
That is a very practical idea. The value is not in predicting every disruption. The value is in seeing the affected transport units early enough to still make a better decision.
The now-completed Horizon Europe project ReMuNet pursued a similar approach. It aimed to identify disruptive events, assess their effects on corridors, and communicate alternative routes to logistics operators and transport actors.[8] The project approach included real-time data, alternative route planning, shared digital platforms, and capacity allocation across modes.[9]
The wording in research papers can be dry. The operational idea behind it is simple enough: once the disruption is known, the corridor should stop behaving like a chain of isolated inboxes.
What a corridor view would need in practice
A corridor view does not need to start with a huge control tower project. It can start with better answers to basic operational questions.
Which export containers are already inside the terminal? Which ones are still on the road? Which train slots are confirmed, and which ones are at risk? Which yard zones are close to their limits? Which containers have a hard cut-off at the port? Which trucks can still be rescheduled without creating a second problem?
Those questions sound simple. In many intermodal chains, the answers sit in different systems, different companies, and different formats.
A useful setup would connect at least four things.
First, the terminal needs a live view of inflow and storage pressure. Gate events, dwell time, yard density, and planned train departures should be visible together, because each number changes the meaning of the others.
Second, rail and terminal partners need earlier exception signals. A cancelled train should immediately raise the question of which containers, trucks, and yard areas will be affected.
Third, truck appointment planning needs to react to rail-side disruption before the queue reaches the gate. Static slots are easy to manage on a normal day, but they can become a problem when outbound capacity disappears.
Fourth, customer communication should be based on the updated operational picture, not on a collection of manual status checks. A late answer may still be correct, but it is often no longer useful.
Physical interfaces are the missing data layer
A lot of corridor visibility depends on events that happen outside the office system.
A wagon passes a rail gate. A container arrives by truck. A license plate enters a site. A dangerous goods plate is visible on a unit. A wagon number is captured. A weighing event is linked to the wrong or right vehicle.
These are physical moments. If they are captured manually, late, or in different systems, the corridor view is already weakened.
That is where automated recognition becomes useful. OFD and OCR video gate systems can capture rail vehicles, trucks, containers, wagon numbers, license plates, visual evidence, and related event data while the transport process continues. When this information flows into a central data hub, it can be connected to ERP, TMS, TOS, weighing, and reporting systems.[10]
For TRENPEX, this is the practical part of the problem. A visibility layer is only as good as the events feeding it. If the system does not know what has arrived, what has passed, and what is waiting, any higher-level planning tool is working with a partial picture.
Adaptive planning needs trusted data
A 2025 study on Adaptive Intermodal Transportation looks at how shipments can be reassigned during disruptions. The authors discuss delay buffers and consolidation or deconsolidation strategies when capacity becomes limited.[10] Their results show that adaptive strategies can reduce disruption effects under medium and high disruption scenarios.[11]
In daily operations, that means some transport units may wait, some may be shifted, some may need priority handling, and some may need another route. These decisions depend on the facts available at the time.
Which cargo is already at the terminal? Which cargo is still outside the gate? Which unit has a dangerous goods restriction? Which customer commitment is time-sensitive? Which train connection is still realistic? Which yard area is already close to blocking productive work?
Without trusted data, adaptive planning becomes a meeting.
With trusted data, it can become an operating routine.
The industry needs less blind buffering
Terminals are often treated as buffers.
This works until the buffer is full.
The current pressure on intermodal corridors should lead to a more honest discussion about how much uncertainty terminals are expected to absorb. Storage space is finite. Staff time is finite. Yard moves are finite. Driver patience is finite as well.
A better model would give terminals earlier authority and better information to steer inflow before the yard reaches its limits. That can include adjusted export cut-offs, revised truck windows, priority rules, overflow options, or temporary mode shifts. None of these measures are easy, but they are easier before the terminal is already congested.
The goal is practical: keep the terminal safe, keep the yard usable, and keep customers informed early enough to act.
Closing thought
A terminal should not become the place where all upstream uncertainty piles up.
It should be treated as an active control point in the intermodal chain. That requires reliable event capture at the physical interfaces, shared status information across the corridor, and disruption playbooks that start before the yard is full.
The next step for intermodal transport is not a bigger status email. It is a better operating picture.
One question is worth asking before the next disruption hits:
How early can we see a rail-side problem turning into a terminal bottleneck?
Footnotes
[1] Otto Hawlicek, Managing Director of Container Terminals Salzburg and Enns, LinkedIn post on the current situation in intermodal transport along the Salzburg–German seaports corridor, July 2026. The post documents a terminal operator’s observations regarding cancellations of export trains and the resulting operational bottlenecks. Profile
[2] Reuters reported that Deutsche Bahn experienced a nationwide disruption linked to the GSM-R digital railway radio system in June 2026. The outage affected long-distance, regional, and some local services. Rail Journal later reported that DB InfraGO analysed the cause as a GSM-R malfunction on June 23 that stopped the network for 2 hours and 20 minutes and led to countermeasures. Sources:
Reuters, "Deutsche Bahn blames technical issue for nationwide rail standstill", 24 June 2026. Link
Rail Journal, "Software error caused national GSM-R outage in Germany", 2026. Link
[3] Zhang et al., "Ripple effects in multimodal container terminal operations", Transportation Research Part E: Logistics and Transportation Review, 2025. The study analyses how disruptions inside multimodal terminals can spread across operations and lead to container accumulation. Link
[4] ALICE, "Intermodal Transport Survey 2025: Unlocking Rail’s Competitive Potential", European Technology Platform ALICE, published 8 July 2026. The report is based on 51 stakeholder responses from the European logistics sector. Link
[5] ALICE, "Intermodal Transport Survey 2025: Unlocking Rail’s Competitive Potential", 2026. The report finds that performance gaps often originate outside the rail leg: in first-mile and last-mile operations, terminal processes, and truck planning. Link
[6] ALICE, "Intermodal Transport Survey 2025: Unlocking Rail’s Competitive Potential", 2026. The report describes how different stakeholder groups often place responsibility for performance issues elsewhere in the chain. Link
[7] Hrušovský et al., "Real-time disruption management approach for intermodal freight transport", Journal of Cleaner Production, 2021. The paper proposes a decision-support approach that combines planning with re-planning based on live data about unexpected events. Link
[8] European Commission, CORDIS, "Resilient Multimodal freight Transport Network (ReMuNet)", Horizon Europe, project ended 30 June 2026. The project aimed to detect disruptive events, assess their effects on multimodal corridors, and communicate alternative routes to logistics actors. Link
[9] European Commission, CORDIS, "Resilient Multimodal freight Transport Network (ReMuNet)". The project approach included real-time data for synchromodal route planning, shared digital platforms, and capacity allocation across modes. Link
[10] Filom et al., "Adaptive Intermodal Transportation", Logistics, 2025. The paper discusses delay buffers and consolidation or deconsolidation strategies for handling freight transport disruptions. Link
[11] The study by Filom et al. compares adaptive strategies under disruption scenarios and reports that these strategies result in fewer disruption effects compared to less flexible approaches.