Executive summary
Chinese New Year 2026 is not “just” a week off in China; it’s a synchronized stop-and-start across factories, trucking, and ports that shows up weeks later as volatile vessel schedules and uneven container waves at U.S. gateways. The official Spring Festival holiday runs Feb 15–23, 2026 (with make-up workdays on Feb 14 and Feb 28), extending the national shutdown window shippers plan around.
This year’s story isn’t only about total volume, it’s about variability. Even as January 2026 TEUs at the Port of Los Angeles and Port of Long Beach were down year-over-year, carriers blanked sailings to match the holiday demand trough, which can compress arrivals into “lumpy” peaks and thin pipelines.
When those waves hit, the system’s weakest link often isn’t the berth, it’s the yard gate + yard decisioning loop. Manual, spreadsheet-driven gates don’t scale cleanly during surge days, and “first-come, first-served” yard execution can starve high-velocity cargo (reefers, critical parts, time-sensitive retail) while low-priority inventory occupies scarce slots.
Terminal Industries’ opportunity in this moment: pair AI-native, computer-vision gate automation with a Yard Operating System (YOSTM) that can prioritize moves in real time, reduce exception friction, and stabilize truck turn time, gate throughput, and dwell; the KPIs that determine whether a surge becomes a controlled pulse or a multi-week recovery.
What’s actually happening around Chinese New Year 2026
The planning reality starts with the calendar. China’s published 2026 holiday schedule puts Spring Festival at Feb 15–23 (nine rest days), with additional “adjusted working days” on surrounding weekends. That structure matters because it effectively concentrates output before the holiday, then constrains it during, followed by a staggered restart.
That shutdown rhythm reliably triggers carrier capacity management. Major ocean carriers explicitly planned blank sailings (canceled voyages) around the holiday window to avoid running underfilled ships into the demand trough:
Maersk published Transpacific schedule adjustments noting temporary reductions and listing blanked sailings with late-February first-port ETDs (e.g., Feb 26–28) as part of CNY planning.
Hapag-Lloyd outlined week-specific “no sailing” periods on multiple Asia–U.S. services, including West Coast loops blanked in weeks 8–9.
MSC stated it would blank specific voyages on Asia-to-USA routes due to the anticipated slowdown.
CMA CGM issued customer advisories on blanked sailings during the CNY period (example: Asia–LatAm services with multiple blanked dates in February).
The operational takeaway: blank sailings don’t remove demand, they reshape it. They shift cargo to earlier sailings (front-loading), later sailings (backlog), alternative strings, or different ports; creating the “bullwhip” effect where small planning changes amplify upstream and downstream volatility. U.S. West Coast ports: why “waves” matter more than totals
Even in a year where headline TEUs are not exploding, variability can still spike congestion.
January 2026 throughput at the Port of Los Angeles was about 812,000 TEUs (down ~12% YoY), with loaded imports about 421,594 TEUs (down ~13% YoY). At the Port of Long Beach, leadership reported about 847,765 TEUs in January (down ~11% YoY), with imports about 409,828 TEUs (down ~13%) and exports about 99,478 TEUs (slightly up).
So why do operators still feel “surge pain”? Because schedule shape drives arrival concurrency, which drives berth + yard + gate concurrency.
A recent trade analysis using AIS-based dwell metrics reported a sudden pattern shift in the San Pedro Bay gateway: vessel dwell times spiked sharply versus the four-week average, while container vessel calls dropped from a high mid-week count to just a few per day as Lunar New Year began; consistent with blanking and holding behavior.
At the same time, broader market commentary indicated that post-CNY demand is uncertain and carriers continue trimming capacity via blankings; conditions that tend to increase “lumpiness” (fewer, fuller arrival clusters rather than smooth daily flow).
The implication for terminals and near-port yards is straightforward: you don’t need record monthly TEUs to break a gate. You just need too many trucks trying to transact in the same hours, driven by compressed vessel discharge windows and customers racing free-time clocks.
The yard implication: congestion becomes “operational debt” at the gate
When ports congest, the “structural chasm” between highway and warehouse widens. Practically, that chasm is the yard’s ability to:
ingest container turns fast enough at the gate,
locate and retrieve the right boxes without excessive rehandles,
and keep trucks flowing so rail and warehouse systems don’t starve.
A simple way to explain why yards tip over is Little’s Law: average inventory in a system ≈ throughput × average time in system. When dwell time increases (containers sitting longer), the yard’s effective inventory rises even if throughput stays constant, pushing occupancy toward the point where rehandles, travel distance, and queueing accelerate.
What “manual + spreadsheet” gate ops look like under a wave
Traditional gate operations often depend on a chain of steps that don’t scale linearly in a surge:
verifying appointment / booking / release status,
checking IDs, container numbers, chassis numbers,
assessing damage or seal exceptions,
updating the TOS/YMS and releasing the driver to a row/stack,
resolving exceptions in-lane (which blocks the lane).
You can see why seconds matter by looking at live terminal turn-time reporting. International Transportation Service, LLC. publishes a “turntimes by date” table separating single vs. double transactions and gate queue time; even in routine operations, queue minutes exist, and surge waves magnify them.
The yard side effect is that gates become a metering valve. If the valve constricts, you get:
longer truck turn time,
higher appointment miss rates,
higher yard dwell (because pickups lag),
more rehandles (because “easy to reach” inventory gets pulled first),
and lower effective TEU/day (because equipment burns moves on reshuffles and exceptions).
Terminal Industries angle: AI-native computer vision + YOSTM to stabilize surge days
The reason “AI-native” matters isn’t buzzwords, it’s that surges are fundamentally real-time prioritization problems under uncertainty (arrivals shift, appointments compress, labor is finite). So the winning move is to (1) automate what should be automatic, then (2) use real-time decisioning to allocate scarce yard capacity.
Traditional vs. AI-native yard workflow
Yard capability | Traditional workflow (manual / semi-manual) | AI-native computer-vision YOS workflow |
Gate identification | Human checks + manual entry; frequent in-lane exceptions | Computer vision captures container ID / chassis / plate + auto-validation; human focuses on true exceptions |
Gate throughput scaling | Add clerks and lanes (limited); congestion spikes | Increase “transactions per lane-hour” by shortening per-transaction processing and exception cycle time |
Dwell segmentation | After-the-fact reporting; weak linkage to move priority | Dwell is a live input to prioritization: target aging imports, freespace, or customer-critical SKUs |
Prioritization | FIFO-ish + tribal knowledge; reactive expediting | Rules + AI decisioning: prioritize reefers, high-velocity retail/electronics, rail cutoffs, demurrage cliffs |
Yard inventory accuracy | Manual audits; mismatch risk during churn | Vision-based verification at gates + in-yard confirmation reduces “lost box” and mislocation risk |
Staffing model | Labor scales with volume; surge = overtime + bottlenecks | “Exception desk” model; labor shifts from transcription to resolution and customer service |
KPI management | Lagging KPIs; slow root-cause analysis | Near-real-time dashboards: gate cycle time, queue time, unproductive moves, exception rate |
What automation can realistically improve (ranges, grounded in published examples)
Published public studies and case reports consistently point to material reductions in gate processing time and truck turn time when OCR/RFID and automated gate workflows replace manual steps:
A peer-reviewed container terminal gate automation case study reported automated gate processing times on the order of ~25–60 seconds for key steps (security out-gate ~25s average; security in-gate ~30–40s average; self-service lanes ~60s average), with OCR/LPR accuracies in the high-90% range—showing what “fast path” transactions look like when exceptions are separated from the main lane flow.
A case study of gate automation reported reducing truck turn-around time from 56 minutes to 25 minutes after automating gate processes, illustrating a ~50% class improvement in a real terminal environment.
Another industry report described truck turnaround time reductions of ~30–45% (to roughly 30–45 minutes) after gate automation, along with large staffing shifts from manual gate handling to exception management.
The U.S. Government Accountability Office has reported that ports and terminal operators adopting process automation such as RFID and OCR have expedited gate transactions and lowered truck turnaround times, reducing truck idling and associated emissions.
A conservative synthesis from these public sources suggests that, where the baseline is manual-heavy, automation commonly supports:
Truck turn time reduction: ~30–55% (site-specific; depends on appointment discipline, chassis availability, inspection rates, and yard layout).
Gate processing time: moving routine transactions toward sub-1–2 minute lane time, with exceptions routed off the critical path.
Labor leverage: fewer people “touching” each transaction; more coverage focused on exceptions and customer visibility.
How AI-native computer vision changes the surge playbook
Computer vision systems in logistics yards are increasingly used to monitor movement through gates and across yards, automate alerts, and reduce manual information entry; especially in layouts that were not designed for today’s volumes.
Implementation checklist
This is a pragmatic, “start small, scale fast” checklist; timeline depends on site constraints and integrations:
Core data inputs
Appointment/visit records (truck appointment system or pre-advise feeds)
Container status (availability/release/holds), vessel discharge lists, and free-time clocks
Yard inventory positions + planned work queue (from TOS/YMS)
Exception codes (damage, seal issues, chassis mismatch, inspections)
Hardware + edge stack
Gate cameras positioned for container code OCR + license plate recognition + chassis capture
Lighting and lane sensors sized for all-weather performance
Edge compute for low-latency processing and resilience during network interruptions
Integration points
TOS/YMS for inventory, gate events, work instructions
Gate controls (barriers, kiosks/intercom where needed)
Billing/demurrage workflows (to prevent “data drift” between ops and invoicing)
Visibility layer (dashboards + alerts to dispatch, yard, and customer service)
Operating model
Define “fast path” vs. “exception path” SOPs
Staff an exception desk with authority to resolve holds quickly
Establish KPI cadence (hourly during surges; daily otherwise)
If there’s one CNY takeaway for terminals and yards: your best surge lever is not brute-force labor, it’s reducing friction per transaction and turning dwell into real-time prioritization. The carriers will keep blanking. The waves will keep coming. The yard that wins is the yard that can see, decide, and execute faster than the surge.
Sources:
https://www.china-briefing.com/news/china-2026-public-holiday-schedule/
https://portoflosangeles.org/business/statistics/container-statistics
https://www.visy.fi/apmt-gothenburg-automation-in-phases/
https://www.ajot.com/insights/full/ai-port-of-long-beachs-hacegaba-says-volume-down-11-in-january
https://web.mit.edu/~paulopg/www/OG_Overreaction.pdf
https://theloadstar.com/post-cny-demand-looks-weak-after-transpacific-capacity-shuffle/
https://www.itslb.com/terminal-condition-report/
https://www.mdpi.com/2071-1050/13/11/6291
https://www.gao.gov/assets/870/867325.pdf
https://innovation.maersk.com/innovations/yard-computer-vision
