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US Import Bookings Q1 2026: Navigating Uncertainty in a Front-Loaded Market

  • 7 hours ago
  • 2 min read

09 April 2026 VIZION 


Q1 2026 opened with a striking surge of pre-tariff front-loading, then softened sharply as shippers absorbed elevated import levels and uncertainty clouded the outlook. This report breaks down weekly booking trends, port performance, commodity flows, and carrier share shifts — giving you the data to make faster, smarter decisions.


Source: Vizion
Source: Vizion

Weekly booking trends: the front-load surge and the pullback


The story of Q1 2026 is two acts. Weeks 1–4 saw robust volumes as importers rushed to beat anticipated tariff increases, with Week 3 topping 368K TEU — slightly above the same period in 2025. From Week 5 onward, bookings softened considerably, with Week 8 collapsing to just 154,730 TEU — the sharpest single-week dip in the dataset due to Chinese New Yea. By Week 13, volumes were recovering toward 352K TEU, signaling a floor may have been found.


Source: Vizion
Source: Vizion

Week 8 2026 (154,730 TEU) was 56.9% below the same week in 2025 (359,491 TEU) — a dramatic pullback likely driven by front-loading fatigue and the Chinese New Year holiday convergence. It stands as the weakest comparable week across all three years in this dataset.


Port performance: winners and losers in Q1 2026


The port picture is nuanced. Houston surged +17.1% year-over-year, emerging as the standout growth story of Q1. LAX held its top position despite a modest decline. Meanwhile, several major East and Gulf Coast ports saw meaningful volume erosion — particularly Savannah (−7.1%), Long Beach (−12.4%), and Seattle (−22.2%), which posted the steepest relative decline among major ports.


Source: Vizion
Source: Vizion

Houston's 17.1% YoY gain to 464K TEU suggests significant trade lane diversion — possibly from West Coast ports facing labor uncertainty and from importers diversifying port risk. Tianjin-linked flows into USTIW dropped 44.6%, the largest proportional decline of any port in the dataset.








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