Seattle Traffic Explorer
What this proves
AI can turn messy public traffic feeds into a readable explanation layer for a real commute bottleneck, not just a pile of maps and CSVs.
A standalone corridor explorer built from WSDOT and OSM data to understand congestion around the Seattle north corridor, with station-level flow, historical patterns, cameras, and route context.
This build is part of the flagship proof collection on ShipWithTez: practical examples that make a new capability feel obvious through a real workflow, real data, or a live interactive surface.
What This Proves
Public infrastructure data is usually technically available but practically unusable. The hard part is not finding an API. The hard part is turning multiple feeds, station IDs, and corridor geometry into something a normal person can actually read.
This build proves that workflow is now fast:
- start with a real question instead of a generic dashboard brief
- use AI to map the data surface quickly
- collapse the useful pieces into one interactive explanation layer
The point is not "Seattle traffic" by itself. The point is that any city, route, or operations feed can now become a navigable proof asset much faster than before.
What I Built
Seattle Traffic Explorer adapts a TezEx corridor experiment into a public proof page. The live explorer combines:
- WSDOT freeway flow and camera coverage
- historical 5-minute corridor data
- route geometry from OpenStreetMap
- a self-contained exported interface that is easy to host and share
The current public version focuses on the north Seattle corridor around Lynnwood, Alderwood, and nearby connectors because that was the original question: why does this commute bottleneck feel bad all the time?
Data Note
The live proof is a standalone exported snapshot, not a continuously running traffic product. The current public refresh packages a continuous free/public corridor history from January 27, 2026 through March 19, 2026 into one shareable artifact.
That tradeoff is intentional. For ShipWithTez, the proof is the workflow: how quickly a messy public dataset can become a usable surface.
What You Can Steal From This
- Lead with a human question: "Why is this interchange always bad?" is a much better starting point than "build a traffic dashboard."
- Use static exports for proof assets: you do not need a full production stack to show the capability clearly.
- Generalize after the first win: once one corridor works, the pattern can be reused for transit, utilities, logistics, campus ops, or city services.
Get the next build and workflow breakdown.