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Traffic

Smart Bridge

Using a 3DGS full-bridge digital twin and VLM stream monitoring, deck, footpath and cycle-lane cameras on long sea- and river-crossing bridges are upgraded into a 24x7 proactive bridge-safety system. It recognises congestion and its cause, crashes with severity pre-grading, debris and animal intrusion, roadwork extent, disaster events (vehicle fire, deck flooding, river surge) and footpath and cycle-lane anomalies. Events are output as structured fields and located in the twin, letting operators jump from full-bridge view to any camera in one click.

VLM output

An aerial camera slowly orbits a large cable-stayed bridge crossing a bay at sunset, with a coastal city skyline in the background. Light traffic — a few cars and a white box truck — moves freely across the bridge deck in both directions throughout the clip.

  • Bridge VisibleYes
  • Bridge TypeCable-Stayed
  • Deck Traffic FlowFree-Flowing
  • Traffic DensityLight
  • Vehicle Count5-8
  • Congestion DetectedNo
  • Structural Anomaly DetectedNo
  • Pedestrian On DeckNo
  • Lighting ConditionSunset

Customer challenges

1

Sea wind, fog and heavy rain degrade generic vision models

2

Mixed road users: motorcycles on footpaths and pedestrians in lanes are missed by manual watching

3

Debris, animal intrusion, deck flooding and vehicle fire in view need first-minute recognition

4

Crash severity is judged on arrival, not from the footage

5

PTZ movement distorts traffic counts; feeds cannot be mapped to a position on the bridge

Customer benefits

All-weather live awareness of the deck; crashes found and pre-graded immediately, speeding resource dispatch

Abnormal congestion known 10 minutes earlier; incident mitigation time cut 20%

Instant alerts for debris, animals and motorcycles on footpaths prevent secondary crashes

Second-level warning for flooding, fire and river surge protects the golden response window

The whole-bridge twin maps every feed to its physical location for intuitive command

Structured output plugs into existing incident and CMS workflows; scan to six live functions in about 4 weeks

Key features

Traffic Congestion & Cause Analysis

Detects traffic congestion on the bridge and analyzes its cause.

Accident Analysis (Severity Pre-grading)

Analyzes accidents and pre-grades their severity.

Road Hazard Detection (Debris & Animal Intrusion)

Detects road hazards including debris and animal intrusion.

Road Construction Detection (Closure Extent)

Detects road construction and the extent of closures.

Disaster Early Warning (Vehicle Fire, Deck Flooding, River Surge)

Provides early warning for vehicle fire, deck flooding, and river surge.

Sidewalk & Bike Lane Anomaly Detection (Railing Climbing, Motorcycle Intrusion, Lane Crossing)

Detects sidewalk and bike lane anomalies such as railing climbing, motorcycle intrusion, and lane crossing.

Before & after

Before Abnormal congestion noticed when queues form After — with Linker AI Nexa Awareness 10 minutes earlier
Before Incident mitigation by manual dispatch After — with Linker AI Nexa Mitigation time -20%
Before Footpath and lane violations unrecorded After — with Linker AI Nexa 100% of in-view violations logged (motorcycles on footpaths, lane debris, pedestrians in lanes)
Before Crash severity assessed on arrival After — with Linker AI Nexa 100% pre-graded within a minute of detection
Before Traffic counted manually or by spot sensors After — with Linker AI Nexa Continuous four-category counting on 100% of streams
Before A new monitoring capability takes months After — with Linker AI Nexa About 4 weeks from scan to live, deployment lead time -75%