LCB ONE · Capacity & congestion simulation

Where does smoothing stop working?

Push the sliders. Scheduling fixes clustering — it cannot invent lanes that don't exist. This shows exactly where that line is.

Inputsillustrative defaults — tune to your real numbers
1.0× = today's volume, 3.0× = triple
trucks the approach road can hold before it visibly blocks traffic
planning estimate — no baseline data yet; measure it in the pilot
share of empty-leaving trucks that pick up an import instead
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Daily capacity check

This is the same number in all three scenarios below — no schedule changes it.

What the queue looks like, all day

Today — flat hourly

everyone books for :00, so demand spikes each hour
Demand vs. capacity
Queue over 24h
Peak queue—
Est. peak wait—
Drains by midnight?—

LCB ONE — dynamic 15-min slots

same trucks, spread evenly within each hour
Demand vs. capacity
Queue over 24h
Peak queue—
Est. peak wait—
Drains by midnight?—

LCB ONE + buffer yard

overflow beyond the road shoulder holds off-road instead
Demand vs. capacity
Queue over 24h — road (solid) + buffer (light)
Peak road queue—
Peak buffer use—
Buffer overflowed?—
Return loads — the one lever that reduces demand itselfapplied on top of the LCB ONE scenario
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Return loads matched / day
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Fewer road trips / day (2 per match)
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Trucks leaving empty
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Daily gate utilization
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Peak queue, LCB ONE
Queue over 24h — LCB ONE (light) vs. LCB ONE + return loads (solid)

Scheduling spreads the same trucks across the day. A matched return load removes a truck visit altogether: the import is collected by a truck already inside the port instead of a second truck driving in empty. That is why it is the only input here that can pull an over-capacity day back under the line without adding lanes.

Methodology & limits

A deterministic fluid-queue model at 15-minute resolution: trucks/day follow a modeled daily traffic shape (low overnight, two daytime peaks); within each hour they either cluster at :00 ("Today") or spread evenly ("LCB ONE"). Capacity = lanes × throughput/lane, held constant through the day. Return loads: matches/day = trucks/day × empty-leaving share × match rate; each match removes one gate visit and two road trips, and the gate service time of a combined drop-and-pickup is assumed equal to a single visit. This is an illustrative planning model, built to make the concept and the breaking point adjustable in front of stakeholders — it is not a calibrated traffic study. Before committing budget to lane-widening or gate counts, validate against a real microsimulation (e.g. SUMO or VISSIM) using PAT's actual road geometry, measured gate service times, and observed arrival data.