Civic Data Playground

Population 90

Light cycle 3.5 s

Road budget 60%

Avg speed

–

Congestion

–

Throughput

–

Trips completed

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How it works

Every vehicle is an agent traveling along one axis of the road grid. A single synchronized signal alternates which axis has right of way; the other axis must brake and queue at the next intersection. Each agent recomputes its distance to the upcoming intersection every frame and decelerates smoothly rather than snapping to a stop, which is what produces the wave-like queues you see building and releasing.

v_target = v_base · roadQuality
if axis is red and d_line < d_trigger:
  v = v_target · clamp((d_line − d_stop) / (d_trigger − d_stop), 0, 1)
congestion = stoppedAgents / totalAgents

Population — how many agents share the grid; more agents mean more queuing at every red phase.
Light cycle — seconds each axis holds green; short cycles switch often and keep queues shallow but interrupt flow more; long cycles let one axis run free while the other backs up.
Road budget — a stand-in for pavement quality and lane capacity: it scales every agent's free-flow speed, the way real capital spending raises a corridor's practical throughput.
Traffic lights — toggle the signal off to see free-flow traffic with no stopping, a useful baseline for comparing against the governed grid.

This mirrors what a civic data playground does with real municipal datasets: turn static open-data tables — signal timing plans, road inventories, census counts — into a model residents and planners can push on to see how policy choices ripple through daily commutes.

Moving vehicle Stopped / queued Green approach Red approach

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