damnlines
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Know before you go.

Contact
ciao [at] damnlines [dot] com
terminal
damnlines.com
$ whoami
damnlines.com
$ cat README.md
damnlines.com shows you real-time line lengths across New York City.
A camera watches the queue, AI counts the people, and you get a live
count before you decide to walk over.
$ cat APPROACH.md
The system runs in three stages:
detect → track → estimate
Computer vision identifies and tracks people in the queue.
Queue theory turns those observations into wait estimates.
Results stream to you in real time.
The Engineering

Three ideas power every estimate on damnlines: a 60-year-old theorem, a service-rate observer, and a real-time people counter.

littles-law
damnlines.com
$ cat littles-law.md
In 1961, John Little proved something intuitive but powerful: the average number of people in a stable queue equals the rate they arrive multiplied by how long each person waits.
L = λ × W
L = average number of people in the queue
λ (lambda) = arrival rate — people joining per minute
W = average time each person spends waiting
The remarkable thing: this holds regardless of arrival patterns, service variability, or how the queue is structured. It's a universal law of queuing systems — and the foundation of everything we calculate.
Example: If 2 people join the line per minute and each waits 5 minutes, the line will average 10 people. Double the arrival rate and it jumps to 20.
Queue length by wait time & arrival rate
── λ = 1/min── λ = 2/min── λ = 4/min
wait-time
damnlines.com
$ explain --wait-estimate
To estimate how long you'd wait if you joined the line right now, we rearrange Little's Law:
West = L / μ
West = your estimated wait time
L = people currently in line
μ (mu) = service rate — people leaving the queue per minute
We calculate μ by observing how many people exit the queue over a rolling time window. The more observations we collect, the more accurate the rate becomes.
When there isn't enough historical data to compute a reliable service rate, we fall back to a conservative per-person estimate. It's better to overestimate your wait than to send you somewhere with a false sense of speed.
Example: 12 people in line, service rate of 4/min → estimated wait ≈ 3 minutes. Same 12-person line at 2/min → 6 minutes.
Estimated wait by queue length & service rate
── Slow (2/min)── Moderate (4/min)── Fast (8/min)
people-count
damnlines.com
$ explain --people-count
Every estimate starts with an accurate count. Here's how we derive it:
1. Detection
Each video frame is analyzed to locate individuals using computer vision. The model outputs a bounding box around every detected person.
2. Tracking
Detections are linked across frames to maintain consistent identities. Person #42 in frame 1 is matched to the same individual in frame 30 — even if they've moved significantly.
3. Zone counting
A queue zone is defined in the camera's field of view. When a tracked person crosses into the zone, the count increments. When they leave, it decrements.
4. Dwell time
We measure how long each person remains inside the zone. This per-person timing feeds directly into service rate and wait time calculations.
current_count = Σ entries − Σ exits
Sample: estimated queue length over a day

Every few seconds the system captures a frame, detects people, updates the count, recalculates the service rate, and pushes a fresh estimate to your screen. The entire pipeline — from camera to prediction — runs continuously throughout operating hours.

fun
damnlines.com
$ cowsay "No one likes waiting in a damn line."
________________________________________
< No one likes waiting in a damn line. >
----------------------------------------
\ ^__^
\ (oo)\_______
(__)\ )\/\
||----w |
|| ||
$ fortune
Time you enjoy wasting is not wasted time.
But time spent in a line you could have skipped? That's just dumb.
About | damnlines