Measure traffic speeds. Demand safer streets.

Open-source speed monitoring for neighbourhood streets.

Collect the evidence yourself. velocity.report turns a Raspberry Pi and a radar sensor into a local speed monitor. Download the Pi image, aim it at your street, and generate a PDF you can take to City Hall: no cameras, no licence plates, no cloud accounts.

Before and after: Measure before. Measure after. Then you can see whether the changes worked, which is better than arguing from memory.
Privacy by design: No cameras, no faces, no licence plates. Measure traffic, not identity.
Open source: Read the code, run it on your own Pi, and keep the data on hardware you control.
Raspberry Pi + radar sensor Local-only data Open source Audit-friendly PDFs
GEN 01 · RADAR
IN THE WILD
Ready now. Measuring streets today.
Radar speed monitoring on Raspberry Pi. It already produces PDF reports, percentile breakdowns, and hourly patterns for real residential streets.
STATUS STABLE RELEASE v0.5.0 HARDWARE OmniPreSense RADAR
GEN 02 · LIDAR
RESEARCH PREVIEW
Open research, calibrating.
LiDAR captures the whole scene, classifies road users, and tracks how they move around each other. The maths are open. The questions are open too. It is not the kind of system you install and forget, yet.
STATUS EXPERIMENTAL PIPELINE CALIBRATING HARDWARE Hesai LiDAR
How it works

Three pieces. One PDF.

01 · RECORD

Point it at the street.

Mount it on a window or fence and the radar measures passing vehicle speeds. No images. No identifying data.

54 mph DOPPLER
02 · STORE

A Raspberry Pi keeps the data.

Everything runs locally on a Pi. The SQLite database stays on the device. Re-aim it, let it run for days or weeks, and keep the data on hardware you control.

SOC RPi SQLITE
03 · REPORT

Generate the PDF people can use.

One click generates a council-ready PDF with speed distributions, percentiles, hourly patterns, and site information. It leaves less room for hand-waving and more room for the actual problem.

85th PDF
Downloads

Pick your platform.

Raspberry Pi image
Release notes →
v0.5.1-pre18
sha256:596eec3f9a94824ab2f211ae80240eacbc9d8c79bf302820fe415b2b878fdd14
Raspberry Pi Imager custom repo
Install Raspberry Pi Imager, then run this in a terminal. The --repo flag should work everywhere. Only the path to the binary changes.
cd "/Applications/Raspberry Pi Imager.app/Contents/MacOS/" && ./rpi-imager --disable-telemetry --repo https://velocity.report/rpi.json
Server downloads
Release notes →
v0.5.0
sha256:1e96dc4ef660f35dcbde98d3b98a7f645e3419d95f3036b029080621321faf5e
v0.5.0
sha256:b263e8a108c58e71f608cb2727fde2b2fd5c3f40e38353b9ef8c2d59d1b094d4
Research

LiDAR pipeline: full-scene understanding.

RESEARCH PREVIEW OPEN PIPELINE

See the whole street, classify road users, and measure how they move around each other. The code is real, the maths are published, and the results are still being calibrated.

The radar setup gives you speed data and a PDF. LiDAR goes further: it captures the full scene, identifies road-user types, and tracks motion through it. What it does not give you yet is a finished set of street-safety metrics you can accept on sight. That part is still under active work.

One part of that future is the Traffic Description Language: a way to ask plain-English questions of the fused transit data. Questions like how closely drivers pass work crews, how much space cars leave cyclists in a bike lane, or how many cars come to a complete stop at an intersection when school gets out. In other words, the questions people actually ask once the speed chart has done its job.

A radar logs velocity. LiDAR paints the rest of the scene.

LiDAR visualiser preview

Get the evidence, or help build the next part

For residents & advocates

Measure your street.

You do not need to be a developer. Flash the Pi image, aim it out of a window, leave it running for a week, and take numbers to the next meeting instead of a story that someone can wave away.

  • Flash the Raspberry Pi image once; after setup, the main workflow lives in the web dashboard
  • Council-ready PDF with speed distributions, hourly patterns, and percentiles
  • Privacy by design: radar only, no faces, no plates, and no cloud account
  • Discord help for mounting, aiming, setup snags, and making the case
For engineers & researchers

Help us see the street.

The radar pipeline is the most mature part of the project. The LiDAR pipeline is open and still calibrating. Geometry, tracking, and classification still have real unanswered questions, which is another way of saying there is useful work to do.

  • Open-source pipeline, published maths, and a public list of open questions
  • LiDAR research preview: point clouds, tracking, classification
  • Discord for fast design triage; GitHub issues and PRs for decisions that need a paper trail
  • Apache-2.0 licensed, documented, and backed by calibration data
FAQ

Common questions

Quick answers to the questions people usually ask before they download.

What is this?

An open-source traffic speed monitor. Plug a radar sensor into a Raspberry Pi, let it measure how fast vehicles pass, and it produces a PDF with speed distributions, hourly patterns, and percentile breakdowns. No cloud, no cameras, and no habit of collecting things it does not need.

Who is this for?

People trying to show that traffic on their street is a safety problem, and to do it with numbers rather than a weary look at the road. Residents, parent groups, neighbourhood campaigns, and anyone who has been told to come back with evidence.

What does it measure?

Vehicle speeds. A radar sensor records how fast vehicles pass and stores the results on a Raspberry Pi. No faces, no licence plates, and no images of any kind.

Where does the data go?

Nowhere special. It stays on your device in a local database. There is no cloud account, no silent upload, and no side business in learning things about your street. Data leaves only when you decide to share the finished PDF.

What about LiDAR?

LiDAR is still research-stage. The pipeline is open, the maths are published, and the current release includes a macOS visualiser for engineers and curious users. Today, the dependable PDF path is radar. LiDAR is not the install-it-and-forget-it path yet.

How do I get started?

Follow the setup guide. You need a Raspberry Pi and an OmniPreSense radar sensor. By the end, you should have a working speed monitor and a PDF report fit for a council meeting.

Advocacy

Making the case at City Hall.

"The goal is fewer crashes, fewer injuries, and zero fatalities.
If the speeds don't drop, the work can't stop."
David Dolphin · SFMTA Board Meeting · Jan 2026