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AI-Assisted Traffic Signal Control for Midtown Manhattan

What it is for

Mobility
  • Improves traffic flow for all modes of transportation through adaptive signal timing in midtown Manhattan, covering 1st to 9th Avenues and 34th to 57th Streets.

NYC Algorithmic Tools Compliance Report

Planning & Decision-making
  • Classifies congestion levels (light, moderate, moderate-heavy, heavy) across the midtown core to support real-time traffic management decisions by NYC DOT staff.

NYC Algorithmic Tools Compliance Report

Run by
Department of Transportation (DOT)
Where
No fixed location
Kept
Not stated by the NYC Office of Technology and Innovation.
Shared with
Accountable organization, Vendor
Your copy
You cannot see the data it holds about you. What you can do

What it is for

MobilityPlanning & Decision-making

This system measures traffic congestion in midtown Manhattan using sensor data, classifies congestion levels, and recommends optimal traffic signal timing to reduce gridlock on major avenues. It operates continuously and affects all road users — drivers, cyclists, and pedestrians — in the area between 34th and 57th Streets. No personal identifying information is collected; the system works entirely from vehicle travel-time measurements. Signal timing recommendations are monitored by city staff before being applied.

What it collects and what happens to it

Data taken in

About a measurement
Anonymized data
  • Motor vehicle travel times captured by Radio Frequency Identification (RFID) sensors within and beyond the midtown zone. No identifying information about individual vehicles or drivers is collected or disclosed.

NYC Algorithmic Tools Compliance Report

About a place
Anonymized data
  • Geographic zone coverage data for the midtown Manhattan core (1st to 9th Avenues, 34th to 57th Streets), used to define the spatial extent of congestion classification.

NYC Algorithmic Tools Compliance Report

Processing

Classification & Prediction
  • Assigns one of four congestion-level labels (light, moderate, moderate-heavy, heavy) to the midtown traffic zone based on real-time RFID travel-time inputs, then selects the optimal signal timing plan for each avenue corridor.

NYC Algorithmic Tools Compliance Report

What it does

Deciding (Analytical AI)
Human decides
  • Classifies congestion into four tiers (light, moderate, moderate-heavy, heavy) from RFID sensor travel-time data, and scores avenue signal plans by optimality — a human operator monitors recommendations before applying them.

NYC Algorithmic Tools Compliance Report

Outputs

A recommendation or prediction
Anonymized data
  • Recommended traffic signal timing plan for avenues in the midtown core, produced continuously and surfaced to DOT Traffic Operations staff for review and implementation.

NYC Algorithmic Tools Compliance Report

About a place
Anonymized data
  • Congestion classification (light, moderate, moderate-heavy, heavy) for the midtown Manhattan geographic zone, used to select signal plans along specific avenue corridors.

NYC Algorithmic Tools Compliance Report

Run by

Department of Transportation (DOT)
  • The NYC Department of Transportation, Traffic Operations division, deploys and operates the Midtown in Motion / ACDSS system to manage traffic signal timing in midtown Manhattan.

NYC Algorithmic Tools Compliance Report

Built by

KLD Engineering, P.C.
  • KLD Engineering, P.C. is the developer of the Adaptive Control Decision Support System (ACDSS) and currently holds the maintenance contract for the system.

NYC Algorithmic Tools Compliance Report

Kept for

Not stated by the NYC Office of Technology and Innovation.

Shared with

Available to the accountable organization
  • Congestion classifications and signal plan recommendations are available to NYC Department of Transportation Traffic Operations staff for monitoring and decision-making.

NYC Algorithmic Tools Compliance Report

Available to vendor
  • KLD Engineering, P.C. has access to system data as part of its maintenance contract responsibilities.

NYC Algorithmic Tools Compliance Report

Not available to me
  • No individual-level data is collected, so there is no personal data available for individual access requests. The system processes only anonymized aggregate travel-time measurements.

NYC Algorithmic Tools Compliance Report

Stored

Not stated by the NYC Office of Technology and Innovation.

How to read the colours

Can it identify you?

Anonymized data
Data about people with the link to who is broken. Stripped of identifiers, blurred, aggregated, or noised so this system can’t reasonably tie a record back to an individual.
Pseudonymous data
Each person’s data is tied to a token (hash, ID, template) that lets this system recognise the same person across events, but the token itself doesn’t reveal a name. Reidentification is possible with extra information.
Identifiable data
The data either contains a direct identifier (name, address, account name, recognisable face or voice, plate number) or carries a token this system uses to look up legal identity during processing.

Who completes the loop?

Human decides
This mode suggests; a person decides what to do next. The AI is always advisory — a human is in the loop on every decision. Example: a triage tool ranks cases for a clinician who chooses which to see first.
Human executes
This mode decides; a person carries out the result. Example: an optimizer plans the day’s trash-collection routes, and drivers run them.
Autonomous
This mode decides and acts on its own. No person reviews each decision or carries out the resulting action.

Definitions from the DTPR standard. Amber is about your data, violet about who decides. The fuller the shape and the deeper the colour, the more identifying the data or the less a person is involved.

What you can do

Ask about this system

Questions go to the NYC Office of Technology and Innovation, not the vendor.

Your rights

  • Right to Algorithmic TransparencyMembers of the public can learn about this system through the NYC Algorithmic Tools Compliance Report, publicly available at data.cityofnewyork.us. The register entry describes the system's purpose, data inputs, computation type, and autonomy level.

Risks and safeguards

  • Civil liberties harmRFID sensors that capture vehicle travel times could in principle be used for vehicle tracking if linked to plate or transponder data.Safeguard: The register states no identifying information is collected or disclosed. The system is described as geographic-space and population-level, not individual-tracking. Human monitoring of all recommendations before application provides an additional check.