A Vision-Based Analysis of Congestion Pricing in New York City

TL;DR

This study uses computer vision to analyze traffic camera data, assessing the effects of New York City's congestion pricing program on vehicle density and traffic patterns over a year.

Contribution

It introduces an automated computer vision pipeline to evaluate congestion pricing impacts using extensive traffic camera footage.

Findings

Identified significant reductions in vehicle density post-implementation

Established baseline traffic patterns before congestion pricing

Detected systematic changes in traffic flow across Manhattan

Abstract

We examine the impact of New York City's congestion pricing program through automated analysis of traffic camera data. Our computer vision pipeline processes footage from over 900 cameras distributed throughout Manhattan and New York, comparing traffic patterns from November 2024 through the program's implementation in January 2025 until January 2026. We establish baseline traffic patterns and identify systematic changes in vehicle density across the monitored region.