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

2026-02-03

AI Summary

This study evaluates the real-world impact of New York City’s congestion pricing policy on traffic flow. Leveraging video data from over 900 traffic cameras across Manhattan and the broader city during November 2024 to January 2026, we developed a deep learning–based computer vision pipeline that enables, for the first time, real-time, fine-grained monitoring of traffic density at the city scale under congestion pricing. Through large-scale video processing and spatiotemporal modeling, the project successfully established a pre-implementation traffic baseline and identified a systematic decline in vehicle density within the congestion zone, providing high-resolution empirical evidence for assessing the policy’s effectiveness.

Technology Category

• Computer Vision: Large Vision Models
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Application Domains: Transportation

Application Category

• Security and Privacy: Large-scale security measurements
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• congestion pricing
• traffic analysis
• computer vision
• urban mobility
• traffic monitoring

Innovation

• computer vision
• congestion pricing
• traffic monitoring
• automated analysis
• urban mobility