Estimating Daytime Population for Data-Driven Urban Planning

The COVID-19 pandemic and the subsequent increase in remote work have changed how people move around cities and where people spend their time during the day, shifting the daytime population to different streets and neighborhoods. Accurately estimating this daytime population is essential for data-informed urban planning, as it impacts infrastructure, transportation service needs, and land use decisions. Partnering with the New York City Department of City Planning, (NYC DCP), this project aims to leverage Artificial Intelligence (AI) and computer vision to analyze video data from over 900 traffic cameras across NYC to estimate daytime population. The primary objectives include classifying cameras by street hierarchy, extracting vehicle and vulnerable road user (e.g., pedestrian) information (e.g., density) on both street level and community level, and using spatial analysis to explore the collective impact of various factors on traffic congestion and urban dynamics. This will provide timely insights for improving urban infrastructure, land use planning, and decision-making, enhancing accessibility, and reducing traffic congestion.

• Record URL:
  https://c2smarter.engineering.nyu.edu/estimating-daytime-population-for-datadriven-urban-planning/

Language

• English

Project

• Status: Active
• Funding: $135000
• Contract Numbers:

  69A3551747124

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)

  New York University
  Brooklyn, New York  United States  11201
• Project Managers:

  Pohl, Lizzie

  Chase, Holly

• Performing Organizations:

  Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)

  New York University
  Brooklyn, New York  United States  11201

  New York University Tandon School of Engineering

  6 Metrotech Center
  Brooklyn, NY  United States  11201
• Principal Investigators:

  Gao, Jingqin

  Zuo, Fan

• Start Date: 20240901
• Expected Completion Date: 20251130
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Artificial intelligence; Cameras; City planning; Computer vision; Population density; Travel patterns
• Geographic Terms: New York City
• Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General);

Filing Info

• Accession Number: 01937756
• Record Type: Research project
• Source Agency: Connected Communities for Smart Mobility Towards Accessible and Resilient Transportation for Equitably Reducing Congestion (C2SMARTER)
• Contract Numbers: 69A3551747124
• Files: UTC, RIP
• Created Date: Nov 21 2024 5:20PM