A Pilot Experimental Project for Predicting Pedestrian Flows Using Computer Vision and Deep Learning

Walking for transportation, health, and pleasure is an essential part of people’s lives in most cities. Knowing where people linger, the destinations that attract them, and how those places are accessed could assist in optimizing business locations and providing better security. In addition, predicting and sharing congestion times and locations (perhaps in real-time as in Waze for cars) could also provide useful information to travelers who can then choose appropriate travel routes and improve travel efficiency. Yet, we know far less about the spatial and temporal variations in pedestrian volumes than we know about vehicular movement. While pedestrian route choice has been an active area of research, few studies have attempted to predict pedestrian flows from unbiased pedestrian count data. Pedestrian route choice models assume that people choose their walking routes based on their perceived path attributes. Statistical path choice models identify people’s behavior related to route attributes on the selected path. These models hypothesize that the fundamental utility attribute is path length or travel time, which pedestrians generally minimize. These models also consider that people are willing to deviate to longer routes if the preferred path is comparatively safe, comfortable, and aesthetically pleasing. Yet, these models are inefficient for pedestrian traffic planning since they require prohibitive amounts of information about individual walkers. In this research, the research team develops a graph convolutional network model (GCN) based only on pedestrian counts at various intersections and segments to predict pedestrian traffic flows.

• Record URL:
  https://tbd.ctr.utexas.edu/research-product/a-pilot-experimental-project-for-predicting-pedestrian-flows-using-computer-vision-and-deep-learning/

Language

• English

Project

• Status: Active
• Funding: $104866
• Contract Numbers:

  69A3552344815

  69A3552348320

• 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:

  Center for Understanding Future of Travel Behavior and Demand

  University of Texas
  Austin, TX  United States 
• Project Managers:

  Bhat, Chandra

• Performing Organizations:

  Georgia Institute of Technology, Atlanta

  790 Atlantic Drive
  Atlanta, GA  United States  30332-0355
• Principal Investigators:

  Guhathakurta, Subhrajit

• Start Date: 20240101
• Expected Completion Date: 20240531
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers

Subject/Index Terms

• TRT Terms: Computer vision; Machine learning; Pedestrian counts; Pedestrian flow; Route choice
• Subject Areas: Pedestrians and Bicyclists; Planning and Forecasting; Safety and Human Factors;

Filing Info

• Accession Number: 01917644
• Record Type: Research project
• Source Agency: Data-Supported Transportation Operations and Planning Center
• Contract Numbers: 69A3552344815, 69A3552348320
• Files: UTC, RIP
• Created Date: May 6 2024 4:17PM