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

Term Start:

January 1, 2024

Term End:

May 31, 2024




Computer Vision, Deep Learning, Pedestrians

Thrust Area(s):

Data Modeling and Analytic Tools

University Lead:

Georgia Institute of Technology


Subhrajit Guhathakurta

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, we develop a graph convolutional network model (GCN) based only on pedestrian counts at various intersections and segments to predict pedestrian traffic flows.

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