Estimating Pedestrian Volumes at Intersections using Artificial Intelligence: A ChatGPT Vision Approach

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Pedestrian volume at intersections is a key input to transportation processes and is vital for developing pedestrian-centric interventions. Though technological solutions to provide continuous monitoring of intersections and field volume counts do exist, most jurisdictions do not have this technology widely deployed and therefore still require a means of estimating volumes. Conventional estimation methods, such as expansion factor methods or the development of direct-demand (DD) models, are alternatives typically employed by practitioners, but they still require known pedestrian volume data for numerous sites, which are often not readily available for jurisdictions. This work explores the use of ChatGPT-4 Vision (GPT-4V) as a tool to assist jurisdictions in estimating pedestrian volume within intersections. The initial assessment of GPT-4V demonstrated its capability in interpreting satellite images, particularly in ranking sites according to pedestrian activity levels. Subsequently, a method was implemented to rank 48 sites based on pedestrian activity using GPT-4V and satellite images. A linear correlation of 0.73 was achieved between the GPT-4V ranking and the true ranking determined from observed field data. Following that, a method was proposed to combine the GPT-4V site rankings and field volumes collected at selected key intersections to estimate the pedestrian volume at all the sites ranked using GPT-4V. Its performance rivaled (and sometimes surpassed) that of existing conventional methods like DD models, all without the need for complex statistical models or extensive datasets. This simplicity makes the GPT-4V method promising for cost-effective pedestrian exposure estimation. This work also investigates inconsistencies and biases in GPT-4V’s responses.