Identify Features Impacting Perceptions of Cycling in NYC – by AI Chatbot

 

Student: Feiyang Ren, Master of Urban Science, at the Center of Urban Science + Progress at the Tandon School of Engineering.

Advisors: Dr. Zhaoxi Zhang, Faculty Fellow in the Center of Urban Science + Progress at the Tandon School of Engineering and Dr. Tamir Mendel, Postdoctoral Researcher in the Department of Technology of Management and Innovation, NYU Tandon School of Engineering, New York University.

 

Project Abstract: Bicycle safety is a critical component of urban bikeability, necessitating focused attention due to its impact on public health and transportation efficiency. New York City, characterized by a high population of cyclists and a correspondingly high incidence of bicycle-related accidents, highlights the urgency of addressing cycling safety issues. This study aims to identify and analyze the key features that contribute to a safer cycling environment in New York City. Utilizing an artificial intelligence (AI) chatbot, we collected feedback from citizens through interactions with a human-like digital agent. The textual data obtained from these interactions were analyzed using advanced Natural Language Processing (NLP) techniques to extract relevant insights. Our findings indicate that factors such as bike lane width, road surface quality, and the presence of dedicated cycling infrastructure significantly influence perceived cycling safety. This research provides valuable insights for urban planners and policymakers aiming to enhance cycling safety and promote sustainable transportation in urban areas.

Pictures from the demo survey: 

 
If you are interested in this project, please write to us for the link to the demo survey.
 
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