A Data-Driven Approach to Transportation Equity: Acquiring a dataset of heterogeneous pedestrian crossing profiles

The project’s goal is to collect a dataset of heterogeneous pedestrian crossing profiles in New York City. The research team intends to do so by deploying high-resolution, high-frame-rate audiovisual sensors at intersections in the city. Unlike past attempts, the data collection process will focus on optimizing the resulting dataset to represent a diverse population of pedestrians at different ability levels. Furthermore, the research team will collect data representing different environmental conditions (sunny and rainy days) and various traffic infrastructures (presence or lack of curb ramps, presence or lack of clearly-painted crosswalks, and timing of traffic lights, among other factors described in this project). By exploring these differences, the research team hopes to accomplish a more accurate profiling of users (motorized and not) of all mobility levels at these intersections and allow comparison of crossing behavior across underrepresented groups, such as people with limited walking ability. This profiling will then serve for data-driven insights to improve safety, flow, and accessibility in these urban environments.

Language

  • English

Project

Subject/Index Terms

Filing Info

  • Accession Number: 01937755
  • 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 5:16PM