Analyzing the drivers of pedestrian activity at high spatial resolution

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Pedestrian activity and mobility are key factors for transportation modeling, public health, and city planning. Traditionally, city agencies conduct manual counts by location and time as a proxy of pedestrian volume in surrounding area. With increasing data sources in the urban domain, particularly open city data, social media, wireless networks, new methods, and data analytics approaches are needed to extract deeper insights on pedestrian activity, volume, and mobility. This paper utilizes a dataset of pedestrian counts conducted by the New York City Department of Transportation (DOT) from 2007 to 2015, and integrates these data with geocoded public transit, land use, building, and streetscape information. Based on 100 locations defined by DOT, the study further explores correlations between pedestrian volume and the surrounding localized urban context. Through data mining, visualization, and statistical modeling, this study aims to (1) identify baseline and key indicators of pedestrian volume, and explore potential integration of new data sources with current methodologies; (2) use integrated approaches to exam how pedestrian volume changes by location and time; and (3) use regression models to predict pedestrian volume across other intersections in NYC. The results provide key indicators for pedestrian volume estimation without conducting manual counts. Overall, this study demonstrates a novel approach in utilizing new data sources and generating deeper analytical insights to understand the determinants and predictors of pedestrian activity.

Original languageEnglish (US)
Title of host publicationInternational Conference on Sustainable Infrastructure 2017
Subtitle of host publicationMethodology - Proceedings of the International Conference on Sustainable Infrastructure 2017
EditorsLucio Soibelman, Feniosky Pena-Mora
PublisherAmerican Society of Civil Engineers (ASCE)
Pages303-314
Number of pages12
ISBN (Electronic)9780784481196
DOIs
StatePublished - 2017
Event2017 International Conference on Sustainable Infrastructure: Methodology, ICSI 2017 - New York, United States
Duration: Oct 26 2017Oct 28 2017

Publication series

NameInternational Conference on Sustainable Infrastructure 2017: Methodology - Proceedings of the International Conference on Sustainable Infrastructure 2017

Other

Other2017 International Conference on Sustainable Infrastructure: Methodology, ICSI 2017
CountryUnited States
CityNew York
Period10/26/1710/28/17

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Management of Technology and Innovation
  • Safety, Risk, Reliability and Quality
  • Renewable Energy, Sustainability and the Environment

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  • Cite this

    Lai, Y., & Kontokosta, C. (2017). Analyzing the drivers of pedestrian activity at high spatial resolution. In L. Soibelman, & F. Pena-Mora (Eds.), International Conference on Sustainable Infrastructure 2017: Methodology - Proceedings of the International Conference on Sustainable Infrastructure 2017 (pp. 303-314). (International Conference on Sustainable Infrastructure 2017: Methodology - Proceedings of the International Conference on Sustainable Infrastructure 2017). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784481196.027