Data-Driven Agent-Based Model of Intra-Urban Activities

Shuhui Gong, John Cartlidge, Ruibin Bai, Yang Yue, Qingquan Li, Guoping Qiu

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

4 Citations (Scopus)

Abstract

We propose an agent-based model (ABM) to simulate city-scale intra-urban activities and movements. We calibrate the ABM for New York City, using NYC Open Data trip diaries and taxi journeys. Model validation demonstrates that the ABM is able to accurately predict activity demand across the city. Further, when a new hospital wing is opened in Queens, a central district of New York City, the ABM is shown to accurately predict increased shopping demand on Staten Island, an isolated area located at the edge of the city. This demonstrates the value of applying ABM to simulate intra-urban movements and activities, offering dynamic scenario testing that is not available in many other forms of modelling.

Original languageEnglish
Title of host publication2020 5th IEEE International Conference on Big Data Analytics, ICBDA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages160-166
Number of pages7
ISBN (Electronic)9781728141114
DOIs
Publication statusPublished - May 2020
Event5th IEEE International Conference on Big Data Analytics, ICBDA 2020 - Xiamen, China
Duration: 8 May 202011 May 2020

Publication series

Name2020 5th IEEE International Conference on Big Data Analytics, ICBDA 2020

Conference

Conference5th IEEE International Conference on Big Data Analytics, ICBDA 2020
Country/TerritoryChina
CityXiamen
Period8/05/2011/05/20

Keywords

  • activity analysis
  • agent-based modeling
  • travel behaviour analysis

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Statistics, Probability and Uncertainty

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