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/Report/Conference proceedingConference Contribution (Conference Proceeding)

1 Citation (Scopus)
103 Downloads (Pure)


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 IEEE 5th International Conference on Big Data Analysis (ICBDA 2020)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages7
ISBN (Electronic)978-1-7281-4111-4
Publication statusPublished - 9 May 2020
Event2020 5th IEEE International Conference on Big Data Analytics - Xiamen, China
Duration: 6 Mar 20209 Mar 2020


Conference2020 5th IEEE International Conference on Big Data Analytics
Abbreviated titleIEEE ICBDA 2020
Internet address


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