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

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

doi:10.1109/ICBDA49040.2020.9101327

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

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

Department of Computer Science

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

1 Citation (Scopus)

103 Downloads (Pure)

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 language	English
Title of host publication	2020 IEEE 5th International Conference on Big Data Analysis (ICBDA 2020)
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	160-166
Number of pages	7
ISBN (Electronic)	978-1-7281-4111-4
DOIs	https://doi.org/10.1109/ICBDA49040.2020.9101327
Publication status	Published - 9 May 2020
Event	2020 5th IEEE International Conference on Big Data Analytics - Xiamen, China Duration: 6 Mar 2020 → 9 Mar 2020 http://www.icbda.org/

Conference

Conference	2020 5th IEEE International Conference on Big Data Analytics
Abbreviated title	IEEE ICBDA 2020
Country/Territory	China
City	Xiamen
Period	6/03/20 → 9/03/20
Internet address	http://www.icbda.org/

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10.1109/ICBDA49040.2020.9101327

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@inproceedings{9bba4962609b4f73b2082b23eb9716c0,

title = "Data-Driven Agent-Based Model of Intra-Urban Activities",

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.",

author = "Shuhui Gong and John Cartlidge and Ruibin Bai and Yang Yue and Qingquan Li and Guoping Qiu",

year = "2020",

month = may,

day = "9",

doi = "10.1109/ICBDA49040.2020.9101327",

language = "English",

pages = "160--166",

booktitle = "2020 IEEE 5th International Conference on Big Data Analysis (ICBDA 2020)",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2020 5th IEEE International Conference on Big Data Analytics, IEEE ICBDA 2020 ; Conference date: 06-03-2020 Through 09-03-2020",

url = "http://www.icbda.org/",

}

Gong, S, Cartlidge, J, Bai, R, Yue, Y, Li, Q & Qiu, G 2020, Data-Driven Agent-Based Model of Intra-Urban Activities. in 2020 IEEE 5th International Conference on Big Data Analysis (ICBDA 2020). Institute of Electrical and Electronics Engineers (IEEE), pp. 160-166, 2020 5th IEEE International Conference on Big Data Analytics, Xiamen, China, 6/03/20. https://doi.org/10.1109/ICBDA49040.2020.9101327

Data-Driven Agent-Based Model of Intra-Urban Activities. / Gong, Shuhui; Cartlidge, John; Bai, Ruibin et al.

2020 IEEE 5th International Conference on Big Data Analysis (ICBDA 2020). Institute of Electrical and Electronics Engineers (IEEE), 2020. p. 160-166.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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AU - Li, Qingquan

AU - Qiu, Guoping

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N2 - 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.

AB - 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.

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DO - 10.1109/ICBDA49040.2020.9101327

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T2 - 2020 5th IEEE International Conference on Big Data Analytics

Y2 - 6 March 2020 through 9 March 2020

ER -

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

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