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

被引：0

作者：

Gong, Shuhui ^{[1
]}

Cartlidge, John ^{[2
]}

Bai, Ruibin ^{[3
]}

Yue, Yang ^{[4
]}

Li, Qingquan ^{[4
]}

Qiu, Guoping ^{[5
]}

机构：

[1] Univ Nottingham Ningbo China, Int Doctoral Innovat, Ningbo 315000, Peoples R China

[2] Univ Bristol, Dept Comp Sci, Bristol, Avon, England

[3] Univ Nottingham Ningbo China, Sch Comp Sci, Ningbo 315000, Peoples R China

[4] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China

[5] Univ Nottingham, Sch Comp Sci, Nottingham, England

来源：

2020 5TH IEEE INTERNATIONAL CONFERENCE ON BIG DATA ANALYTICS (IEEE ICBDA 2020) | 2020年

基金：

英国工程与自然科学研究理事会; 浙江省自然科学基金; 美国国家科学基金会;

关键词：

agent-based modeling; travel behaviour analysis; activity analysis; TRAVEL PATTERNS; DEMAND;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：160 / 166

页数：7

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