Real-time Machine Learning Prediction of an Agent-Based Model for Urban Decision-making

被引：0

作者：

Zhang, Yan ^{[1
]}

Grignard, Arnaud ^{[1
]}

Lyons, Kevin ^{[1
]}

Aubuchon, Alexander ^{[2
]}

Larson, Kent ^{[1
]}

机构：

[1] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[2] Northeastern Univ, Boston, MA 02115 USA

来源：

PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18) | 2018年

关键词：

machine learning; urban decision-making; agent-based modeling; real-time feedback;

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：

CityMatrix is an urban decision support system that has been developed to facilitate more collaborative and evidence-based urban decision-making for experts and non-experts. Machine learning techniques have been applied to achieve real-time prediction of an agent-based model (ABM) of city traffic. The prediction with a shallow convolutional neural network (CNN) is significantly faster than performing the original ABM, and has enough accuracy for decision-making. The result is a versatile, quick, accurate, and computationally efficient approach to provide real-time feedback and optimization for urban decision-making.

引用

页码：2171 / 2173

页数：3

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