A DEVS-based Formal Description Method for Complex Product Behavior Models

Authors

Qingquan Lin, Department of Automation, Tsinghua University, Beijing 100084, China;Follow
Jiaran Yang, Department of Automation, Tsinghua University, Beijing 100084, China;
Heming Zhang, Department of Automation, Tsinghua University, Beijing 100084, China;Follow

Abstract

Abstract: For the online optimization of pedestrian flow control in subway station, an algorithm frame for pedestrian flow control in subway station based on machine learning is designed. The pedestrian flow control process of a subway station during morning rush hour is selected,and the agent-based model is built to simulate the control process. The training data is collected through the multiple runs of the model, which is used as the input of deep reinforcement learning network, and the mature net is obtained through adequate training to provide the optimizing scheduling policy. Linking the actual data with the mature net to realize the real-time schedule optimization of subway pedestrian flow control. Simulation experiments show that the framework of the deep reinforcement learning can realize the on-line optimization and the performance is better than traditional algorithm.

Recommended Citation

Lin, Qingquan; Yang, Jiaran; and Zhang, Heming (2022) "A DEVS-based Formal Description Method for Complex Product Behavior Models," Journal of System Simulation: Vol. 34: Iss. 4, Article 1.
DOI: 10.16182/j.issn1004731x.joss.21-1318
Available at: https://dc-china-simulation.researchcommons.org/journal/vol34/iss4/1

First Page

661

Revised Date

2022-02-25

DOI Link

https://doi.org/10.16182/j.issn1004731x.joss.21-1318

Last Page

669

CLC

TP391.9

Recommended Citation

Qingquan Lin, Jiaran Yang, Heming Zhang. A DEVS-based Formal Description Method for Complex Product Behavior Models[J]. Journal of System Simulation, 2022, 34(4): 661-669.

Corresponding Author

Heming Zhang,hmz@mail.tsinghua.edu.cn

DOI

10.16182/j.issn1004731x.joss.21-1318