当前位置：
X-MOL 学术
›
arXiv.cs.CE
›
论文详情
Our official English website, www.x-mol.net, welcomes your
feedback! (Note: you will need to create a separate account there.)
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning
arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2020-03-06 , DOI: arxiv-2003.03327 Rui Zhou, Shiji Song, Anke Xue, Keyou You, Hu Wu
arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2020-03-06 , DOI: arxiv-2003.03327 Rui Zhou, Shiji Song, Anke Xue, Keyou You, Hu Wu
During recent decades, the automatic train operation (ATO) system has been
gradually adopted in many subway systems. On the one hand, it is more
intelligent than traditional manual driving; on the other hand, it increases
the energy consumption and decreases the riding comfort of the subway system.
This paper proposes two smart train operation algorithms based on the
combination of expert knowledge and reinforcement learning algorithms. Compared
with previous works, smart train operation algorithms can realize the control
of continuous action for the subway system and satisfy multiple objectives (the
safety, the punctuality, the energy efficiency, and the riding comfort) without
using an offline optimized speed profile. Firstly, through analyzing historical
data of experienced subway drivers, we summarize the expert knowledge rules and
build inference methods to guarantee the riding comfort, the punctuality and
the safety of the subway system. Then we develop two algorithms to realize the
control of continuous action and to ensure the energy efficiency of train
operation. Among them, one is the smart train operation (STO) algorithm based
on deep deterministic policy gradient named (STOD) and another is the smart
train operation algorithm based on normalized advantage function (STON).
Finally, we verify the performance of proposed algorithms via some numerical
simulations with the real field data collected from the Yizhuang Line of the
Beijing Subway and their performance will be compared with existing ATO
algorithms. The results of numerical simulations show that the developed smart
train operation systems are better than manual driving and existing ATO
algorithms in respect of energy efficiency. In addition, STOD and STON have the
ability to adapt to different trip times and different resistance conditions.
更新日期：2020-08-18
京公网安备 11010802027423号