1 research outputs found
Smart Train Operation Algorithms based on Expert Knowledge and Reinforcement Learning
During recent decades, the automatic train operation (ATO) system has been
gradually adopted in many subway systems for its low-cost and intelligence.
This paper proposes two smart train operation algorithms by integrating the
expert knowledge with reinforcement learning algorithms. Compared with previous
works, the proposed algorithms can realize the control of continuous action for
the subway system and optimize multiple critical objectives without using an
offline speed profile. Firstly, through learning historical data of experienced
subway drivers, we extract 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 for optimizing the energy
efficiency of train operation. One is the smart train operation (STO) algorithm
based on deep deterministic policy gradient named (STOD) and the other 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 from the Yizhuang Line of the Beijing
Subway and illustrate that the developed smart train operation algorithm are
better than expert manual driving and existing ATO algorithms in terms of
energy efficiency. Moreover, STOD and STON can adapt to different trip times
and different resistance conditions