Deep reinforcement learning has gradually shown its latent decision-making
ability in urban rail transit autonomous operation. However, since
reinforcement learning can not neither guarantee safety during learning nor
execution, this is still one of the major obstacles to the practical
application of reinforcement learning. Given this drawback, reinforcement
learning applied in the safety-critical autonomous operation domain remains
challenging without generating a safe control command sequence that avoids
overspeed operations. Therefore, a SSA-DRL framework is proposed in this paper
for safe intelligent control of urban rail transit autonomous operation trains.
The proposed framework is combined with linear temporal logic, reinforcement
learning and Monte Carlo tree search and consists of four mainly module: a
post-posed shielding, a searching tree module, a DRL framework and an
additional actor. Furthermore, the output of the framework can meet speed
constraint, schedule constraint and optimize the operation process. Finally,
the proposed SSA-DRL framework for decision-making in urban rail transit
autonomous operation is evaluated in sixteen different sections, and its
effectiveness is demonstrated through an ablation experiment and comparison
with the scheduled operation plan.

提出了一种用于安全智能控制城市轨道交通自主运营列车的 SSA-DRL 框架，结合了线性时态逻辑、强化学习和蒙特卡洛树搜索，可以生成满足速度约束、时间表约束和优化运营流程的安全控制指令序列。通过消融实验和与计划操作方案的比较，评估了该框架在城市轨道交通自主运营中的决策效果。

如何确保安全的控制策略？走向城市公共交通自主运营的强化学习

How to ensure a safe control strategy? Towards a SRL for urban transit  autonomous operation

In the realm of urban transportation, metro systems serve as crucial and
sustainable means of public transit. However, their substantial energy
consumption poses a challenge to the goal of sustainability. Disturbances such
as delays and passenger flow changes can further exacerbate this issue by
negatively affecting energy efficiency in metro systems. To tackle this
problem, we propose a policy-based reinforcement learning approach that
reschedules the metro timetable and optimizes energy efficiency in metro
systems under disturbances by adjusting the dwell time and cruise speed of
trains. Our experiments conducted in a simulation environment demonstrate the
superiority of our method over baseline methods, achieving a traction energy
consumption reduction of up to 10.9% and an increase in regenerative braking
energy utilization of up to 47.9%. This study provides an effective solution to
the energy-saving problem of urban rail transit.

在城市交通领域，地铁系统是重要和可持续的公共交通方式。该研究利用基于策略的强化学习方法，通过调整列车停站时间和巡航速度等方式，优化地铁系统的能效，以应对干扰问题，实验结果表明该方法与基准方法相比性能更佳，最高可实现牵引能耗降低达 10.9％、再生制动能量利用率提高 47.9％，提供了一种有效的城市铁路交通节能解决方案。

应用强化学习优化地铁系统在不确定扰动下的能源效率

Optimizing Energy Efficiency in Metro Systems Under Uncertainty  Disturbances Using Reinforcement Learning

Accurate passenger flow prediction of urban rail transit is essential for
improving the performance of intelligent transportation systems, especially
during the epidemic. How to dynamically model the complex spatiotemporal
dependencies of passenger flow is the main issue in achieving accurate
passenger flow prediction during the epidemic. To solve this issue, this paper
proposes a brand-new transformer-based architecture called STformer under the
encoder-decoder framework specifically for COVID-19. Concretely, we develop a
modified self-attention mechanism named Causal-Convolution ProbSparse
Self-Attention (CPSA) to model the multiple temporal dependencies of passenger
flow with low computational costs. To capture the complex and dynamic spatial
dependencies, we introduce a novel Adaptive Multi-Graph Convolution Network
(AMGCN) by leveraging multiple graphs in a self-adaptive manner. Additionally,
the Multi-source Data Fusion block fuses the passenger flow data, COVID-19
confirmed case data, and the relevant social media data to study the impact of
COVID-19 to passenger flow. Experiments on real-world passenger flow datasets
demonstrate the superiority of ST-former over the other eleven state-of-the-art
methods. Several ablation studies are carried out to verify the effectiveness
and reliability of our model structure. Results can provide critical insights
for the operation of URT systems.

提出了一种名为 ST-former 的基于变压器的全新架构，在编码器 - 解码器框架下，利用修改的 CPSA 机制和自适应的 AMGCN 网络来准确预测 COVID-19 期间的城市轨道交通乘客流量变化，并将多源数据融合以研究 COVID-19 对客流量的影响