As urban rail transit construction advances with information technology, modernization, information, and intelligence have become the direction of development. A growing number of cloud platforms are being developed for transit in urban areas. However, the increasing scale of urban rail cloud platforms, coupled with the deployment of urban rail safety applications on the cloud platform, present a huge challenge to cloud reliability.One of the key components of urban rail transit cloud platforms is Automatic Train Supervision (ATS). The failure of the ATS cloud service would result in less punctual trains and decreased traffic efficiency, making it essential to research fault tolerance methods based on cloud computing to improve the reliability of ATS cloud services. This paper proposes a proactive, reliability-aware failure recovery method for ATS cloud services based on reinforcement learning. We formulate the problem of penalty error decision and resource-efficient optimization using the advanced actor-critic (A2C) algorithm. To maintain the freshness of the information, we use Age of Information (AoI) to train the agent, and construct the agent using Long Short-Term Memory (LSTM) to improve its sensitivity to fault events. Simulation results demonstrate that our proposed approach, LSTM-A2C, can effectively identify and correct faults in ATS cloud services, improving service reliability.

Leaky Coaxial Cables (LCXs) are widely used in tunnels and underpasses for smoother electric field coverage in wireless communication systems. But there are very few studies on LCXs working in 2.4GHz, which are the frequency bands used in 802.11b/g. The paper shows characteristics of 2.4GHz LCX through simulations and field tests. The results show that the communication through 2.4GHz frequency band LCX can provide stronger radio signals and thus better throughput compared with free space in tunnels. What's more, the handover interruption time between APs can be reduced because the Ping-Pong handover is avoided through LCX.

Train-to-wayside (T2W) and train-to-train (T2T) communication mode may coexist in future train-centric communication-based train control (CBTC) systems. The feasibility of train-to-train communication in urban rail transit is analyzed first. Referring to the D2D communication scenario in the general cellular network, this paper establishes a radio resource optimization model for the coexistence of train-to-train communication and train-to-wayside communication. Aiming at scheduling radio time-frequency resources in the dedicated frequency band more efficiently, we propose a Stackelberg game based radio resource management algorithm based on the consideration of different service priorities of trains. The results show that the proposed algorithm can effectively guarantee the performance of the system and improve the reliability of the CBTC system.

Train-to-wayside (T2W) and train-to-train (T2T) communication modes may coexist in future train-centric communication-based train control (CBTC) systems. The feasibility of T2T communication in urban rail transit is analyzed first. Referring to the device-to-device (D2D) communication scenario in the general cellular network, this paper establishes a radio resource optimization model for the coexistence of train-to-train communication and train-to-wayside communication. With the aim of more efficient scheduling of radio time-frequency resources in the dedicated frequency band, we propose a Stackelberg game-based radio resource management algorithm based on the consideration of different service priorities of trains. The analysis and simulation results show that the proposed algorithm can effectively guarantee the performance of the system and improve the reliability of the CBTC system.

The safe operation of rail transit systems relies heavily on the efficient and reliable maintenance of their equipment, as any malfunction or abnormal operation may pose serious risks to transportation safety. Traditional manual inspection methods are often characterized by high costs, low efficiency, and susceptibility to human error. To address these limitations, this paper presents a rail transit equipment inspection system based on Edge Intelligence (EI) and 5G technology. The proposed system adopts a cloud–edge–end collaborative architecture that integrates Computer Vision (CV) techniques to automate inspection tasks; specifically, a fine-tuned YOLOv8 model is employed for object detection of personnel and equipment, while a ResNet-18 network is utilized for equipment status classification. By implementing an ETSI MEC-compliant framework on edge servers (NVIDIA Jetson AGX Orin), the system enhances data processing efficiency and network performance, while further strengthening security through the use of a 5G private network that isolates critical infrastructure data from the public internet, and improving robustness via distributed edge nodes that eliminate single points of failure. The proposed solution has been deployed and evaluated in real-world scenarios on Beijing Metro Line 6. Experimental results demonstrate that the YOLOv8 model achieves a mean Average Precision (mAP@0.5) of 92.7% ± 0.4% for equipment detection, and the ResNet-18 classifier attains 95.8% ± 0.3% accuracy in distinguishing normal and abnormal statuses. Compared with a cloud-centric architecture, the EI-based system reduces the average end-to-end latency for anomaly detection tasks by 45% (28.5 ms vs. 52.1 ms) and significantly lowers daily bandwidth consumption by approximately 98.1% (from 40.0 GB to 0.76 GB) through an event-triggered evidence upload strategy involving images and short video clips, highlighting its superior real-time performance, security, robustness, and bandwidth efficiency.