Search Results (9)

Modern metro rail systems have problems concerning the safety of passengers and the operational efficiency. Among these, passenger obstruction is a major challenge which refers to the unintentional or intentional interference of passengers in train and platform screen doors, while boarding or alighting from the trains. This paper provides a risk assessment of passenger obstruction at Orange Line Metro Rail Transit System (OLMRTS) in Lahore, Pakistan. This study adopted structured observations, incident analysis and review by experts to control the obstruction cases. Both quantitative and qualitative data analyses of obstruction cases were performed to evaluate the key risk factors associated with passenger obstructions in OLMRTS. Based on the risk assessment framework, prioritized countermeasures with higher risk reduction impacts have been proposed. The effectiveness of the countermeasures was evident in the substantial reduction in obstruction cases by 80%. This research paper will present a reduction in safety risks by reducing the likelihood of incidents and without compromising the passenger service of OLMRTS. Full article

Modern subway platforms are generally equipped with platform screen door systems to enhance safety, but the gap between the platform screen doors and train doors may cause passengers or objects to become trapped, leading to accidents. Addressing the issues of excessive parameter counts and computational complexity in existing foreign object intrusion detection algorithms, as well as false positives and false negatives for small objects, this article introduces a lightweight deep learning model based on YOLOv11n, named GA-YOLOv11. First, a lightweight GhostConv convolution module is introduced into the backbone network to reduce computational resource waste in irrelevant areas, thereby lowering model complexity and computational load. Additionally, the GAM attention mechanism is incorporated into the head network to enhance the model’s ability to distinguish features, enabling precise identification of object location and category, and significantly reducing the probability of false positives and false negatives. Experimental results demonstrate that in comparison to the original YOLOv11n model, the improved model achieves 3.3%, 3.2%, 1.2%, and 3.5% improvements in precision, recall, mAP@0.5, and mAP@0.5: 0.95, respectively. In contrast to the original YOLOv11n model, the number of parameters and GFLOPs were reduced by 18% and 7.9%, respectfully, while maintaining the same model size. The improved model is more lightweight while ensuring real-time performance and accuracy, designed for detecting foreign objects in subway platform gaps. Full article

Mechanical and electrical equipment is an important component of urban rail transit stations, and the service capacity of stations is affected by its reliability. To solve the problem of predicting faults in station mechanical and electrical equipment with sparse data, this study proposes a fault prediction framework based on SSA-CNN-LSTM. Firstly, this article proposes a fault enhancement method for station electromechanical equipment based on TimeGAN, which expands and generates data that conform to the temporal characteristics of the original dataset, to solve the problem of sparse data in the original fault dataset. An SSA-CNN-LSTM model is then established to extract effective data features from low-dimensional data with insufficient feature depth through structures such as convolutional layers and pooling layers in a CNN, determine the optimal hyperparameters, automatically optimize the model network size, solve the problem of the difficult determination of the neural network model size, and achieve accurate prediction of the fault rate of station electromechanical equipment. Finally, an engineering verification was conducted on the platform screen door (PSD) systems in stations on Shanghai Metro Lines 1, 5, 9, and 10. The experiments showed that the proposed prediction method improved the RMSE by 0.000699, the MAE by 0.00042, and the R2 index by 0.109779 when predicting the fault rate data of platform screen doors on all of the lines. When predicting the fault rate data of the screen doors on a single line, the performance of the model was better than that of the CNN-LSTM model optimized with the PSO algorithm. Full article

The prevention of passengers’ access to the tracks is crucial for current urban railway transport. The “Safety first” principal led to the need to separate the platform from the train tracks as a measure of passengers’ protection. Due to this, many train stations around the world are equipped with barrier (screen) security systems. However, the requirements for passengers’ comfort are high as well. Automated transport should ensure the trains are on time and passengers’ exchange at stations is smooth. Therefore, it is necessary that station passenger barrier systems comply with the line signalling systems. Preventing or reducing additional delays is essential to provide efficient transport services and maximum line capacity while ensuring passenger safety. This report provides the operation outcomes for different train lines with implemented advanced station barrier systems for passengers—automatic platform doors (vertical or horizontal) and beam barriers—and indicates the strong and weak points of the given solutions. Full article

Experience-based methods like reinforcement learning (RL) are often deemed less suitable for the safety field due to concerns about potential safety issues. To bridge this gap, we introduce STPA-RL, a methodology that integrates RL with System-Theoretic Process Analysis (STPA). STPA is a safety analysis technique that identifies causative factors leading to unsafe control actions and system hazards through loss scenarios. In the context of STPA-RL, we formalize the Markov Decision Process based on STPA analysis results to incorporate control algorithms into the system environment. The agent learns safe actions through reward-based learning, tracking potential hazard paths to validate system safety. Specifically, by analyzing various loss scenarios related to the Platform Screen Door, we assess the applicability of the proposed approach by evaluating hazard trajectory graphs and hazard frequencies in the system. This paper streamlines the RL process for loss scenario identification through STPA, contributing to self-guided loss scenarios and diverse system modeling. Additionally, it offers effective simulations for proactive development to enhance system safety and provide practical assistance in the safety field. Full article

The utilization of primary school buildings is multifaceted, primarily due to the high occupancy density, varying thermal preferences among occupants, diverse indoor activities (such as walking, sports, and conversation), and a constant flow of individuals entering and exiting the building. This results in the frequent opening and closing of external windows and doors and fluctuations in internal heat gain. Consequently, frequent interactions between the indoor and outdoor microenvironments lead to energy losses. This study conducts a comprehensive literature review on building energy loss stemming from occupant behavior and the interactions between indoor and outdoor microenvironments. Furthermore, it proposes a dynamic real-time monitoring system based on a foundation of computer data capture and a visualization platform for building energy loss. The research methods include data crawling, data association rule mining, and data association analysis. The research findings yield a universally applicable and informative building energy-saving design system based on extensive data analysis. Additionally, the system presents information on occupants’ behavior and the microclimate data of indoor and outdoor environments on a computer screen, facilitating human–machine communication and enabling timely adjustments to be made, thus facilitating the construction of design strategies for new buildings and operation and maintenance strategies for existing buildings. Full article

The complex wind effects around platform screen doors (PSDs) caused by train-induced piston wind effect and positive micropressure waves in subway station platforms are investigated. Numerical modeling of the wind field around full-scale PSDs with real gaps under different inflow conditions is developed to analyze the pressure distributions on and around the PSDs and the corresponding recirculation regions in the frontal and rear PSD areas with computational fluid dynamics (CFD) method. An equivalent porous media model is developed to obtain the relationship between the pressure difference and wind velocity based on Darcy–Forchheimer’s Law. It includes a viscosity loss term and an inertial loss term in the simulation of the air leakage flow generated from the PSD gap. The coefficients of these two terms are estimated from the CFD results from the full-scale models. The complicated flow field originated from the gaps is the main cause of the large wind pressure on the PSD, and the flow velocity on the platform may significantly affect the comfort of pedestrians and of the safety design of the PSD system. Full article

Almost all recently built subway stations are equipped with Platform Screen Doors (PSDs) due to the numerous proven benefits of these systems. In addition, PSDs are now being introduced in existing subway stations, but their operation in conjunction with previously designed ventilation systems in case of emergency should be deeply studied. In this context, the objective of this study is to assess the efficiency of the planned emergency strategy (coupled operation, ventilation systems–PSDs system) in the case of trains on fire stopped at the platform of a subway station retrofitted with PSDs. The approach is based on Computational Fluid Dynamics (CFD) full-scale simulations to predict the airflow, temperature, and pollutant (carbon monoxide—CO and carbon dioxide—CO₂) concentrations caused by the fire. The results show the evident contribution of PSDs in stopping the dispersion of hot and polluted air in the subway station during the entire simulation time (20 min from the arrival of the train on fire). Consequently, the investigated emergency strategy (exhausting air both through the “over track system” and the “under platform system”, simultaneously with the opening of the PSDs on the side with the train on fire) assures the safe evacuation of passengers as soon as they have left the subway train. The results indicate that access to the platform is not perturbed by high temperatures or dangerous concentrations of CO and CO₂. Full article

Following the success of and the high demand for recombinant protein-based therapeutics during the last 25 years, the pharmaceutical industry has invested significantly in the development of novel treatments based on biologics. Mammalian cells are the major production systems for these complex biopharmaceuticals, with Chinese hamster ovary (CHO) cell lines as the most important players. Over the years, various engineering strategies and modeling approaches have been used to improve microbial production platforms, such as bacteria and yeasts, as well as to create pre-optimized chassis host strains. However, the complexity of mammalian cells curtailed the optimization of these host cells by metabolic engineering. Most of the improvements of titer and productivity were achieved by media optimization and large-scale screening of producer clones. The advances made in recent years now open the door to again consider the potential application of systems biology approaches and metabolic engineering also to CHO. The availability of a reference genome sequence, genome-scale metabolic models and the growing number of various “omics” datasets can help overcome the complexity of CHO cells and support design strategies to boost their production performance. Modular design approaches applied to engineer industrially relevant cell lines have evolved to reduce the time and effort needed for the generation of new producer cells and to allow the achievement of desired product titers and quality. Nevertheless, important steps to enable the design of a chassis platform similar to those in use in the microbial world are still missing. In this review, we highlight the importance of mammalian cellular platforms for the production of biopharmaceuticals and compare them to microbial platforms, with an emphasis on describing novel approaches and discussing still open questions that need to be resolved to reach the objective of designing enhanced modular chassis CHO cell lines. Full article