Railway turnout system is a key infrastructure to railway safety and efficiency. However, it is prone to failure in the field. Therefore, many railway departments have adopted a monitoring system to monitor the operation status of turnouts. With monitoring data collected, many researchers have proposed different fault-diagnosis methods. However, many of the existing methods cannot realize real-time updating or deal with new fault types. This paper—based on imbalanced data—proposes a Bayes-based online turnout fault-diagnosis method, which realizes incremental learning and scalable fault recognition. First, the basic conceptions of the turnout system are introduced. Next, the feature extraction and processing of the imbalanced monitoring data are introduced. Then, an online diagnosis method based on Bayesian incremental learning and scalable fault recognition is proposed, followed by the experiment with filed data from Guangzhou Railway. The results show that the scalable fault-recognition method can reach an accuracy of 99.11%, and the training time of the Bayesian incremental learning model reduces 29.97% without decreasing the accuracy, which demonstrates the high accuracy, adaptability and efficiency of the proposed model, of great significance for labor-saving, timely maintenance and further, safety and efficiency of railway transportation.

Thermal imaging is an important technology in low-visibility environments, and due to the blurred edges and low contrast of infrared images, enhancement processing is of vital importance. However, to some extent, the existing enhancement algorithms based on pixel-level information ignore the salient feature of targets, the temperature which effectively separates the targets by their color. Therefore, based on the temperature and pixel features of infrared images, first, a threshold denoising model based on wavelet transformation with bilateral filtering (WTBF) was proposed. Second, our group proposed a salient components enhancement method based on a multi-scale retinex algorithm combined with frequency-tuned salient region extraction (MSRFT). Third, the image contrast and noise distribution were improved by using salient features of orientation, color, and illuminance of night or snow targets. Finally, the accuracy of the bounding box of enhanced images was tested by the pre-trained and improved object detector. The results show that the improved method can reach an accuracy of 90% of snow targets, and the average precision of car and people categories improved in four low-visibility scenes, which demonstrates the high accuracy and adaptability of the proposed methods of great significance for target detection, trajectory tracking, and danger warning of automobile driving.

This paper proposes a new hybrid hierarchical roadside unit (RSU) deployment algorithm that combines traditional RSU deployment strategies with temporary RSUs (t-RSUs) selected from many parked vehicles. The driving vehicles in the corresponding RSU coverage section are divided into clusters and the demand is thus summarized. We first solve the problem of how to choose the appropriate cars serving as t-RSUs and then optimize the RSU deployment with the dynamic existing t-RSUs. We simulate the deployment application in a downtown area of Shanghai, China, using the simulation of urban mobility (SUMO), version 1.11.0, an open-source traffic simulation package, and its traffic-control interface (TraCI) with Python. The simulation results show that, within a given economic cost, the proposed hybrid hierarchical RSU deployment algorithm outperforms the maximum vehicle coverage deployment algorithm (MVCD) and the random deployment algorithm (RandDeploy) in terms of overall vehicle coverage and point-to-point vehicle connectivity ratio. Our scheme provides new solutions and ideas for the deployment of RSUs in future urban traffic environments.

Purpose: When a railway emergency occurs, it often leads to unexpected consequences, especially for trains of higher speed and larger passenger flow. Therefore, the railway emergency plan, a pre-established plan to deal with emergencies, plays an important role in reducing injuries and losses. However, the existing railway emergency plans remain as plain-text documents, requiring lots of manual work to capture the important regulations. This paper aims to propose a visualized, formal and digital railway emergency plan modeling method based on hierarchical timed Petri net (HTPN), which is also of better interpretability. Design/methodology/approach: First, the general railway emergency plan was analyzed. Second, the HTPN-based framework model for the general railway emergency plan was proposed. Then, the instantiated model of electric multiple units rescue emergency plan was built by ExSpect, a Petri net simulation tool. Findings: The experiments show that the proposed model is more digital and of better readability, visualization and performability, and, meanwhile, can generally conform to the practice well, offering a promising reference for future analysis of the optimization of railway emergency plans. Originality/value: This study offers a promising reference for future analysis of the optimization of railway emergency plans.

In order to enhance the precision of network traffic prediction for multi-area vehicle networks, this paper proposes a two-tier distributed Software-Defined Vehicular Network (SDVN) architecture equipped with multiple controllers, which is subsequently deployed to manage traffic across regions, thus minimizing communication costs and enabling seamless vehicle movement. We firstly build on control plane placement (CPP) research, focusing on deployment strategies that impact scalability, performance, and fault tolerance. It highlights the importance of hierarchical decision-making with multiple controllers handling varying traffic demands. A comprehensive comparison of various machine learning and deep learning algorithms is then conducted to evaluate their efficacy in forecasting SDVN traffic patterns, which is crucial for system efficiency. Experiments show the proposed architecture’s effectiveness in traffic prediction and management in Shenzhen’s Longhua New District. The study confirms that the SDVN system enhances traffic prediction and management, improving urban mobility and Internet of Vehicles. The paper offers a framework using advanced technologies to address challenges in traffic prediction and management in modern vehicular networks.