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.

Roadside unit (RSU) cloud and its vehicle-to-infrastructure (V2I) connectivity can enable various security, entertainment, and shared mobility applications for vehicles in intelligent transportation systems (ITS) through wireless communications. In this article, the deep programmability of software-defined networking (SDN) is employed to dynamically reconfigure network hosting services and their data forwarding information for effectively meeting the basic shared mobility applications’ needs in vehicle ad hoc networks (VANETs). Multipath is also enabled to forward data flow for balancing network links utilization rate and SDN is thus utilized to achieve the minimum cloud delay with the least number of hosts, which can be summarized as a mixed-integer linear programming (MILP) problem. The joint optimization (JO) algorithm is proposed and in contrast to the two single-objective algorithms which are the delay optimization (DO) algorithm and host optimization (HO) algorithm, respectively. Results show that, for the single-threading instance, the JO and DO algorithms are the same in essence. For the multithreading instance, the JO algorithm generally outperforms the two single-objective optimization algorithms, respectively, under given demands. Furthermore, results also demonstrate that the services should be deployed globally in a distributed manner rather than in the centralized manner for achieving the minimized cloud delay in designing an RSU cloud.

Communication-based train control (CBTC) has been the prevailing technology of the urban transit signaling system. However, CBTC also faces a few issues to extend and maintain because of its complicated structure. This paper presents a novel urban transit signaling system architecture, software-defined train control (SDTC), which is based on cloud and high-speed wireless communication technology. The core functions of the proposed SDTC, including the onboard controller, are implemented in the cloud platform, with only sensors and input–output (IO) units remaining on the trackside and the train. Because of the scalable framework, the system function can be expanded according to the user’s demand, making signaling as a service possible. With warm standby server redundancy, SDTC has better reliability. Compared with the traditional CBTC architecture, the mean time between failures is improved by 39% by calculating typical project parameters by the Markov model based on some assumptions.

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.

In existing urban rail systems, most trains are operated by automatic control, which places high demands on the control effectiveness of automatic train operation (ATO). In this study, a train operation model considering the response delay is firstly constructed. Subsequently, by analyzing and comparing the existing mainstream research methods, neural network and sliding mode control techniques were selected and incorporated into the speed control of the train. Among them, the dynamic sliding mode technique, is used to optimize the PID control effect of ATO. Single neuron and back propagation (BP) neural network algorithms are applied to the selection of PID control parameters. The study selects the difference between the actual speed of the control train and the target speed as the control objective. Through continuous optimization and iteration of the control parameters, the control accuracy of the train operation was improved. Finally, this study validates and simulates different model control methods using the actual operation data of urban rail transit. The results show that the sliding mode PID control model optimized with BP neural network performs better in terms of error distribution, average error value, and control effect variance under different simulation scenarios, showing good tracking ability and robustness. The related research results are expected to be applied to the initial selection of rail vehicle control parameters, dynamic adaptive operation control, and other fields, providing practical help to rail operators and rail signaling companies.