Design of Real-time Tracking and Early-warning of High-speed Trains Based on Beidou System

2020 ◽  
Keyword(s):  
Real Time ◽  
Early Warning ◽  
High Speed ◽  
Beidou System ◽  
High Speed Trains ◽  
Real Time Tracking

A real-time control method-based simulation for high-speed trains on large-scale rail network

2017 ◽  
Vol 28 (10) ◽  
pp. 1750126 ◽  
Author(s):  
Yutong Liu ◽  
Chengxuan Cao ◽  
Yaling Zhou ◽  
Ziyan Feng
Keyword(s):  
Real Time ◽  
Traffic Flow ◽  
High Speed ◽  
Large Scale ◽  
Simulation Method ◽  
Real Time Control ◽  
Rail Network ◽  
Time Control ◽  
High Speed Trains ◽  
Rail Traffic

In this paper, an improved real-time control model based on the discrete-time method is constructed to control and simulate the movement of high-speed trains on large-scale rail network. The constraints of acceleration and deceleration are introduced in this model, and a more reasonable definition of the minimal headway is also presented. Considering the complicated rail traffic environment in practice, we propose a set of sound operational strategies to excellently control traffic flow on rail network under various conditions. Several simulation experiments with different parameter combinations are conducted to verify the effectiveness of the control simulation method. The experimental results are similar to realistic environment and some characteristics of rail traffic flow are also investigated, especially the impact of stochastic disturbances and the minimal headway on the rail traffic flow on large-scale rail network, which can better assist dispatchers in analysis and decision-making. Meanwhile, experimental results also demonstrate that the proposed control simulation method can be in real-time control of traffic flow for high-speed trains not only on the simple rail line, but also on the complicated large-scale network such as China’s high-speed rail network and serve as a tool of simulating the traffic flow on large-scale rail network to study the characteristics of rail traffic flow.

A measurement method for the overturning coefficient of high-speed trains passing through complex terrain sections under strong wind conditions

2019 ◽  
Vol 234 (8) ◽  
pp. 885-895
Author(s):  
Zhaijun Lu ◽  
Weijia Huang ◽  
Mu Zhong ◽  
Dongrun Liu ◽  
Tian Li ◽  
...  
Keyword(s):  
Real Time ◽  
Complex Terrain ◽  
High Speed ◽  
Suspension System ◽  
Strong Wind ◽  
Real Time Monitoring ◽  
Monitoring Method ◽  
High Speed Trains ◽  
Strong Winds ◽  
Pass Through

Real-time monitoring of overturning coefficients is very important for ensuring the safety of high-speed trains passing through complex terrain sections under strong wind conditions. In recent years, the phenomenon of “car swaying” that occurs when trains pass through the complex terrain has brought new challenges to ensuring the safety and riding comfort of passengers. In China, more and more high-speed trains are facing strong wind environments when running in complex terrain sections. However, due to the limitation of objective conditions, so far, only a few economical and effective methods of measurement have been developed that are suitable for real-time monitoring of the overturning coefficient of commercial vehicles. Therefore, considering the applicability and universality of such a monitoring method, this study presents a method for measuring the overturning coefficient of trains using the primary suspension system under strong winds. A vehicle test was carried out to verify the accuracy of the method. The results show that after correction, the overturning coefficient obtained from the primary suspension system is generally consistent with the overturning coefficient obtained from the instrumented wheelset. The method of measuring the overturning coefficient of trains in strong wind environments with the primary suspension system is, thus, proven feasible.

scholarly journals Multitarget Real-Time Tracking Algorithm for UAV IoT

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Tao Hong ◽  
Qiye Yang ◽  
Peng Wang ◽  
Jinmeng Zhang ◽  
Wenbo Sun ◽  
...  
Keyword(s):  
Machine Learning ◽  
Target Tracking ◽  
Real Time ◽  
High Speed ◽  
Rapid Development ◽  
Machine Learning Algorithms ◽  
Urban Life ◽  
Tracking Accuracy ◽  
Tracking Model ◽  
Real Time Tracking

Unmanned aerial vehicles (UAVs) have increased the convenience of urban life. Representing the recent rapid development of drone technology, UAVs have been widely used in fifth-generation (5G) cellular networks and the Internet of Things (IoT), such as drone aerial photography, express drone delivery, and drone traffic supervision. However, owing to low altitude and low speed, drones can only limitedly monitor and detect small target objects, resulting in frequent intrusion and collision. Traditional methods of monitoring the safety of drones are mostly expensive and difficult to implement. In smart city construction, a large number of smart IoT cameras connected to 5G networks are installed in the city. Captured drone images are transmitted to the cloud via a high-speed and low-latency 5G network, and machine learning algorithms are used for target detection and tracking. In this study, we propose a method for real-time tracking of drone targets by using the existing monitoring network to obtain drone images in real time and employing deep learning methods by which drones in urban environments can be guided. To achieve real-time tracking of UAV targets, we employed the tracking-by-detection mode in machine learning, with the network-modified YOLOv3 (you only look once v3) as the target detector and Deep SORT as the target tracking correlation algorithm. We established a drone tracking dataset that contains four types of drones and 2800 pictures in different environments. The tracking model we trained achieved 94.4% tracking accuracy in real-time UAV target tracking and a tracking speed of 54 FPS. These results comprehensively demonstrate that our tracking model achieves high-precision real-time UAV target tracking at a reduced cost.

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