Analysis of the possibility of implementing interoperability tests on Polish railways

Ensuring the greatest possible interoperability of rail transport, especially for railways in Europe, is one of the key projects to be implemented using the European Rail Traffic Management System (ERTMS), including the European Train Control System (ETCS) and the Global System for Mobile Communications-Railways (GSM-R). The ERTMS system aims to replace many different rail traffic control systems with one, common and unified European solution (Commission Regulation (EU) 2016/919, 2016), (Directive (EU) 2016/797, n.d.). Its creation was dictated by the desire to standardize the traffic control systems present in the territories of various European countries, at the same time extending their functionality and eliminating the existing technical barriers. The aim of this article is to present the possibility of implementation interoperability tests - IOP tests, on Polish railways. These tests are intended to provide a faster, more accurate and less costly demonstration of compliance with the ETCS interoperability requirements compared to field tests. The work defines the concept of interoperability tests as well as the purpose of their application. The general principles and procedures for conducting interoperability tests are presented. In the further part of the work, the operation of laboratories in the European Union is analysed. The laboratories functional in Switzerland and Spain were selected for this analysis. Following, the paper presents the validity of implementing interoperability tests on the territory of the Republic of Poland. On the basis of the pan-European procedure of conducting interoperability tests and the experience of foreign independent laboratories, conditions for the implementation of tests in the Polish railways were developed, which could be used in the future to introduce IOP tests in Poland.

Queuing Theory in Traffic Management System

In cities where the number of vehicles has consistently expanded faster than the transportation infrastructure available to serve them. More on queuing theory and its crucial application has been discussed in the current study. In Thudiyalur, Gandhipuram, and Periyanaickenpalayam, all in Coimbatore, this research examines the usefulness of queuing theory in the field of traffic management. The concept of traffic intensity isapplied to a set of areas in queuing theory in this study. Keywords: Traffic intensity, Queuing theory, Single server Poisson model

Review various technologies used to manage Traffic Management System using IOT

Internet of things (IoT) is the network of the devices includes the updating in technology, various devices are using sensors, actuators, embedded computing and cloud computing. This type of system leads to smart architecture in the home, cities and smart world. IoT plays an important role in traffic controlling and managing. In this paper, we give an overview of the various methods of traffic control management. With the help of this IOT kit, which includes different sensors to collect the data and process it accordingly with the help of big data analysis and deep learning algorithms, most accurate and efficient results are obtained for traffic management.

A review of computational intelligence methods for traffic management systems

One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.

A review of computational intelligence methods for traffic management systems

One of the most important challenges in modern city life is to enable effective and efficient traffic management system. Recently, computational intelligence methods have become increasingly popular for traffic management system design, application, and monitoring. Computational intelligence methods are often deployed for managing traffic, that is for reducing mileage, congestion, the use of fuels, and environmental impact. The aim of this paper is twofold. First, to present the three main areas in a computational intelligence approach, namely neural networks, fuzzy logic systems, and evolutionary computation. Second, to emphasize their impact on various traffic management domains, including traffic flow forecasting, traffic light control, traffic fatalities prediction, traffic sign detection, and optimization of transportation networks.

Research and Implementation of Fast-LPRNet Algorithm for License Plate Recognition

The license plate recognition is an important part of the intelligent traffic management system, and the application of deep learning to the license plate recognition system can effectively improve the speed and accuracy of recognition. Aiming at the problems of traditional license plate recognition algorithms such as the low accuracy, slow speed, and the recognition rate being easily affected by the environment, a Convolutional Neural Network- (CNN-) based license plate recognition algorithm-Fast-LPRNet is proposed. This algorithm uses the nonsegment recognition method, removes the fully connected layer, and reduces the number of parameters. The algorithm—which has strong generalization ability, scalability, and robustness—performs license plate recognition on the FPGA hardware. Increaseing the depth of network on the basis of the Fast-LPRNet structure, the dataset of Chinese City Parking Dataset (CCPD) can be recognized with an accuracy beyond 90%. The experimental results show that the license plate recognition algorithm has high recognition accuracy, strong generalization ability, and good robustness.