During last 10 years the technology connected to video streaming analytics and deep learning algorithms connected to Intelligent Transport System and later, initiative of worldwide concept called “Smart City” pushed in reality a telematics into direction of IoT and possible use of neural network to support and create an added value in the way the Traffic Management Systems are collecting raw date in a massive data streaming mode, to be able to manage in an optimized and wright way big cities transportation networks. Recently HD cameras started to be one of the more widely used detectors replacing inductive loops and radars. The observations during productive life time of this kind of sensors on the field, created other challenges, efficiency aspects and unfavourable cost structure as consequence of usage of this product (HD camera), on such a big scale. Cameras designed to detect the length of queues at intersections require configuration by the operator. This process is extremely arduous, tedious and time consuming , considering how many cameras are located in cities. In addition, the configuration has to be done by a trained person, and what’s more, moving or replacing the camera involves its manual reconfiguration. The aim of this article is to present a prototype of an algorithm that uses RetinaNet neural network to detect bikes/motorbikes on the street, and using monodepth2 determines the length of the queue and autonomously determines the direction of cars on the road. All the work undertaken confirmed that the approach used is effective and additionally allows to limit the operator’s work only to defining the focal length and size of the camera sensor.
The paper presents the principles of the Automated System for Management Depot. ASMD system includes: - Identification System designed to recognize individuals who enters into depot (marker RFID), - Radio Data transmission system, dedicated fibre optic network, - The place for dispatcher with terminal equipped with interface for dispatcher, - Integration with: subsystem monitoring (CCTV), the position of replenishment of sand, under- track turning machine, position of the laser measuring of flat area on wheels and stickers on the wheels. System constantly analyzes the situation of track and signals from sensors available. The system is equipped with a number of reports to the dispatcher. The elements of detection and device drivers track ensure safety in the class SIL3.
The paper presents information of the project and the results of research on the problem of monitoring and management of information about occupancy of parking spaces in real time. The material draws attention the potential which, being properly used with adequate detection of the presence of selected technology and the desired transmission of information in graphic form for selected locations respectively in real time which will result in traffic that users will be more consciously decide on it's journey with the appropriate leads on the possibility of to reach the goal, as well as surrounding their existing infrastructure and the possibility of using public transport, The present level of knowledge and experience is realized in the framework of research and development project in score of ITS applications in response to the lack of consistency between multiple transport streams and the lack of their interaction on each other.
The paper is a form of mathematical description of the tool - a computer whose primary function is to manage liquidity through follow-up control of the traffic (in less than 1 second) to send signals to the drivers of management traffic light intersections of application is mentioned. The purpose of this paper is to provide reliable knowledge in this area with particular emphasis on implementation of the criterion of maintaining traffic flow take account of any abnormalities that occur as a dynamic and random variables in the urban space.
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