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EN
The capability of a telematic vision system of estimating dimensions of vehicles is used for such tasks as vehicle classification or preselection of vehicles that violate local vehicle size limitations. Also in some European countries dimensions of heavy vehicles must obey some global regulations. Furthermore, vehicle size estimation allows us to determine the structure of traffic and can be very useful for advanced traffic flow control. Many existing Intelligent Transportation Systems consist of a large number of video cameras located in various places e.g., the ITS in Wrocław uses more than 1400 cameras. In this paper we propose a new method developed by the ArsNumerica Group and CyberTech Scientific Circle for the precise estimation of vehicle sizes using a single camera. The method does not require the entering of measurements such as the distance between lane lines or the height of the camera above the roadway. Only one vehicle’s dimensions are used for calibration. The proposed method is easy to implement and may be applied with the OpenCV library which is free both for academic and commercial use. The method is tested on real-world video streams. The obtained results are shown and analyzed in the paper.
EN
Nowadays, vehicles are equipped with various on-board devices that work in Bluetooth technology and log on to the ITS infrastructure whenever passing by Bluetooth readers. The location of Bluetooth readers is an important issue for travel time prediction in urban areas. Bluetooth technology is used to enhance travel time prediction accuracy and is additional to vehicle license number identification. The algorithms for travel time prediction are used by such technologies e.g., TRAX to offer the road user an alternative route to traverse the most congested regions of the city in the most efficient way. In this paper we present the implementation of the algorithm that enables us to match Bluetooth on-board devices, and also cell phones that are mounted or are just in vehicles of road users. Since the ITS is a source of an enormous and increasing amount of data for this purpose we engage Big Data tools such as Apache HaDoop and Apache Spark. To build Map-Reduce tasks we use Hive-SQL. The algorithm is tested on ITS data from the city of Wroclaw. The results of the algorithm may be used to locate stolen vehicles.
PL
Współczesne pojazdy wyposażane są w wiele różnych urządzeń Bluetooth, które logują się do infrastruktury ITS za każdym razem gdy przejeżdżają one w zasięgu czytników Bluetooth. Położenie czytników Bluetooth jest zagadnieniem istotnym dla metod predykcji czasu przejazdu w regionach zurbanizowanych. Technologia Bluetooth jest użyta do poprawy dokładności czasu przejazdu i jest uzupełnieniem dla identyfikacji pojazdów po numerach rejestracyjnych. Algorytmy do predykcji czasu przejazdu są używane do proponowania użytkownikom trasy alternatywnej w celu przejazdu przez najbardziej zatłoczone regiony miasta w sposób najbardziej efektywny. W artykule jest prezentowana implementacja algorytmu, który pozwala połączyć urządzenia Bluetooth i telefony znajdujące się w pojazdach z samymi pojazdami. Do tego celu angażuje się narzędzia Big Data takie jak Apache HaDoop i Apache Spark. Do zbudowania zadań Map-Reduce używa się Hive-SQLa. Algorytm był testowany na danych z wrocławskiego ITS. Wyniki działania algorytmu mogą być użyte do lokalizowania skradzionych pojazdów.
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EN
Nowadays, the crucial issue of guidance systems based on a GPS signal is that they are not able to redirect road users, taking into account the current state of traffic (and the predicted state within the time of the travel) in the city. In this paper we present a three layer architecture of a computer system capable of redirecting users of an urban road system via routes with a lighter traffic load in order to reach their declared destination in the city. A basic layer is a multiprocessor calculation server running Dijkstra path search tasks, the middle layer - the one which is visible to the road user - is a replicable proxy server that collects route requests from road users. The third layer is a mobile application. The prototype of such a system was developed by the ArsNumerica Group. The crucial feature of the system is feedback from road users that allows us to adjust the whole Intelligent Transportation System in the city to changes in traffic flow at various road links introduced by the redirection process applied to many users. The performance test strategy to prove the efficiency of the architecture was carried out for the city of Wrocław.
EN
Introduction and objective. The aim of the article is to present the consequences of traumatic brain injury in children, associated with general cognition and behavioural disorders, mainly of the anti-social type. Materials and method. A total of 20 school-age children took part in the study – 6 girls and 14 boys; average age of the children – 13.35 years (standard deviation SD = 1.95). The research instruments included an analysis of documentation, a structured clinical interview, MMSE and Frontal Behavioral Inventory (FBInv) with an additional set of 5 supplementary questions directed at the detection of anti-social behaviour. Results. The functioning of the children with traumatic brain injury is severely disrupted because of the presence of cognitive impairment; however, dementia was not manifested. In a significant number of the children with traumatic brain injury, not only frontal syndrome was found, but also the occurrence of anti-social behaviour. The most commonly reported behavioural problems were: disorganization, commonly referred to as laziness, hypersensitivity, and anxiety. The most common types of anti-social behaviours were: impulsivity, physical and verbal aggression, as well as outbursts of anger. Conclusions. The children with traumatic brain injury suffer from cognitive disorders and behavioural problems, especially impulsivity, physical and verbal aggression, increased anxiety, and disorganization. The occurrence of frontal syndrome is related to the development of anti-social behaviour.
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EN
Nowadays, in urbanized areas one of the most important matters is to determine a priori the time of driving from one zone of the city to another at various times of the day. The problem of travel time prediction is crucial in Intelligent Transportation Systems. The solution to this problem is a foundation of any route guidance system that will redirect drivers to their target destination via routes that have a lighter traffic load and thus higher travel velocity. In this paper is present a concept of a statistical methodology, developed by the ArsNumerica Group, that enables a quantity audit a travel time prediction algorithm. The methodology assumes that we are given database records of vehicles recognized by their unique identifier as well as duration times for which the messages with the predicted travel time are displayed VMS. the second aspect of ITS auditing considered in this paper is a placement of video cameras to measure vehicle stream velocity. Inappropriate camera location results in the fact that the stream velocity measured by them has a low usefulness for travel time prediction.
EN
In this paper, we present a case study, showing step by step, how to speed up Dijkstra’s method by parallelizing its computation and using different data structures. We compare basic algorithm with its bidirectional version and investigate two-and-multi-thread implementations based on Fibonacci heaps and regular priority queues. Experimental results obtained for artificially generated graphs as well as real-world road network data are presented and described.
EN
ITS systems have been deployed for last one and a half decades. Their aim is to increase a traffic flow rate in a road network of an urbanized area, to improve the comfort of driving as well as to decrease the pollution. A lot of commercial software are is available to simulate road traffic in urbanized areas. Some of them are suitable to perform traffic simulations of intelligent transportation systems via traffic modeling. A process of traffic modeling exploiting numerical simulations for an urban area where an ITS is deployed requires provision of digital maps, traffic demand amongst city zones, traffic signalization micro-programs being executed in the environment that can imitate a dynamic behavior of traffic lights at intersections in the ITS. In this paper an execution environment, developed by the Ars Numerica Group, that launches a road traffic micro-simulation is used to audit a performance of intersections’ signalization micro-programs on one of the main arteries in the city of Wrocław (Poland).
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Content available Green wave optimization
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EN
In this paper we present the results of global optimization of green wave parameters (offsets, opening times, speed limit) for the main artery in the city of Wrocław (Poland). The optimization process was performed in ArsNumerica Execution Environment [1] and involved two different objective functions: the average waiting time and the average queue length. Both approaches were compared by calculating the number of vehicles that pssed the artery in a prescribed time.
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Content available Detection of vehicles moving in wrong direction
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EN
In this paper we describe a method for detecting situations where a vehicle moves along a highway in the wrong direction. The first step of the algorithm is to build a pattern using Gaussian mixture model based on the optical flow calculated with the Lucas-Kanade method. The second stage concerns the detection of objects as a potential road hazard. The optical flow calculated on-line during the second stage is compared with the traffic pattern used in the first stage. Then the difference in movement direction is detected using predefined thresholds.
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