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Donciu et al.

Donciu et al. /Environmental Engineering and Management Journal 6 (2007), 6, 563-566 2. System architecture Fig. 2. CO, VOC, NOX variation depending of vehicle speed At the traffic rolling level there are the following constructive alternatives: • loop plates, similar to conventional inductive loops, meaning that they generate an electromagnetic field, troubled by a vehicle’s passing-by; • pressure plates, which upon the wheels passing-by discharge an electric current. This device is limited to the axles passing-by, not vehicles; • magnetometer, which measure changes in the Earth’s magnetic field upon a vehicle’s passingby. At the pavement level the following constructive alternatives are used: • magnetic inductive loops are the type of detector the most commonly used. They generate an electromagnetic field that is troubled upon the vehicle passing-by, and that detects the vehicle’s presence in this manner. The size of a loop is generally 1x1,5m; • magnetic probes measure changes in Earth’s magnetic field, in order to detect the vehicle’s passing-by over them. • sensitive cables. Upon the vehicle’s passingby over a sensitive cable, the tires produce compression of the piezoelectric cable, which generates instantly an electric signal. Hey cannot however measure some traffic parameters, and are limited to detection of the axle. Along the traffic ways, there are video cameras installed, set into crossroads with the main target of indicating any violation of the red signal in traffic lights (Krumm et al., 2000). They use a trigger for the beginning of the record which comes from a sensor set into the pavement. If the red signal appears at the traffic lights, and the pavement sensor detects a vehicle moving above it, the record is started (Puzicha et al., 1999). The above presented systems are used in low traffic crossroads, being able to interpret information only from the near proximity of the sensors, but unable to estimate the number of vehicles waiting in line on a traffic lane (Sista et al., 2000). This project approaches the build of an intelligent command video system for trafficlightening crossroads, its main goal being to diminish of traffic congestions, to improve vehicle speed, to reduce environment pollution and to improve negative effects of traffic congestion over the physic and psychic state of the population. It also brings up the possibility of extending this system to a macrotype that can also offer the benefit of a ‘green wave’ inter-synchronizing. The architecture of the suggested system is made on a hardware level from three different areas: the video sensor’s level (video cameras), the computer process level and the represented execution level and the traffic-lights that exist in the crossroad. (Fig. 3). Webcams have the role to import in real time images of the road traffic and can be placed depending on the urban configuration of the crossroad (if there are/are not trees/buildings higher than 12 m, slopes leaned more than 5 %) in two alternatives: AC – altitude camera with an overview of the crossroad, or CS – camera system set on the directions of the traffic lanes. In this last choice, one of the cameras must have an optic view transverse to the center of the crossroad, by mounting it in a diametric opposite point compared to the crossroad and monitor artery conjunction. Fig. 3. System architecture The process computer represents the hardware support of the algorithms and routines destined to 564

Urban traffic pollution reduction image processing and fuzzy control. The data transmission between the process computer, the video cameras and the execution elements is made by radio or cable, depending on the crossroad configuration. The software architecture of this system is made out of the main routine and a number of secondary routines of image processing and of the Fuzzy command. The fundamental routine has, as target, the determination of the vehicle number waiting in line on a traffic lane. For this, as you can notice in Fig. 3, the main IP image, that comes from the altitude camera AC or the one obtained by reconstructing the image (RIT) from the CS camera system, is submitted to a pre-processing process for removing the video sounds, followed by a numeric differentiation process from the background image IF and by applying a ‘high-pass’ filter to emphasize shapes. The processing operation is done by segmentation, obtaining the IS image. By segmenting we get to make partitions of the numeric image into sub-sets, by attributing individual pixels to these subsets (also named classes), resulting into distinctive objects from the scene, through the bridge method. And so, because of the significant differences between the grey levels of the object’s pixels and the background’s, the segmenting criteria are the grey level value. The pixel corresponding to the coordinate point (i,j) is considered to be an object-pixel if it’s value (i, j) is higher than a bridge value. Obtaining good results by this method depends of the way the bridge is chosen, which can be a value for a certain image that is given or a slim function that depends on the pixel’s positioning. The last stage of the fundamental routine is the N sequence of estimating the number of vehicles waiting in line and the build of the E (e1, e2, ……. en) vector, who’s elements are the numbers of vehicles standing in line on the road artery, and the index represents the number allocated to the road arteries (or to the traffic lanes, if on from way there are several directions that can be followed). The secondary routines of image processing gather the event memory EM routine, the algorithm for unfriendly weather conditions detection DCMN and the emergency situations detection algorithm SDU. The event memory routine uses a circular recording buffer with a stocking capacity of 48 h and its role is to provide witness video digital recordings for road-event like situations (road accident of robbery from vehicles in crossroads). The routine also fulfils the “running the red light” function. In the case of traffic violation by crossing over the red signal at the traffic-lighted crossroads a digital trigger is activated and an image of the vehicle is stored in a special location. The bad weather condition detection algorithm DCMN has as a goal establishing the visibility conditions in a crossroad and the road adherence by identification night/day, the existence of rain/snow weather phenomenon’s and deposition level on the road. The going-out value of this algorithm through the BI delaying block applies corrections depending on weather conditions and the times provided by BCF. The DSU - emergency situations detection algorithm’s goal is to establish the approach to the crossroad of a light-signaled vehicle – ambulance, police car, firemen’s cars or official cars - followed by priority on the identified lane given by the Fuzzy BCF command block. The Fuzzy BCF command block is the central part of the whole traffic-lightening system and has the role to establish, upon the data send by the fundamental and secondary routines, the command times of the trafficlights. On an overview look, by its architecture, this project represents a high novelty solution with also high complexity in crossroads traffic-lightening, taking into consideration that such a configuration is not yet implemented. Development of the algorithms necessary to this crossroads traffic-lightening intelligent video system requires a interdisciplinary and complex approach of this matter, by putting together knowledge from domains such as: numeric view of the signals, Fuzzy artificial intelligence and data transmission. In particular, the project is distinctive by the following novelty aspects: • the secondary routines for image processing (the memory routine EM, the bad weather condition detection algorithm DCMN and the emergency situations detection algorithm SDU) give to this system a high level in command decisions making and furthermore is gives adaptability to trafficlightening depending on weather conditions and emergency situations; • the development of a new FFT image processing algorithm, with a processing speed higher than the classical alternative and with frequency, amplitude and phase errors substantially reduced, necessary to the co-existence of the two different processing types: the fundamental routine and detection algorithm of the bad weather condition detection by using an overview level processing, while the emergency situations detection algorithm uses an identification processing on a detail level; • the making of an adjustable traffic-lightening to the necessities of the traffic type and conditions. 3. Conclusions Development of proposed system has a large impact under economical, health and environment aspect. Under economical aspect, taking into consideration the overwhelming growth of the vehicle number in the past decades, the existence of an intelligent traffic-lighted crossroad system has as an immediate result a better traffic flow efficiency, this being emphasized under reduction of the medium time travel on a certain itinerary. On another side, the fuel burnings due to repeated stops/slowing-downs and accelerations is reduced, with significant values on medium and long time length. 565

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