The formula (8) shows that the instantaneous driving speed v 10 and v 20 of vehicle 1 and vehicle 2 are all non-linear multivariate functions with 8 input quantities. In general, the uncertainty of the vehicle quality parameters [6,7] and the geometric parameters of the accident evidence on the scene such as braking (or taxiing) distance, speed direction angle, etc., are relatively small [8,9], which can be regarded as small uncertainty to be ignored, however, the uncertainty of the parameters, such as the coefficient of adhesion, which needs to be measured and selected are large enough to be set as the uncertainty factor, and according to its value range, the corresponding range and uncertainty of the accident speed can be calculated. This characteristic of uncertainty evaluation can simplify the speed identification of vehicles involved in the complicated road traffic accident.
This project is mainly designed to be executed in the safe zones like schools, hospitals, etc. Here the speed limit is set in the RF tag being placed in the safe zones and the vehicles speed is checked with the speed limit. If the speed of vehicle is greater, a buzzer is being produced to indicate the user that he is above his speed limit. Even after the indication of the buzzer alarms, if the driver does not reduce his speed then a message, which contains the information about the vehicle will be send to the nearby authorities with the help of GSM. Then the vehicles speed will be gradually reduced and the vehicle will be stopped automatically by the use of Arduino.
According to the above literatures, current research methods of driving behavior analysis include the driving data collection, driving modeling algorithms, and applications. Driving data collection includes automotive video capture, car-mounted sensors, and the on board diagnostic (OBD). In terms of driving behavior modeling algorithms, there are HMM, support vector machine (SVM), decision trees, and other principles. The main application of the driving behavior analysis is the identification of driving lethargy or the driver's actions forecast. In this paper, a novel driving behavior analysis method based on the vehicle OBD and AdaBoost algorithms is proposed. This proposed method collects the vehicle operation information, including vehicle speed, engine RPM, throttle position, and calculated engine load,
Abstract— The paper proposes a multi agent system for the urban undisciplined traffic. The paper explains two systems: one is Intelligent Traffic Light Controller System and other is Intelligent Traffic Deviation System. The light control system measures the traffic parameters such as vehicle counting, vehicle classification, vehicle speed and vehicle length to reduce the traffic queue size. The traffic deviation system helps in deviating the vehicles before the traffic congestion occurs. This framework makes use of intelligent vehicle measurement and traffic light control system which serves as the input for traffic deviation system. Euler’s path approach is used for converting any traditional city map to planar graph for route pattern identification. Validation is done using the Nagle- Schreckenberg model. The performance of the system when compared with the existing systems is found to be satisfactory in terms of cost, time, maintenance, expense and performance.
The project is proposed to develop vehicle identification system by using radio Frequency, RF. The RF consists of one RF transmitter and one RF receiver. The data transfer speed varies based on the frequency of receiver and transmitter. Make sure the transmitter and receiver are both for the same frequency for an application .
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Abstract: Wireless sensor network refers to a group of spatially dispersed and dedicated sensors for monitoring and recording the physical conditions of the environment and organizing the collected data at a central location. The current abnormal wireless sensor network vehicle load data recognition method is more complex, which leads to low recognition rate, false alarm rate and slow recognition speed. Based on the genetic algorithm, the accurate method for abnormal wireless sensor network vehicle load data recognition is proposed. The effective feature set of abnormal vehicle load data in the wireless sensor network is constructed, to remove irrelevant features and redundant features from existing abnormal wireless sensor network vehicle load data. The abnormal wireless sensor network vehicle load data in the effective feature set are coded, to reduce the recognition time of abnormal wireless sensor network vehicle load data. The adaptive fitness function, crossover operator and mutation operator are applied to genetic algorithm, which can improve the recognition rate, reduce the false alarm rate, and realize the recognition of abnormal vehicle load data wireless sensor network. The experimental results show that the recognition rate of this method is high, the false alarm rate is low, and the time of recognition is less.
Another test is conducted when the vehicle speed is increased using ramp function with slope 1 km/h and the torque is a random signal. The power consumption using GA-PID has shown an average reduction of 75% compared to PID as shown in Fig 8. When a rapid driver’s torque is applied with increasing vehicle speed the power consumption increased significantly with PID controller. However further analysis has to be done to illustrate the power consumption such as in parking situation where the vehicle speed is zero and a rapid changes of driver’s torque. This initial investigation is merely to justify and to make assessment the potential of other controller algorithm for EPAS system.
Many a times the warning sign on the road sides becomes difficult to watch for the drivers and the driver may sometimes miss the warning notes. These warning notes may be speed breaker ahead or narrow bridge or even accident zone etc. This becomes tedious during rainy seasons and at nights. Sometimes the boards may be so dirty and the driver may not read anything and even if he tries to read it with a wide eye there is a chance for the drive to lose concentration on the road. For this a portable unit is placed inside the vehicles and it communicates with the roadside systems at all times. So the position of each vehicle can be very easily known by the roadside system and also the system has all the details of roads and emergency station such as hospitals stored in it. So whenever the information is needed such information is automatically passed on to the vehicle from roadside system and the driver in the vehicle can get a clear idea of the contents received. The microcontroller used is a high Speed PIC microcontroller for fault free operation. It has a variety of advantages over conventional old microcontrollers such as RISC architecture, larger memory word, fast operation and a host of inbuilt features such as ADC, communication protocols etc.,. A GPS system is placed along with the microcontroller. The GPS system gives the exact position of the vehicle by giving out its latitude and longitude. The microcontroller at all times receives the information and displays the information using the dedicated LCD display. Further the same is used to announce to the driver about the hurdles such as speed breakers. This voice alerting system helps the drivers to concentrate on the road without even worrying about the sign boards near the road.
Vehicle speed is an important parameter that finds tremendous application in traffic control identifying over speed vehicles with a view to reducing acci- dents. Many methods, such as using RADAR and LIDAR sensors have been proposed. However, these are expensive, and their accuracy is not quite satis- factory. In this paper, a video-based vehicle speed determination method is presented. The method shows satisfactory performance on standard data sets and gives that error rate of velocity estimation is within 10%.
Over the decades most of the road accidents are due to over speeding of vehicles. The road accidents are increasing year after year due to more number of vehicles. The youthfulness in drivers makes them to drive the vehicle very rashly, which is the craze of every driver.Overspeeding in accident zones (like school areas, hospitals and crowded areas etc.) and also the speed humps are major cause for the accidents. In addition to that drivers often can’t recognize the appearance of unmarked speed
The correct timing of displaying the application information to the driver depends on the geographic location of the vehicle relative to the intersection. Pinpointing the geographic area includes targeting the roads as well defining the minimum and maximum distance to the intersection. Determining this ZOI should be aligned with the traffic objectives as mentioned in the introduction of this chapter, and is more devoted to the area of traffic engineering. Nevertheless, the expressed ZOI could have a major impact on the communication requirements. For example, in case the defined geographic area exceeds the transmission range of the RSU the communication architecture should include multi-hop technology. In the list of standardized applications and requirements in  nothing is mentioned about the geo- graphic location at which vehicles should be informed. In  the writers claim the longer the distance between the traffic light and the vehicle is, the more efficient their application will be. However they use a information range of 500 m in their field tests, which happens to be exactly the transmission range of their used wireless technology. In  it is claimed that there exists some point at a distance around 1000 m away from the intersection, that a speed adjustment is not of influence on the emission any more. Although the writers argue that such a point exists and do not provide an exact distance, we adopt the information range under the assumption that this is the saturation point. For the minimum distance we adopt the assumption of  which states a driver decides to slow down to zero if it approaches a red light and no communication is used at close to 100 m.
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A vehicle identification system includes a transmitter, receiver and a database management system. The transmitter can be placed on any part of a vehicle. The system will read data from tag. The tag were contains identification number for the vehicle. Receiver for the tag is a reader which will be placed in the system. This system also required a database management system to manage and display the data that is associated with identification number.
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Traffic flows through signalized intersections are filtered by the signal system (stopping of vehicles during red time) causing vehicular delays. Vehicular delays at signalized intersections will increase the total travel time through an urban road network and ultimately results in the reduction of speed, reliability and cost-effectiveness of the transportation system. The uncontrolled and ill planned growth of urban centers has resulted in a number of problems like traffic congestion, shortage of water and electricity, deteriorating environment and public health. The growing cities have generated the high levels of demand for travel by motor vehicles in the cities. The automobile population in India has increased from a mere 0.3 million in 1951 to more than 45 million in 2001. The registered two wheelers constitute nearly seventy percent of the vehicle population in almost all the cities. More than 90 per cent of the automobiles are located in urban centers. By considering the above factors traffic signals has to be designed in such a way that the flow is organized with less amount of delays.
vehicles through it. The problem of location involves lot of preprocessing activities like, normalization, skew detection and correction and segmentation. it is required to carry out preprocessing activities such as noise removal, edge detection, is done on the recorded video. Any standard OCR can be used at later stage to identify the text. Since the domain of the characters is very limited in the text of vehicle number, high recognition rate can be expected in the OCRs. Segmented characters are to be recognized. It was decided to use an algorithm, which must be as simple as possible, since the types of characters that appear on the number plates are limited.
The principle behind the unprecedented navigational capabilities of GPS is triangulation. To triangulate, a GPS receiver precisely measures the time it takes for a satellite signal to make its brief journey to Earth - less than a tenth of a second. Then it multiplies that time by the speed of a radio wave—300,000 km (186,000 miles) per second—to obtain the corresponding distance between it and the satellite. This puts the receiver somewhere on the surface of an imaginary sphere with a radius equal to its distance from the satellite. When signals from three other satellites are similarly processed, the receiver’s built-in computer calculates the point at which all four spheres intersect, effectively determining the user’s current longitude, latitude, and altitude. (In theory, three satellites would normally provide an unambiguous three-dimensional fix, but in practice at least four are used to offset inaccuracy in the receiver’s clock.) In addition, the receiver calculates current velocity (speed and direction) by measuring the instantaneous Doppler Effect shifts created by the combined motion of the same four satellites. Although the travel time of a satellite signal to Earth is only a fraction of a second, much can happen to it in that interval. For example, electrically charged particles in the ionosphere and density variations in the troposphere may act to slow and distort satellite signals. These influences can translate into positional errors for GPS users -a problem that can be compounded by timing errors in GPS receiver clocks. Further errors may be introduced by relativistic time dilations, a phenomenon in which a satellite’s clock and a receiver’s clock, located in different gravitational fields and traveling at different velocities, tick at different rates. Finally, the single greatest source of error to users of the Navstar system is the lower accuracy of the civilian C/A-code pulse. However, various augmentation methods exist for improving the accuracy of both the military and the civilian systems.
crossing warning signs, which are installed in accordance with local standards and specifications. Apart from these, speed control devices were also introduced to reduce traffic speed through the installation of yellow tranverse bars. However, they were found to be not effective in reducing speeds, especially during the peak school periods. It should also be noted that the road along the study sites has been identified as an accident blackspot by the Ministry of Transportation (Mustakim and Fujita, 2011). Since the speeds observed during school hours were statistically indifferent during off-school hours, therefore the risk of accidents in school zones is high at all times.
compared with the reference material PICA, appears to be inappropriate for a GEO initial altitude. Using the RDCP, the sole case with the highest M S is feasible, but still not convenient. Conversely, using the AVCOAT increases the feasibility compared with RDCP but limits convenience compared with PICA. Thus, the relatively low density of the PICA is a dis- criminating element in the selection of the ablative material. Actually, these results are conservative be- cause of the assumption that the vehicle is covered with a TPS with a uniform initial thickness.
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The CA MUTCD standard and Caltrans standards and specifications for traffic control devices shall not be applicable to privately owned and maintained roads or commercial establishments, unless the particular city or county enacts an ordinance or resolution to this effect (Fiske, Surabian, & Weigold, 2010).Private roads are roads separated from access by the general public by physical barriers and do not have to meet the speed zone criteria of this manual (Abraham, 2001).
The adaptive processes of beaconing interval of node joining in group and nodes in the group are shown in Figure 5. For joining in node B, the speed decreases to be same with the speed of the group. The neighbor change rate of node B varies greatly at first, and then varies slowly until keeps unchanged. At the same time, the self-decision-making ability changes from strong to weak. When the absolute ability is less than 0.1, the beaconing interval of node B will keep unchanged. For nodes in the group, few nodes joining in cannot change the neighbor change rate greatly. That means the absolute self-decision-making ability of nodes in the group is less than 0.1. The beaconing interval of nodes in the group will keep unchanged. It will change unless there are enough number nodes joining in the group or the number of nodes in the group is small. But it can gradual change to be a stable value.
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The main aim of this system is to detect rash driving on highways using IoT which is cheaper and easy to carry and install. As a number of accidents on Indian highways increase day by day so it is necessary to monitor the speed of the vehicles passing on highways so as to reduce the accident cases. It also reduces the difficulties of traffic police department and makes them easy to control the rash driving on highways without human intervention. This concept can be enhanced in the future by integrating a camera with the system which could capture the image of the number plate of the vehicle to sends that to the traffic authorities.