Developing the fabricated system of automatic vehicle identification using RFID based poultry traceability system

The Automatic Vehicle Identification (AVI) system was recently installed in expressway networks in Japan. License plate numbers of passing vehicles are monitored through roadside AVI cameras and then recognized. This paper shows the formulation of origin and destination (OD) matrices estimation model using the observed data with the AVI system. The results of license plate matching between a pair of AVI cameras are involved as the input variables. The formulated model is a least squares model and yields to the linear transformation of the partly observed OD matrices. The model is applied to the Kobe corridor line in the Han-Shin expressway network. It is found that the estimated OD matrix is consistent with the one using the previous mail survey. The proposed estimation method is expected to investigate the day-to-day fluctuations of OD patterns in the expressway network.

Electronic Toll Collection (ETC) system is an important part of modern intelligent transport system, which plays an important role in intelligent car parks and congestion charging. Electronic Toll Collection (ETC) system includes automatic vehicle identification system and electronic billing system. The automatic vehicle identification system includes three parts: automatic vehicle identification (AVI), automatic vehicle model identification (AVC) and inspection system (ES).The application of ETC in intelligent car parks and congestion charging also includes automatic vehicle identification system (automatic vehicle identification, automatic vehicle model identification and inspection system) and electronic billing system. In intelligent car parks, ETC technology achieves fast and accurate automatic toll payment by automatically identifying the ETC tags carried by vehicles, which greatly improves the efficiency of car parks and reduces the queuing and waiting time due to manual toll collection. In terms of congestion charging, ETC technology provides an effective means to implement congestion charge collection. By setting up ETC toll lanes in core urban areas or busy road sections, the system can automatically record the information of the vehicles passing through and carry out toll settlement, thus realising dynamic toll collection according to the degree of congestion and the frequency of vehicle use. This congestion charging strategy aims to regulate urban traffic flow through economic means, alleviate traffic congestion and promote the sustainable development of urban transport. Although China's theoretical research on congestion charging, has been relatively mature, but affected by a variety of factors, no city has yet to start the implementation of congestion charging, the lack of experience in practice. Singapore will not stop the car charging system (ETC) technology successfully applied to urban congestion charging and intelligent car parks, ETC system in intelligent car parks and congestion charging application, help to promote the development of urban transport to a more intelligent, green direction. China can learn from the successful experience of foreign countries and apply it to its own intelligent car parks and congestion charging. This paper introduces the basic principle of ETC, discusses the research progress of ETC in terms of technical characteristics, and analyses the application scenarios of ETC in intelligent car parks and congestion charging. By examining the opportunities offered by Electronic Toll Collection (ETC) systems in smart car parks and congestion pricing, this study aims to delve into the transformative impacts of Electronic Toll Collection (ETC) systems on intelligent mobility and contribute to the sustainable development of intelligent mobility.

A system for identifying moving objects such as vehicles, trucks, buses, containers, etc., in which a passive label having a number of dipoles in accordance with a code is attached to the object, is described. A synthetic aperture radar consisting of a Gunn diode mounted within a resonant cavity and connected to a horn is used to illuminate the label. The Gunn diode operates as a homodyne so that signals from the label are also detected by the diode as a number of overlapping chirp signals, which are sent to a computer for processing. The computer output is a pulse train with pulses corresponding to dipoles in the label. A number of tests have been made indicating that labels can be read regardless of range and vehicle speed. A number of horns may be strategically located in a city or along a highway, and these horns can be multiplexed into a common computer to provide an inexpensive all weather automatic vehicle identification system.

Real Time Number Plate Recognition System is an image processing technology which uses number (license) plate to identify the vehicle. The objective is to design an efficient automatic authorized vehicle identification system by using the vehicle number plate. No plate help for movement of traffic securely without any collision. They can reduce the number of accidents on roads like pedestrian accident and right-angle collision of two cars. Number plate recognition (NPR) can be used in various fields such as vehicle tracking, traffic monitoring, automatic payment of tolls on highways or bridges, surveillance systems, tolls collection points, and parking management systems. The developed system first detects the vehicle and then captures the vehicle image. Vehicle number plate region is localized using Neural Network(rcnn) then image segmentation is done on the image. Character recognition technique is used for the character extraction from the plate. The system is implemented and simulated in python, and its performance is tested on real image. Automatic vehicle detection and recognition is a key technique in most of traffic related applications and is an active research topic in the image processing domain. Different methods, techniques and algorithms have been developed for vehicle detection and recognitions but they are not very useful for parking system. In this project we aim to make an application which will help for society and mostly for corporation buldings in each state for doing their work very efficiently and in very small time. Live camera feeds are the primary input we give the system. On the main gate is a camera. When a car or bike passes in front of the camera, the camera reads the number plate number. Using an OCR algorithm, this vehicle can be determined to be authorized or not. If the car is not allowed in the premises the notification will be activated. authorized popup will be on if the vehicle is authorized

Due to a world wide surge in the intelligent vehicle highway system (IVHS), the field of automatic vehicle identification (AVI) has been rapidly growing. The use of spread spectrum communications has not yet pervaded the AVI market. However, in the already cramped electromagnetic environment, spread spectrum techniques have gained significant popularity in many growing areas of wireless communication. AVI is perfectly suited to benefit. The fundamental advantage of spread spectrum technology in AVI applications is its invulnerability to interference and its ability to operate alongside narrow band communication systems already in place. The second advantage is the ease of licensing for spread spectrum AVI, since it does not require a dedicated frequency spectrum. It is also possible for several spread spectrum AVI systems to operate simultaneously in the vicinity of each other, using different spreading codes. This article concentrates on architecture as well as applied key signal processing techniques for a proposed direct sequence spread spectrum (DSSS) AVI system. A functional block diagram that outlines the major components of the system is presented. The system consists of three major functional parts: interrogator, transponder, and trigger. Free flow of traffic can be maintained only if the interrogator can isolate each vehicle in a common interrogation zone. DSSS allows this through code division multiple access (CDMA). The triggering system is based on the principles of speed enforcement radar. The proposed digital modulation scheme is BPSK. The DSSS modulator applies a Gold pseudo-random (PN) code to spread the spectrum of the binary signal. Despreading is carried out through the use of a PN matched filter. The antenna is assumed to have a 60/spl deg/ half-power radiation field and is placed on an existing overpass approximately 6 meters above the roadway. With 50 mW antenna transmit power, link budget analysis indicates that sufficient bit energy-to-noise power spectral density ratio is available at the receiver to generate an excellent bit error rate without requiring error control coding.

In recent years, automatic vehicle identification (AVI) systems have rapidly developed in many countries, which provides an excellent opportunity to understand drivers’ mobility patterns in urban road networks. Few efforts have been devoted to fully utilizing the AVI data to address the prediction issue in behaviors modeling. This paper proposes a deep learning (DL) framework driven by AVI data to model drivers’ behaviors and further incorporate travel time prediction in the next location prediction problem. Specifically, DeepWalk encoder and DeepWalk + Time long short-term memory (DT-LSTM) were proposed to capture the spatial and temporal correlations simultaneously. By learning the spatial relationship between the sensors from the historical trajectories, the DeepWalk encoder converted the sensors into low-dimensional numerical vectors essential for DL. As a vector form, the spatial relationship of the sensor can be adequately measured. Besides, a new long short-term memory (LSTM) variant, DT-LSTM, was designed to memorize drivers’ short-term and long-term interests, respectively. By adding additional time gates, DT-LSTM facilities the location and travel time consistency in the trajectories sequences with different time intervals. Experimental results demonstrated the effectiveness of the model, which achieved the state-of-the-art performance (SOTA) compared with other benchmark methods, with the Acc-1 of 82%, Acc-5 of 95%, and MAPE of 17%.

Input-output cumulative count curves are a powerful tool to forecast freeway travel times in the very short term, as they rely on the predictive information given by the vehicles' accumulation in the target section. Therefore, they could represent a very appealing method to feed real-time traffic information systems. However, traffic detectors' count drift implies very poor accuracy in the estimation of the vehicles' accumulation, leading to completely unreliable travel time predictions. A drift correction method is necessary. In contrast, several technologies allow accurate direct travel time measurements, like automatic vehicle identification (AVI) or vehicle tracking systems. In all cases, direct travel time measurements are obtained once the vehicle has crossed the target section. This means that the information is representative of the near past traffic conditions, while the objective of real-time information systems is to transmit information about traffic conditions in the near future. In this context, the present paper aims to fuse the information provided by input-output diagrams, obtained from loop detectors, with AVI direct travel time measurements. This fusion allows exploiting the accuracy of the direct measurements to correct the count drift in loop detectors. Then, corrected input-output curves can be used to obtain reliable short-term predictions of travel times. The proposed data fusion method has been applied to a test site in the AP7 freeway near Barcelona, obtaining significantly better results than with the common practices of simply disseminating direct measurements or using spot-speed methods.

Queue length is more likely to be affected by inefficient departure behaviour near the stop line at an intersection because of the prohibition of lane changes. The inefficient departure behaviour and its influence on the evolution of queue discharging in specific lanes have significant research value. In the past few years, the deployment of automatic vehicle identification (AVI) systems has made it possible to acquire information on vehicles’ passing through an intersection. Researchers can obtain individual driving information more accurately and effectively based on an analysis of AVI data. These high‐resolution data have enabled the reconstruction and quantitative analysis of the influence of microscopic traffic behaviour. This study aims to model the departure of vehicle platoon and predict the queue length at isolated intersections. They propose a multi‐layer gated recurrent unit (GRU) network to understand the mechanism of queue length evolution. A case study of vehicle departure pattern and queue length prediction is presented with AVI data obtained from an isolated intersection in Xuancheng, Anhui province, China for the first quarter of 2018. The results of the case study indicate that their work has good application prospects. The proposed multi‐layer GRU network has potential to guide the signal scheme optimisation.

Forecasting Freeway Link Travel Times with a Multilayer Feedforward Neural Network

Recently, automatic vehicle identification system has great significance in practical life and with modern technology now widely available, the research on automatic vehicle recognition based on computer vision is becoming increasingly thorough and extensive. However, automatic vehicle identification also faces significant challenges, such as changes in lighting, weather conditions and distortion of captured images in natural road scenes. Since 2012, as a deep learning in image classification, the convolution neural network has become a major subject in many fields, so this study would focus on the vehicle identification in terms of the convolutional neural network. The Cars196 data set and an improved Convolutional Neural Network (CNN) network capable of multi-attribute recognition are applied to study the optimization network problem of the convolutional neutral network and to realize the multi-attribute classification and identification of automobile brands, models and colors. The data analysis shows that the improved CNN recognition network has a recognition accuracy in automatic vehicle recognition, but the overall recognition network is affected by many factors. According to the results of data analysis, in practice, alongside the improvement of traditional convolutional neural network, the improved new neural network structure should also be used for vehicle recognition, so as to improve the accuracy of vehicle identification to better serve the community.

This paper presents the design and analysis of general packet radio service (GPRS)-wireless local area network (WLAN) mobility gateway (GWMG) for the integration of GPRS and WLANs. The interworking between GPRS and WLANs is achieved by the GWMG that resides on the border of GPRS and WLAN systems. The design goal is to minimize the modifications in GPRS and WLANs as both systems are already widely available. By deploying the GWMG, users can seamlessly roam among the two systems. Unlike other related work, the proposed GWMG could be used when either the GPRS or WLAN is a user's home network. Both mathematical analysis and simulation are developed to analyze the performance. The proposed GWMG has also been implemented in a testbed comprising a commercial GPRS system. The results show that the GWMG could achieve a design goal that will effectively integrate GPRS and WLANs.

General packet radio service (GPRS) is a part of the evolution path towards 3G. This paper investigates the effect of using two representative scenarios: a macro cellular system operating from a high altitude platform line of sight system and a terrestrial non-line-of-sight system, in each case a packet-based such as GPRS system is considered. The macro cellular layer will serve as second server to the mobile data terminals located in a microcellular layer, both layers form a Multi-layer cellular system. A comparison between GPRS system using only microcellular structure and when using both microcellular and macro cellular structures is evaluated. A simulator model was developed to study the behavior of data transmission between a GPRS server and data terminals via air interface. Simulation results show the validity of using macro cells to serve as a second layer, and how this can enhance the GPRS system performance.

The General Packet Radio Service (GPRS) is being deployed by GSM network operators world-wide, and promises to offer users always-on data access at bandwidths comparable to that of conventional fixed-line telephone modems. Unfortunately, many users have found the reality to be rather different, experiencing very disappointing performance when, for example, browsing the web over GPRS.In this paper we investigate what causes the HTTP protocol and its underlying transport TCP to underperform in a GPRS environment. We examine why certain GPRS network characteristics interact badly with TCP to yield problems such as: link under-utilization for short-lived flows, excess queueing for long-lived flows, ACK compression, poor loss recovery, and gross unfairness between competing flows. We also show that many web browsers tend to be overly aggressive, and by opening too many simultaneous TCP connections can aggravate matters.We present the design and implementation of GPRSWeb - a mobile HTTP proxy system that mitigates many of the performance problems with a simple software update to a GPRS mobile device. The update is a 'client proxy' that sits in the mobile device, and communicates with a 'server proxy' located at the other end of the GPRS link close to the wired-wireless border. The dual proxy architecture collectively implements a number of key enhancements - an aggressive caching scheme that employs content-based hash keying to improve hit rates for dynamic content, a preemptive push of web page support resources to mobile clients, resource adaptation to suit client capabilities, delta encoded data transfers, DNS lookup migration, and a UDP-based reliable transport protocol that is specifically optimized for use over GPRS. We show that these enhancements result in significant improvement in overall WWW performance over GPRS.

General packet radio service (GPRS) is a packet oriented mobile data service on the 2G and 3G cellular communication systems global system for mobile communications (GSM). GPRS is the most widely used technology solutions to solve mobile communication combined with IP. Due to its advantage such as real-time online, binding billing, quick login, high-speed transmission, widespread coverage etc, it has been widely used in the remote monitoring and data transmission fields, etc. In this paper the PPP protocol of GPRS was researched in detail, moreover, the PPP protocol process based on GPRS was analyzed. In addition, it describes the composition and content, meaning and connection of the PPP protocol process and data exchange between terminal and monitoring center. Meanwhile, the TCP/ IP transmission protocol was discussed. it also gives three handshake data exchange situation which is connected by TCP/IP. It gives the specific application example of PPP and TCP/IP protocol exchanging data when connecting public wireless internet net with GPRS module. This article provides the essential basis for making further use of PPP and TCP/IP protocol to achieve the design of wireless transmission terminal.

Identification of players in image or videos in the sporting events, and automatic vehicle identification by number plate during its moving state is an approachable but tough task. In NBA basketball games, where pictures can be taken by using multiple cameras, by characterizing the jersey colour with MAP detection, isolating the jerseys with the player numbers, and using template matching. A max-effort method is used to locate the majority of game’s participants. Similarly in automatic vehicle identification by licence plate is quite useful in highway electronic toll collection, border, customs checkpoints, and red-light violations etc. It has two techniques like edge detection method and image processing tools. Additionally, there is another technique that has three main components: extraction of the plate region, character segmentation, and plate character identification. We are discussing all these methods along with their working and how these are providing outcomes for the recognition and tracking of the jersey number and number plate recognition.