A Traffic Information Detection Method at Single Intersection Based on Wi-Fi Data

This paper presents a study on a stand-alone image tracking system for automatic traffic monitoring. The proposed image tracker consists of three parts: an edge detection module, an image tracking module and a traffic monitoring module. The edge-detection module is a special designed circuit board, which features fast CCD image processing and feature extraction. Frame rate (60 Hz) edge detection of an image with resolution of 320/spl times/240 pixels is obtained on-board. The image tracking module performs the vehicle tracking in real time. Adopting active contour models and Kalman filtering techniques, we successfully achieved real-time image tracking of multi-lane moving vehicles. The traffic-monitoring module determines the traffic information from the tracked locations of vehicles. The current system provides three types of traffic information: the velocity of multi-lane vehicles, the number of vehicles and car accident detection. Experimental results are presented to demonstrate the real-time tracking of vehicles on an urban artery.

The paper details the design of a data acquisition system for evaluating blade and shaft designs of a propulsion system for a human powered submarine. The objectives of the design were to sample and store data representing several analog signals and the time of measurement, and to display velocity and shaft rpm for the submarine operator in real-time. The sampled parameters are then used to calculate the submarine's performance in terms of velocity, thrust shaft torque and rpm. Several parameters are displayed in real-time to aid the submarine operator with training. System design restrictions including size, power requirements, heat dissipation, data integrity, and others had to be considered. Since the data acquisition system is a completely portable system, if is possible to incorporate the system into other applications. >

Traffic flow detection is an important part of intelligent transportation system and it has a wide range of applications. We analyse the existing methods of traffic flow detection and propose a traffic flow detection method which based on vertical virtual road induction line (VVRIL). Firstly, according to the direction of the vehicle travelling, we set a VVRIL in the middle of the driveway. Secondly, the background image is gained from the video image with Gauss mixture model. We then make differential operation between the background image and video image to get a binary image, which we set the values of the foreground pixels as 1 and that of background pixels as 0. Thirdly, we extract the values of the pixels in the VVRIL of the binary image. Besides, we regard the vehicle maximum length obtained by self-learning as the length of the detection zone and get the information of vehicles in the VVRIL. Finally, we get the number of vehicles through the analysis of vehicle centre coordinates in the VVRIL of each video image. Experimental and theoretical analyses show that the method is accurate enough to meet the requirement of real-time performance.

CRRFNet: An adaptive traffic object detection method based on camera and radar radio frequency fusion

<div class="section abstract"><div class="htmlview paragraph">As modern society develops rapidly, people’s requests for traffic convenience and traffic safety become greater and greater, and it is essential to eliminate traffic congestion and traffic accident to sustainable development of urban areas. Therefore, this paper brings forward novel solution based on hybrid sensor networks to observe the status of traffic in road networks in order to alleviate traffic jam and prevent traffic accident. With the collection of precise traffic flow information at the time, it realizes traffic flow control at crossroads, gives warning in advance with the congestion or accident. We carried out a bunch of simulation experiments in succession, the main discoveries are as follows. a. The energy consumption is great reduced under the sensor deployment rate between 1:50–1:60 (sensor : vehicles). b.The sampling rates can keep a very high level of precise and efficiency under the appropriate range between 1:50–1:60 (sensor : vehicles).The critical segments of roadways are fitted with the radar sensors to accomplish not only reliable surveillance of traffic congestions but also timeearlies warning of traffic accidents as opposed to relying on the single-sensor network. As reflected in Fig. 17, the heterogeneous sensor network is more robust against sensor errors because of the complementarity effects without relying on individual sensors. The experimental results highlight the potential for hybrid sensing architecture for intelligent transportation systems(ITS) and provides a well-technical basis to alleviate urban traffic jam and improve transportation efficiency.</div></div>

Data Acquisition System (DAS) is a fundamental functional part in every radar application, especially when used for high range resolution radar system. This paper presents a high speed and reliable DAS of a High range Resolution Radar used for Acquiring Traffic flow Information (HRRATI). The system uses high performance Field Programmable Gate Array (FPGA) to cope with the data transformed by the high speed 8-bits Analog-to-Digital Converter (ADC08D500), which performs digitization of the dual channels radar echo signals with sampling rate at 500MHz. The signal bandwidth up to 180MHz in each channel, then the system preprocesses all the data onboard in real time. In view of the broad bandwidth of the signal and high sampling rate, clock jitter, signal integrity and EMI/EMC issues assume great importance and pose a great challenge to the Printed Circuit Board (PCB) design. This paper gives a thorough investigation of such problems. Finally, clock jitter and ENOB test experiment results show that the DAS is capable of sampling the radar signal effectively.

This paper designs a detection method for real-time acquisition of traffic intersection parameters based on image processing. Firstly, two virtual coils were drawn at the beginning and the end on the acquired video stream image. Next, the presence of the vehicle was judged based on the change of the gray value in the two virtual coils. The background difference algorithm was adopted to detect and obtain various traffic parameters. The experimental video samples were collected in the actual road environment. Our algorithm relies on the first and last two coils for information processing, and completes vehicle detection, speed detection, and important parameter information acquisition of body color. Experiments show that, compared with the traditional machine vision-based traffic information detection algorithm, our traffic parameter detection method, which is based on double virtual coils, has high detection accuracy, and can detect more parameter information, while satisfying real-time performance.

Early traffic event detection is essential for transportation networks’ quick response and accurate performance recovery. Social media data (e.g., Twitter data) can be a valuable source for detecting and describing traffic events, such as accidents, congestion, and road closures. Integrating spatial and temporal features inherent in Twitter can enhance models by revealing non-trivial information, patterns, and knowledge, resulting in improved performance. Recent research in Deep Learning (DL) has revealed the strength of learning features directly from data to extract potential hidden features that efficiently infer human activities and interactions and detect the relationships to generate fine-grained information. This research explores the efficiency of integrating Twitter data’s spatial, temporal, and semantic features for traffic event detection using DL. A novel framework employing a transformer-based multi-feature fusion approach is proposed, designed to detect traffic incidents comprehensively via Twitter data. The framework classifies tweets based on multiple dimensions: (1) semantic content is numerically represented and categorized traffic incidents, non-traffic, or traffic conditions and information; (2) spatial characteristics are analyzed through hot-spot analysis techniques, classifying locations into hot or cold spots; and (3) temporal attributes (date and time) are visualized and analyzed through heat maps reflecting incident densities. The performance of the models was then evaluated based on various fusion scenarios combining spatial, temporal, and semantic data using performance metrics such as F-score and accuracy. The results showed that the scenario of transformer-based multi-feature fusion of spatial, temporal, and semantic data for traffic event detection yielded better results. The model achieved a 7.93% accuracy improvement when distinguishing between the two classes, “Traffic Incident” and “Non-Traffic”, and a 6.09% increase in accuracy when classifying across three categories: “Traffic Incident,” “Non-Traffic,” and “Traffic Conditions and information.” These findings highlight the effectiveness of using a multifeatured Twitter dataset for improved detection accuracy.

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

This paper introduces a bio-inspired GAORI robot with renewable energy source and presents its command and data acquisition system for remote commanding as well as data forwarding/backup/relay. The hardware architecture design and software implementation of GAORI command and data acquisition system are presented in this paper.

Design of on-line embedded web server is a challenging part of many embedded and real time data acquisition and control system applications. The World Wide Web is a global system of interconnected computer networks that use the standard Internet Protocol Suite (TCP/IP) to serve billion of users worldwide and allows the user to interface many real time embedded applications like data acquisition, Industrial automations and safety measures etc,. This paper approached towards the design and development of on-line Interactive Data Acquisition and Control System (IDACS) using ARM based embedded web server. It can be a network, intelligent and digital distributed control system. Single chip IDACS method improves the processing capability of a system and overcomes the problem of poor real time and reliability. This system uses ARM9 Processor portability with Real Time Linux operating system (RTLinux RTOS) it makes the system more real time and handling various processes based on multi tasking and reliable scheduling mechanisms. Web server application is ported into an ARM processor using embedded `C' language. Web pages are written by Hyper text markup language (HTML); it is beneficial for real time IDACS, Mission critical applications, ATM networks and more.

Aiming at the current situation that the existing traffic event detection algorithm is complex and cumbersome, a traffic incident detection method based on the width characteristics of the moving object was proposed. Firstly, the foreground object is extracted by ViBe (visual background extractor) and the foreground target is subjected to hole removal and smoothing processing. Then, the moving target is marked with a label, the width change characteristic of the moving target mark frame is analysed and the traffic event detection model is introduced, thereby achieving detection of traffic events. The experimental results show that the method can effectively detect the rear-end event, the crossing event and the collision-fixing event in the traffic incident.

A vibration signal acquisition system was designed based virtual instrument technology. The process and principle design of structure vibration signal acquisition system based on LabVIEW software and CompactRIO hardware were discussed in detail. Finally, system test was conducted by a three story steel frame structure. And the test results were compared with traditional instruments system. The experiment showed that the data acquisition system of structure vibration signal which based on CompactRIO is precise and reliable in measurement results, and can realize multi channel synchronous data acquisition, storage, and support the real-time online data filtering and spectrum analysis and so on.Compared with the traditional platforms, the system proposed in the paper has short development cycle, low cost, user-defined functions, rugged, portable and other advantages. It can be widely used in complex and special building environment, so it will have a broad application prospect in the field of SHM.

Fourteen pilots flew a synthetic vision system (SVS) display, through a terrain and traffic-rich environment in a high fidelity flight simulator. Traffic information was hosted on the SVS display. In a 2times2 factorial design, the SVS display hosted a highway-in-the-sky in half the conditions, and hosted an instrument panel overlay in the other half. We examined the effects of the resulting clutter from overlay (but reduced scanning) on routine flight performance, SVS traffic detection, and response to off-normal events, as these were mediated by visual scanning measures of attention allocation. The tunnel greatly improved flight path tracking and traffic detection, but slightly disrupted the detection of unexpected outside world traffic. The instrument panel overlay provided no benefits to tracking and a clutter-related time cost to SVS traffic detection. Visual attention was focused on the SVS display over half the time, and rarely on the outside world, even in visual meteorological conditions (VMC), a source of possible cognitive tunneling.

With the rapid development of Internet of Vehicles (IoV), the applications of traffic congestion detection based on Intelligent Traffic System (ITS) are proposed and widely used. However, location privacy leakage has become a serious problem in traffic congestion detection based on traffic voting, because vehicles will upload traffic congestion information and their own locations to the traffic management server. In this paper, we propose a decentralized weighted vote traffic congestion detection framework, which allows vehicles can safely participate in the traffic voting, under location privacy protection, and accurately obtain the traffic congestion status of the road. Specifically, we introduce blockchain technology into traffic congestion detection, and apply smart contract to replace the traditional trusted third party. We propose a weighted voting scheme, and set the weight of votes depending on the distance between the vehicle’s current position and the location of congestion. The security analysis result shows that our framework can satisfy the above requirements. Finally, the experiment results demonstrate that our framework is efficient and practical.