Статті в журналах з теми "Traffic signs and signals Australia"
Щоб переглянути інші типи публікацій з цієї теми, перейдіть за посиланням: Traffic signs and signals Australia.
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-50 статей у журналах для дослідження на тему "Traffic signs and signals Australia".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте статті в журналах для різних дисциплін та оформлюйте правильно вашу бібліографію.
1
Rash-ha Wahi, Rabbani, Narelle Haworth, Ashim Kumar Debnath, and Mark King. "Influence of Type of Traffic Control on Injury Severity in Bicycle–Motor Vehicle Crashes at Intersections." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 38 (May 14, 2018): 199–209. http://dx.doi.org/10.1177/0361198118773576.
Повний текст джерелаАнотація:
Many studies have identified factors that contribute to bicycle–motor vehicle (BMV) crashes, but little is known about determinants of cyclist injury severity under different traffic control measures at intersections. Preliminary analyses of 5,388 police-reported BMV crashes from 2002 to 2014 from Queensland, Australia revealed that cyclist injury severity differed according to whether the intersection had a Stop/Give-way sign, traffic signals or no traffic control. Therefore, separate mixed logit models of cyclist injury severity (fatal/hospitalized, medically treated, and minor injury) were estimated. Despite similar distributions of injury severity across the three types of traffic control, more factors were identified as influencing cyclist injury severity at Stop/Give-way controlled intersections than at signalized intersections or intersections with no traffic control. Increased injury severity for riders aged 40–49 and 60+ and those not wearing helmets were the only consistent findings across all traffic control types, although the effect of not wearing helmets was smaller at uncontrolled intersections. Cyclists who were judged to be at fault were more severely injured at Stop/Give-way and signalized intersections. Speed zone influenced injury severity only at Stop/Give-way signs and appears to reflect differences in intersection design, rather than speed limits per se. While most BMV crashes occurred on dry road surfaces, wet road surfaces were associated with an increased cyclist injury severity at Stop/Give-way intersections. The results of this study will assist transport and enforcement agencies in developing appropriate mitigation strategies to improve the safety of cyclists at intersections.
2
Inagaki, Joji. "Traffic message signals and signs." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 76, no. 1 (1992): 21–24. http://dx.doi.org/10.2150/jieij1980.76.1_21.
Повний текст джерела
3
C, Bharanidharan, Jeevan Chandra, Hitesh Kumar, Jayasurya s, and Stella A. "GLOBAL IMAGE IDENTIFIER." International Research Journal of Computer Science 9, no. 8 (August 12, 2022): 195–200. http://dx.doi.org/10.26562/irjcs.2022.v0908.08.
Повний текст джерелаАнотація:
Many of the things, signs, and symbols we encounter when exploring the world might not be familiar to us. A global image identifier must be created to minimize confusion and misunderstanding. We shall use the less-than-universal traffic signs as an example. Road signs are strategically positioned to safeguard drivers’ and tourists' safety. Additionally, they offer instructions on when and where cars should turn or not turn. The traffic signs on the road express several cautions. In India, there are 400 traffic accidents per day, according to official statistics. Road signs ensure the safety of both automobiles and pedestrians by preventing accidents from occurring. Additionally, traffic signals reduce the incidence of traffic offences by ensuring that drivers follow certain laws. All users of the road, including pedestrians and automobiles, should give priority to traffic signals. For a multitude of reasons, including difficulty focusing, tiredness, and lack of sleep, we fail to see traffic signs. Other reasons for ignoring the indicators include impaired vision, the outside world's influence, and environmental factors. There is a critical need for a system that can recognize traffic lights automatically.
4
Sreenivas, Dr M. "Traffic Sign Recognition Using CNN." International Journal for Research in Applied Science and Engineering Technology 10, no. 6 (June 30, 2022): 3522–34. http://dx.doi.org/10.22214/ijraset.2022.44532.
Повний текст джерелаАнотація:
Abstract: You've probably heard about self-driving automobiles, in which the passenger can completely rely on the vehicle for transportation. Cars must, however, understand and follow all traffic rules in order to achieve level 5 autonomy. Many researchers and large organisations, including as Tesla, Uber, Google, Mercedes-Benz, Toyota, Ford, Audi, and others, are working on autonomous vehicles and self-driving automobiles in the world of artificial intelligence and technological innovation. As a result, in order for this technology to be accurate, the vehicles must be able to understand traffic signs and make proper decisions. Speed limits, prohibited entry, traffic signals, turn left or right, children crossing, no passing of big trucks, and so on are all examples of traffic signs. Traffic sign classification is the process of determining which class a traffic sign belongs to. In this project, we'll create a deep neural network model that can categorise traffic signals in an image into several groups. Using our model, we can read and understand traffic signs, which is a critical function for all autonomous vehicles. Based on Convolutional Neural Networks, we offer a method for detecting traffic signs (CNN). We employ support vector machines to convert the original image to grey scale, then apply convolutional neural networks with fixed and learnable layers for detection and recognition. The fixed layer can limit the number of interest regions to be detected and crop the boundaries to be as near to the original as possible.
5
Almusawi, Husam A., Mohammed Al-Jabali, Amro M. Khaled, Korondi Péter, and Husi Géza. "Self-Driving robotic car utilizing image processing and machine learning." IOP Conference Series: Materials Science and Engineering 1256, no. 1 (October 1, 2022): 012024. http://dx.doi.org/10.1088/1757-899x/1256/1/012024.
Повний текст джерелаАнотація:
Abstract The major goal of this paper is to build and represent a prototype of a fully autonomous car that employs computer vision to detect lanes and traffic signs without human intervention using limited computing capacity. The project contains an embedded system represented by a Raspberry Pi 3 which serves as the image processing and machine learning unit. This method requires a stream of images as input for the computer vision using OpenCV2 library with C++ programming language along with Haar Cascade Classifier for the detection of traffic signs. The Raspberry Pi will send binary signals to the Arduino UNO which is responsible for merging those signals with the ones from the ultrasonic sensor and producing new signals which are sent to the motor driver to control the direction and speed of the dc motors. The system was able to detect the lane and respond to changes in lane direction, as well as to detect traffic signs and give appropriate responses.
6
Xiong, Jun Yu, Xiao Hui Du, Jia Qi Wang, and Hui Li Zhai. "A Optimized Design of One Traffic Circle." Advanced Materials Research 588-589 (November 2012): 1632–35. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.1632.
Повний текст джерелаАнотація:
In this paper we use queuing theory to analysis the incoming traffic, developed an effective way to control the traffic of a circle by using stop signs and yield signs,and calculated the traffic capacity and average waiting time of this method. Then, we use signals to control the traffic and improve the original method by a analysis the ways the car can pass through the circle crossing. Taking into account of the traffic flow in the different time of a day, we got the light's signal period to adapt to the features of the traffic flow.
7
Satpute, Ms Bhumika Vasant, Ms Dhanlaxmi Balavant Don, Ms Rakhi Ajaykumar Salave, Ms Abrar Zameer Shaikh, and Prof Akash K. Gunjal. "Intelligent Transportation System." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4560–69. http://dx.doi.org/10.22214/ijraset.2022.43354.
Повний текст джерелаАнотація:
Abstract: People have experienced frequent communication and information exchange in recent years as a result of the proliferation of mobile devices. For example, when people go on vacations, it is common for each person to bring a smart phone with them to get information about nearby attractions. When a user visits a location, the application will provide useful information based on the user's current location preferences and previous visits to locations and their traffic signs. This new feature of map will learn your preferences and will display traffic signs in the area this system would display all traffic signs in and around the city including No Parking, Give Way, Speed Breakers ,Zebra Crossings ,Signals ,Tunnels, Sharp Curves, Speed ,No Overtaking Zones, Accidents Ponds, and Cycle Lanes. The use of popularity based filtering allows users to see all of the traffic signs in the area. Keywords: Traffic signs, Intelligent Transportation
8
Allen, R. Wade, Zareh Parseghian, and Theodore J. Rosenthal. "Simulator Evaluation of Road Signs and Signals." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 38, no. 14 (October 1994): 903–6. http://dx.doi.org/10.1177/154193129403801423.
Повний текст джерелаАнотація:
This paper describes a accuracy versus speed paradigm for evaluating signing and traffic signal conditions using low cost simulation technology. Two research examples are reviewed. One study involved the use of an interactive driving simulator that included the presentation of high resolution signs over the apparent viewing range from 500 to 50 feet. Drivers had to control vehicle speed and lane position while identifying the meaning of symbol signs as rapidly as possible. Subjects were scored in terms of correctness and the distance at which signs were identified. A second study involved a computer controlled presentation of static signalled intersection scenes, including supplemental signs, to subjects who were required to make decisions about permissive movements. Subjects were required to make decisions about permissive movements as rapidly as possible, and were scored by the computer on correctness and response time. Results in both studies showed that both response speed and correctness degrade with the complexity of signal and sign treatments.
9
Lee, Suzanne E., Sarah B. Brown, Miguel A. Perez, Zachary R. Doerzaph, and Vicki L. Neale. "Normal and Hard Braking Behavior at Stop Signs and Traffic Signals." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 22 (September 2005): 1897–901. http://dx.doi.org/10.1177/154193120504902203.
Повний текст джерелаАнотація:
A testbed intersection violation warning system was developed to address the problem of intersection crashes. The effectiveness of such systems is fundamentally dependent on the driver-braking model used to decide if a warning should be issued to the driver. If the model is unrealistic, drivers can either be annoyed due to assumed braking levels that are too low, or can be warned too late if braking expectations are too high. Initial algorithm development relied on data from the Collision Avoidance Metrics Partnership (CAMP) Forward Collision Warning (FCW) project. However, it was unknown whether the CAMP data (collected in the presence of stopped lead vehicles) would be applicable to the intersection problem (e.g., will drivers respond similarly to red traffic signals and stopped lead vehicles). Braking profile and performance tests were thus conducted to determine the applicability of the CAMP FCW results to the intersection violation warning.
10
Wani, Gulzar Ahmad, and Dr Gurinder Kaur Sodhi. "Implementation of Bootstrap Technique in Detection of Road Sign using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 12 (December 31, 2022): 2299–304. http://dx.doi.org/10.22214/ijraset.2022.48460.
Повний текст джерелаАнотація:
Abstract: Traffic sign recognition is a driver assistance tool that can alert and warn the driver by showing any applicable limitations on the current stretch of road. Such limitations include signs such as 'traffic light approaching' or 'walking crossing.' The present research focuses on identifying Indian road and traffic signs in real time. Real-time footage from a moving automobile is captured by a computerized camera, and genuine traffic signs are retrieved using vision data. The network is divided into three stages: one for identification and the other for classification. The first stage created and constructed hybrid colour edge detection. In stage 2, a new and successful custom feature-based technique is used for the first time in a road sign identification strategy. Finally, a multilayer Convolution Neural Network (CNN) with Graphical User Interface (GUI) is being created to identify and analyse various traffic signals. It's tricky, despite the fact that it's been tested on both traditional and nontraffic signs and passed with flying colours..
11
Koh, Dong-Woo, Jin-Kook Kwon, and Sang-Goog Lee. "Traffic Sign Recognition Evaluation for Senior Adults Using EEG Signals." Sensors 21, no. 13 (July 5, 2021): 4607. http://dx.doi.org/10.3390/s21134607.
Повний текст джерелаАнотація:
Elderly people are not likely to recognize road signs due to low cognitive ability and presbyopia. In our study, three shapes of traffic symbols (circles, squares, and triangles) which are most commonly used in road driving were used to evaluate the elderly drivers’ recognition. When traffic signs are randomly shown in HUD (head-up display), subjects compare them with the symbol displayed outside of the vehicle. In this test, we conducted a Go/Nogo test and determined the differences in ERP (event-related potential) data between correct and incorrect answers of EEG signals. As a result, the wrong answer rate for the elderly was 1.5 times higher than for the youths. All generation groups had a delay of 20–30 ms of P300 with incorrect answers. In order to achieve clearer differentiation, ERP data were modeled with unsupervised machine learning and supervised deep learning. The young group’s correct/incorrect data were classified well using unsupervised machine learning with no pre-processing, but the elderly group’s data were not. On the other hand, the elderly group’s data were classified with a high accuracy of 75% using supervised deep learning with simple signal processing. Our results can be used as a basis for the implementation of a personalized safe driving system for the elderly.
12
Ford, Garry L., and Dale L. Picha. "Teenage Drivers’ Understanding of Traffic Control Devices." Transportation Research Record: Journal of the Transportation Research Board 1708, no. 1 (January 2000): 1–11. http://dx.doi.org/10.3141/1708-01.
Повний текст джерелаАнотація:
Teenage drivers are involved in traffic crashes more often than any other driver group, and their fundamental knowledge of traffic control devices and rules of the road is extremely important in safe driving. Only limited data exist, however, on teenage drivers’ understanding of traffic control devices, and little research has been done on determining their comprehension thereof. Research was performed to document teenage drivers’ ability to understand 53 traffic control devices. These traffic control devices included 6 combinations of sign shape and color; 8 regulatory signs; 14 warning signs; 7 school, highway–railroad grade crossing, and construction warning signs; 7 pavement markings; and 11 traffic signals. Research results were then compared with previous comprehension studies to identify specific traffic control devices that the driving public continually misunderstands. In general, the results indicated that surveyed teenage drivers understood the traffic control devices to some degree. Only nine devices were understood by more than 80 percent of the respondents. The devices found problematic to teenage drivers include combinations of sign shape and color, warning-symbol signs, white pavement markings, flashing intersection beacons, and circular red/green arrow left-turn-signal displays. Recommendations include revising states’ drivers handbooks and increasing emphasis in the driver education curriculum to clarify the meaning and intent of problematic traffic control devices.
13
Balázs, Viktor, László Szilágyi, Antal Apagyi, and Timotei István Erdei. "The Implementation of an Opencv-Based Traffic Sign Identifier Videoanalyst Software." Műszaki Tudományos Közlemények 9, no. 1 (October 1, 2018): 39–42. http://dx.doi.org/10.33894/mtk-2018.09.05.
Повний текст джерелаАнотація:
Abstract Nowdays, accidents tend to happen because our attention is being split up by the ever-growing influx of information, losing the focus from the driving, traffic signs, and other signals. The consequences of these minor or major accidents weight down on our shoulders. During our project, we tried to eliminate, or help this issue, using present technology, improving upon that, trying to avoid these accidents. Our task consisted on implementing a software, that could identify traffic signs from any video streams.
14
Saadi Abdullah, Ahmed, Majida Ali Abed, and Ahmed Naser Ismael. "Traffic signs recognitionusing cuckoo search algorithm and Curvelettransform with image processing methods." Journal of Al-Qadisiyah for computer science and mathematics 11, no. 2 (September 6, 2019): 74–81. http://dx.doi.org/10.29304/jqcm.2019.11.2.591.
Повний текст джерелаАнотація:
Compliance with traffic signs is one of the most important things to follow to avoid traffic accidents as well as compliance with traffic rules in terms of parking, speed control, and other traffic sings. Progress in different areas, such as self-propelled car manufacturing or the production of devices that help the visually impaired, require values to find a way to determine traffic signals with high precision in this research, The first step is to take a picture of the traffic sign and apply some digital image processing techniques to increase image contrast and eliminate noise in the image, the second step resize of origin image , the third step convert color to(YCbCr, HSB) or stay on RGB, the fourth step image is disassembled using curvelet transform and get coefficients , and the last step using cuckoo search algorithm to recognition sings traffics ,the MATLAB (2011b) program was used to implement the proposed algorithm . After applying this method to a set of traffic the percentage of discrimination of traffic signs was yellow 93%, green 94%, blue 94.5%, red 96%.
15
Jain, Vaibhav, Tanay ., Saransh Gangele, and K. Kalimuthu. "Driver Assistance System using in-vehicle Traffic Lights and Signs." International Journal of Engineering & Technology 7, no. 2.24 (April 25, 2018): 527. http://dx.doi.org/10.14419/ijet.v7i2.24.12151.
Повний текст джерелаАнотація:
In recent years, with the advancement of vehicular communication, it is possible to detect various road signs and provide traffic light information to the driver inside the vehicle with the application of heads-up display (HUD). It detects road signs, does basic classifications and accordingly directs the driver to slow down or stop the vehicle. The vehicle’s heads-up display keeps the driver focused by providing road warnings, speed limit, traffic signals and some vital navigation information in the driver’s line of sight(LOS). This system has 4 phases, Image recognition, wireless communication, obstacle detection and driver mechanism. This system aims to create a prototype of a smart driver assistance system which provides better road traffic and driver’s safety in countries with high traffic congestion where fully automated vehicles cannot function effectively. This system can be easily implemented in real time scenarios to reduce accidents and enhance the convenience of driving.
16
Lengyel, Henrietta, and Zsolt Szalay. "Classification of traffic signal system anomalies for environment tests of autonomous vehicles." Production Engineering Archives 19, no. 19 (June 1, 2018): 43–47. http://dx.doi.org/10.30657/pea.2018.19.09.
Повний текст джерелаАнотація:
Abstract In the future there will be a lot of changes and development concerning autonomous transport that will affect all participants of transport. There are still difficulties in organizing transport, but with the introduction of autonomous vehicles more challenges can be expected. Recognizing and tracking horizontal and vertical signs can cause a difficulties for drivers and, later, for autonomous systems. Environmental conditions, deformity and quality affect the perception of signals. The correct recognition results in safe travelling for everyone on the roads. Traffic signs are designed for people that is why the recognition process is harder for the machines. However, nowadays some developers try to create a traffic sign that autonomous vehicles can use. Computer identification needs further development, as it is necessary to consider cases where traffic signs are deformed or not properly placed. In the following investigation, the advantages and disadvantages of the different perception methods and their possibilities were gathered. A methodology for the classification of horizontal and vertical traffic signs anomalies that may help in designing better testing and validation environments for traffic sign recognition systems in the future was also proposed.
17
Paavani, R. Krishna, V. Indraja, V. Neelimajyothi, S. Sai, and Mr M. Sriramulu. "Traffic Sign Board Detection Using Single Shot Detection (SSD)." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 4095–97. http://dx.doi.org/10.22214/ijraset.2022.43336.
Повний текст джерелаАнотація:
Abstract: Traffic sign board detection (TSBD) is a significant portion of intelligent transportation system (ITS). Being able to identify traffic signals more accurately and effectively can improve safe driving .Due to increase in technology there are autonomous vehicles . The traffic sign recognition process includes two parts: detection and classification. In this paper, we use an object detection algorithm called SSD to detect the traffic signs. This convolutional neural network uses multiple feature maps to detect objects. For the traffic sign is very small to the whole picture, the SSD model has been improved to have a better detection result of traffic signs. In the experiments, the model has been simplified and the size of the prior box has been modified. The improved network has a good detection effect on small targets. The results on the test data set show that the proposed algorithm performs well for single-target, multi-target and dark-light images. The precision and recall on the test data set are 91.09%, and 88.06%. Keywords: Automatic traffic sign board Detection, SSD, Image processing, Text alert.
18
Domínguez, Hugo, Alberto Morcillo, Mario Soilán, and Diego González-Aguilera. "Automatic Recognition and Geolocation of Vertical Traffic Signs Based on Artificial Intelligence Using a Low-Cost Mapping Mobile System." Infrastructures 7, no. 10 (October 4, 2022): 133. http://dx.doi.org/10.3390/infrastructures7100133.
Повний текст джерелаАнотація:
Road maintenance is a key aspect of road safety and resilience. Traffic signs are an important asset of the road network, providing information that enhances safety and driver awareness. This paper presents a method for the recognition and geolocation of vertical traffic signs based on artificial intelligence and the use of a low-cost mobile mapping system. The approach developed includes three steps: First, traffic signals are detected and recognized from imagery using a deep learning architecture with YOLOV3 and ResNet-152. Next, LiDAR point clouds are used to provide metric capabilities and cartographic coordinates. Finally, a WebGIS viewer was developed based on Potree architecture to visualize the results. The experimental results were validated on a regional road in Avila (Spain) demonstrating that the proposed method obtains promising, accurate and reliable results.
19
Mitroshin, Dmitriy V. "On the Improvement of the International Statutory Regulation in Road Traffic." Administrative law and procedure 2 (February 11, 2021): 25–28. http://dx.doi.org/10.18572/2071-1166-2021-2-25-28.
Повний текст джерелаАнотація:
The article describes the role of the Russian Federation in the development of an international legal framework for road traffic, and its implementation at the global and regional levels. The content of the amendments implemented in the relevant basic international legal acts — the 1968 Conventions on Road Traffic and on Road Signs and Signals was specified. An assessment is given to the contribution of LL.D, Professor Alexander Yuryevich Yakimov to this activity.
20
A, Jayaprakash, and C. Kezi Selva Vijila. "Detection and Recognition of Traffic Sign using FCM with SVM." JOURNAL OF ADVANCES IN CHEMISTRY 13, no. 6 (February 25, 2017): 6285–89. http://dx.doi.org/10.24297/jac.v13i6.5773.
Повний текст джерелаАнотація:
This paper mainly focuses on Traffic Sign and board Detection systems that have been placed on roads and highway. This system aims to deal with real-time traffic sign and traffic board recognition, i.e. localizing what type of traffic sign and traffic board are appears in which area of an input image at a fast processing time. Our detection module is based on proposed extraction and classification of traffic signs built upon a color probability model using HAAR feature Extraction and color Histogram of Orientated Gradients (HOG).HOG technique is used to convert original image into gray color then applies RGB for foreground. Then the Support Vector Machine (SVM) fetches the object from the above result and compares with database. At the same time Fuzzy Cmeans cluster (FCM) technique get the same output from above result and then to compare with the database images. By using this method, accuracy of identifying the signs could be improved. Also the dynamic updating of new signals can be done. The goal of this work is to provide optimized prediction on the given sign.
21
Azzam, Diya Mahmoud, and Craig C. Menzemer. "Numerical Study of Stiffened Socket Connections for Highway Signs, Traffic Signals, and Luminaire Structures." Journal of Structural Engineering 134, no. 2 (February 2008): 173–80. http://dx.doi.org/10.1061/(asce)0733-9445(2008)134:2(173).
Повний текст джерела
22
Balado, Jesús, Elena González, Pedro Arias, and David Castro. "Novel Approach to Automatic Traffic Sign Inventory Based on Mobile Mapping System Data and Deep Learning." Remote Sensing 12, no. 3 (February 1, 2020): 442. http://dx.doi.org/10.3390/rs12030442.
Повний текст джерелаАнотація:
Traffic signs are a key element in driver safety. Governments invest a great amount of resources in maintaining the traffic signs in good condition, for which a correct inventory is necessary. This work presents a novel method for mapping traffic signs based on data acquired with MMS (Mobile Mapping System): images and point clouds. On the one hand, images are faster to process and artificial intelligence techniques, specifically Convolutional Neural Networks, are more optimized than in point clouds. On the other hand, point clouds allow a more exact positioning than the exclusive use of images. The false positive rate per image is only 0.004. First, traffic signs are detected in the images obtained by the 360° camera of the MMS through RetinaNet and they are classified by their corresponding InceptionV3 network. The signs are then positioned in the georeferenced point cloud by means of a projection according to the pinhole model from the images. Finally, duplicate geolocalized signs detected in multiple images are filtered. The method has been tested in two real case studies with 214 images, where 89.7% of the signals have been correctly detected, of which 92.5% have been correctly classified and 97.5% have been located with an error of less than 0.5 m. This sequence, which combines images to detection–classification, and point clouds to geo-referencing, in this order, optimizes processing time and allows this method to be included in a company’s production process. The method is conducted automatically and takes advantage of the strengths of each data type.
23
Et. al., Nikhil S. Rajguru,. "Implementation paper of Traffic Signal Detection and Recognition using deep learning." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 1S (April 11, 2021): 212–19. http://dx.doi.org/10.17762/turcomat.v12i1s.1760.
Повний текст джерелаАнотація:
Traffic boards and traffic signals are used to maintain proper traffic through busy roads. They help to recognize the rules to follow when driving the vehicle. These signs warn the distracted driver, and prevent his/her actions which could lead to an accident. We have proposed a system which can help recognize these boards and signals at real time thus avoiding major mishap. A real-time automatic sign detection and recognition can help the driver, significantly increasing his/her safety. Lately traffic sign recognition has got an immense interest lately by large scale companies such as Google, Apple and Volkswagen etc. which is driven by the market needs for intelligent applications such as autonomous driving, driver assistance systems (ADAS), mobile mapping, Mobil eye, Apple, etc. Hence, here, we have implemented to do the same with cost efficient manner using Raspberry Pi. The proposed system detects the traffic board or traffic signals, capture its image which through deep learning approach recognizes the same to give result on dashboard as well it gives the measures of distance from front obstacle which helps to implement brake system if obstacle is near. PiCam is used to capture images of traffic sings and is connected to RaspberryPi. Monitor is used to display required output, showing type of sign and distance of collision. This proposal will avoid large number of accidents occurring at bridges and work in progress area due to automated braking system and simultaneous reduce death ratio.
24
Hashim, Rabia, Ravinder Pal Singh, and Monika Mehra. "Road Sign Detection System using Neural Networks and Tensor Flow." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 548–56. http://dx.doi.org/10.22214/ijraset.2022.40672.
Повний текст джерелаАнотація:
Abstract: Automated tasks have simplified almost everything we perform in today's environment. Due to a desire to focus only on driving, drivers regularly ignore signs placed on the side of the road, which can be harmful to themselves and others. To address this issue, the motorist should be informed in a method that does not require them to divert their concentration. Traffic Sign Detection and Recognition (TSDR) is critical in this case since it alerts the motorist of approaching signals. Not only are roads safer because of this, but motorists also feel more at ease when driving unfamiliar or difficult routes. Another typical issue is inability to read the sign. Driver assistance systems (ADAS) will make it easier for motorists to read traffic signs with the help of this software. We provide a traffic sign detection and recognition system that employs image processing for sign detection and an ensemble of Convolutional Neural Networks (CNNs) for sign recognition. Because of its high recognition rate, CNNs may be used in a wide range of computer vision applications. TensorFlow is used in CNNTSR (Traffic Sign Recognition), a key component of current driving assistance systems that improves driver safety and comfort. TensorFlow is used to implement CNNTSR (Traffic Sign Recognition). This article examines a technology that assists drivers in recognizing traffic signs and avoiding road accidents. Two things determine the accuracy of TSR: the feature extractor and the classifier. Although there are a variety of approaches, most recent algorithms use CNN (Convolutional Neural Network) to do both feature extraction and classification tasks. Using TensorFlow and CNN, we create traffic sign recognition. The CNN will be trained using a dataset of 43 distinct types of traffic signs. The accuracy of the findings will be 95 percent. Keywords: Driver, Tensor flow, Data Sheet, Alert, CNNTSR, ADAS.
25
Kozłowska, Małgorzata Klaudia. "Consistency and certainty of the road marking system as a subject of protection based on the offence law. Analysis of the characteristics of the offence from article 85 § 1 of offence code." Transportation Overview - Przeglad Komunikacyjny 2017, no. 1 (January 1, 2017): 17–23. http://dx.doi.org/10.35117/a_eng_17_01_03.
Повний текст джерелаАнотація:
Nowadays when the road infrastructure rapidly expands as well as the traffic, the correct road markings are of a vital importance in ensuring safety and efficiency of this traffic. Negligible number of road incidents caused by incorrect road markings results in treating quality and certainty of those markings as being of less importance. Thus, such an important issue is to ensure effective, criminal law protection of the legal interests which is a stable and reliable system of road markings. Polish legislator adopted as a subject of individual protection on the basis of code of offence inviolability of road signs and signals, and what stands behind it - stability and certainty of the road markings system. Road markings; Inviolability of road marks and signals; Road infrastructure
26
Özarpa, C., İ. Avcı, B. F. Kınacı, S. Arapoğlu, and S. A. Kara. "CYBER ATTACKS ON SCADA BASED TRAFFIC LIGHT CONTROL SYSTEMS IN THE SMART CITIES." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-4/W5-2021 (December 23, 2021): 411–15. http://dx.doi.org/10.5194/isprs-archives-xlvi-4-w5-2021-411-2021.
Повний текст джерелаАнотація:
Abstract. There are regular developments and changes in cities. Developments in cities have affected transportation, and traffic control tools have changed. Traffic signs and traffic lights have been used to direct pedestrians and vehicles correctly. Traffic light control systems are used to ensure the safety of vehicles and pedestrians, increase the fluency in traffic, guide them in transportation, warn pedestrians and drivers, and regulate and control transportation disruptions. In order to facilitate people's lives, it is desired to control the traffic components autonomously with the developments in autonomous systems. Cyber threats arise due to the active use of the internet and signals or frequencies in the use of modules that will provide communication with traffic lights, traffic signs, and vehicles, which are traffic components at the inter-sections of many roads in the control of central systems. The study is limited to smart traffic lights, which are traffic components. If we examine the cyber-attacks, we can see that Malware Attacks, Buffer Overflow Attacks, DoS attacks, and Jamming Attacks can be made. Network-Based Intrusion Detection Systems and Host-Based Intrusion Detection Systems can be used to detect and stop Malware Attacks, Buffer Overflow Attacks, DoS attacks, and Jamming Attacks. Intrusion detection systems tell us whether the data poses a threat or does not pose after the data passing through the system is examined. In this way, system protection is ensured by controlling the data traffic in the system.
27
Long, Richard G., David A. Guth, Daniel H. Ashmead, Robert Wall Emerson, and Paul E. Ponchillia. "Modern Roundabouts: Access by Pedestrians who are Blind." Journal of Visual Impairment & Blindness 99, no. 10 (October 2005): 611–21. http://dx.doi.org/10.1177/0145482x0509901005.
Повний текст джерелаАнотація:
This article describes the key differences between roundabouts and traditional intersections that have traffic signals or stop signs and discusses how these differences may affect the mobility of pedestrians who are visually impaired. It also provides a brief summary of the authors’ research on this topic and suggests strategies for addressing the access issues that roundabouts sometimes create.
28
Kim, Eunjee, Hyorim Kim, Yujin Kwon, and Gwanseob Shin. "Visibility of an in-ground signal when texting while walking." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1933–37. http://dx.doi.org/10.1177/1071181320641466.
Повний текст джерелаАнотація:
An increase in pedestrian accidents associated with smartphone use has been one of the main issues in road traffic safety research and administration. Recently, traffic lights and safety signs embedded in the ground have been introduced, but without sufficient scientific consideration. A laboratory experiment evaluated the visibility of an in-ground signal while varying its contrast and position. Twenty-three participants performed a signal detection task when conducting texting while walking on a treadmill. The signals were displayed randomly onto the ground one at a time at three different positions with three different contrasts levels and moved towards a participant. In results, the approaching signals were detected 1.7 m ~ 2.9 m in front of participants, and there were significant differences in the visibility between contrast levels and positions (p<.01). The findings suggest the importance of proper contrast level and placement when installing in- ground signals for improving their visibility by smartphone users.
29
Poku, Samuel, Delia Bandoh, Ernest Kenu, Emma Kploanyi, and Adolphina Addo- Lartey. "Factors contributing to road crashes among commercial vehicle drivers in the Kintampo North Municipality, Ghana in 2017." Ghana Medical Journal 54, no. 3 (September 30, 2020): 132–39. http://dx.doi.org/10.4314/gmj.v54i3.2.
Повний текст джерелаАнотація:
Objective: The study assessed driver, vehicular and road-related factors associated with road crashes (RC) in the Kintampo North Municipality.Design: Cross-sectional studySetting: Kintampo North MunicipalityData source: Demographics, vehicular and road usage information on registered drivers at Ghana Private Road and Transport Union (GPRTU) and Progressive Transport Owners Association (PROTOA) in Kintampo North MunicipalityMain outcome: involvement in road crashes and related factorsResult: A total of 227 drivers were approached for this study. None of them declined participation. They were all males. Most were between 28-37 years (30%). The proportion of drivers that reported RC ever involvement in at least one RC was 55.5% (95% CI: 8.0%, 62.1%). In the bivariate analysis, drink and drive changed lane without signalling, ever bribed police officer, drove beyond the maximum speed limit, paid a bribe at DVLA for driving license, violation of traffic signals were found to be associated with RC involvement (p<0.05). Drivers who violated traffic signals had 2.84 odds of being involved in road crashes compared to those who did not [aOR; 2.84 (95%CI:1.06,7.63)]Conclusion: The proportion of drivers ever involved in road crashes was high. The major factor that is associated with RC involvement was a violation of the traffic light signals. Continuous driver education and enforcement of road traffic regulations by the appropriate authorities could curb the road crash menace in the Municipality.Keywords: commercial drivers, road crashes, vehicle, road signs, traffic light signalFunding: The authors funded this work.
30
Fouad, Fouad H., Edgar Nunez, and Elizabeth A. Calvert. "Proposed Revisions to AASHTO Standard Specifications for Structural Supports for Highway Signs, Luminaires, and Traffic Signals." Transportation Research Record: Journal of the Transportation Research Board 1656, no. 1 (January 1999): 102–9. http://dx.doi.org/10.3141/1656-14.
Повний текст джерела
31
Cho, Taejun, Myeong-Han Kim, and Hyo-Seon Ji. "Odyssey for the Standard Design of Highway Minor Structures (Cantilever Columns for Signs, Luminaries, Traffic Signals)." Journal of the Korean Society for Advanced Composite Structures 6, no. 3 (September 30, 2015): 62–68. http://dx.doi.org/10.11004/kosacs.2015.6.3.062.
Повний текст джерела
32
Kozin, Yuriy. "Road Traffic Light in New Configuration." Journal of Road Safety 32, no. 1 (February 1, 2021): 52–54. http://dx.doi.org/10.33492/jrs-d-20-00253.
Повний текст джерелаАнотація:
The three-color system containing signals of the same circular shape has been in existence for over a hundred years. Each traffic signal has been justifiably selected to have a special color light to correspond to human psychoemotional reaction (red – stop, yellow – caution, green – go) to a given color signal (British Standards, 2015) and to comply with the laws of physics (The Motivated Engineer, 2015) – Rayleigh’s scattering law (Banc SpaceTek, 2017). The main downsides of the traditional road traffic light include the following: • The uniform circular shape of light signals results in uncertainty and difficulties for road users with color blindness and visual impairments, resulting in the need for restrictions or bans on driving license issuance in some countries. This uncertainty becomes particularly acute in conditions of low visibility. • According to the concept of harmony of form and color (Itten, 1961), a green light alone corresponds to the circular (spherical-like) shape of the signal. Red and amber lights harmoniously combine with other geometrical shapes. • The uniform shape of light signals prevents the implementation of the original compact combined model of traffic lights. For example, during the day, colorblind people can tell which signal is which because there is a standard position assigned: top – bottom or right – left (Oliveira, Souza, Junior, Sales & Ferraz, 2015). This becomes problematic if the compact combined models of traffic lights are used. Engineers and inventors have been trying to solve these problems by introducing random changes in the light signal shape and complicating the traffic light design. For a long time there have been different proposals about how to eliminate the demerits of the existing traffic lights: from arbitrary changes in the signal shape (Patterson, 1988) to transformation of traffic lights into a single-section display panel (Kulichenko, 2011) which replaces among others stationary road signs. However, technical solutions like these deprive the traffic light of its signal uniformity and conciseness (simplicity, clarity and precision of its controlling effect), features which help safe traffic regulation in a busy and dynamic mode. Technical modernization of individual signal components has been going hand in hand with technological developments as light sources, diffusers, lenses, controllers, materials, control systems, timers, etc. are improved. However, adequate design and aesthetic proposals are considerably behind. The aim of this paper is to propose a concept of creating control signals of traffic light that harmonize color and form, and, as a result, to create a new model of traffic light that will be convenient for all road users.
33
J., Parkavi. "A Research Paper on Convolutional Neural Network (CNN) Theory based Automated Traffic and Road Sign Detection and Recognition System." International Journal for Research in Applied Science and Engineering Technology 9, no. VII (July 31, 2021): 3775–83. http://dx.doi.org/10.22214/ijraset.2021.37195.
Повний текст джерелаАнотація:
India is a country with a dense road network and has a complex system to maintain road safety. As we all know that we have a complex traffic system in which we have more than 100 types of traffic symbols in it. While driving, it is tough to take care of all the symbols placed at the road end. Sometimes the driver does not know what that symbol says. In this system sometimes the driver misses the road signs because the attention of the driver is overdriving the vehicle safe which leads to an accident or issuing Challan. Sometimes the traffic signs don't notice by the driver. So all the drivers or the vehicle need a system which is capable to read and recognize the traffic symbol placed at the road end and the system must be capable of giving simple instruction to the driver. So that system can automatically detect which type of symbol is this and can notify the driver. The system must have a good accuracy rate, as well as the system, must have a very good speed of working. This system can also be used in driverless cars to notify the system about the road signals and hence the system can tackle all the symbols carefully.
34
Li, Zongzhi, Hoang Dao, Harshingar Patel, Yi Liu, and Bei Zhou. "Incorporating Traffic Control and Safety Hardware Performance Functions into Risk-based Highway Safety Analysis." PROMET - Traffic&Transportation 29, no. 2 (April 19, 2017): 143–53. http://dx.doi.org/10.7307/ptt.v29i2.2041.
Повний текст джерелаАнотація:
Traffic control and safety hardware such as traffic signs, lighting, signals, pavement markings, guardrails, barriers, and crash cushions form an important and inseparable part of highway infrastructure affecting safety performance. Significant progress has been made in recent decades to develop safety performance functions and crash modification factors for site-specific crash predictions. However, the existing models and methods lack rigorous treatments of safety impacts of time-deteriorating conditions of traffic control and safety hardware. This study introduces a refined method for computing the Safety Index (SI) as a means of crash predictions for a highway segment that incorporates traffic control and safety hardware performance functions into the analysis. The proposed method is applied in a computation experiment using five-year data on nearly two hundred rural and urban highway segments. The root-mean square error (RMSE), Chi-square, Spearman’s rank correlation, and Mann-Whitney U tests are employed for validation.
35
Шакирзянов, Р. М. "DETECTION OF TRAFFIC SIGNALS USING COLOR SEGMENTATION AND A RADIAL SYMMETRY DETECTOR." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, no. 6 (January 10, 2021): 25–33. http://dx.doi.org/10.36622/vstu.2020.16.6.004.
Повний текст джерелаАнотація:
В настоящее время широкое распространение получают беспилотные системы управления различными транспортными средствами, в том числе автомобилями. Управление беспилотным автомобилем предполагает решение задач, связанных с распознаванием объектов дорожной обстановки: пешеходов, автомобилей, препятствий (в виде ям, кочек, столбов, деревьев, зданий и т.д.), дорожных знаков, разметки, светофоров. Предложен алгоритм решения задачи обнаружения и распознавания сигналов светофоров круглой формы. Для решения этой задачи задействованы: быстрое преобразование радиальной симметрии, цветовая сегментация, морфологические операции. Особенностью алгоритма является то, что области расположения световых сигналов предварительно определяются по цветовому признаку с последующим уточнением формы и положения объектов на изображении. На основе предложенного метода было разработано программное обеспечение для обнаружения сигналов светофоров на фотоснимках. Программное обеспечение было протестировано на общедоступной базе изображений, содержащей светофоры. Предлагаемый алгоритм показал работоспособность, он может быть расширен в части типов распознаваемых сигналов и применён в составе систем управления беспилотными транспортными средствами, а также в составе систем помощи водителю для решения задач по предупреждению опасных и аварийных ситуаций на транспорте Currently, unmanned systems for controlling various vehicles, including cars, are becoming widespread. Driving an unmanned vehicle involves solving problems related to the recognition of traffic objects: pedestrians, cars, obstacles (in the form of holes, bumps, poles, trees, buildings, etc.), road signs, markings, traffic lights. An algorithm for solving the problem of detecting and recognizing circular traffic signals is proposed. To solve this problem, the following are involved: rapid transformation of radial symmetry, color segmentation, morphological operations. A feature of the algorithm is that the areas of the location of the light signals are preliminarily determined by color, followed by the refinement of the shape and position of objects in the image. Based on the proposed method, software was developed for detecting traffic signals in photographs. The software was tested on a publicly available database of images containing traffic lights. The proposed algorithm has shown its efficiency, it can be expanded in terms of the types of signals recognized and used as part of control systems for unmanned vehicles, as well as part of driver assistance systems for solving problems to prevent dangerous and emergency situations
36
Barvinska, Khrystyna, and Oleh Hrytsun. "Comparative analysis of the driver's psychological perception of information and the use of road sign recognition systems." Journal of Mechanical Engineering and Transport 16, no. 2 (January 17, 2023): 3–8. http://dx.doi.org/10.31649/2413-4503-2022-16-2-3-8.
Повний текст джерелаАнотація:
This article analyzes drivers' psychophysiological perception of information on the road and the advantages of using means of automatic use of signs (TSR). A survey of drivers was conducted on the road section where traffic organization changed. The drivers were chosen with different driving experiences, age categories, and needs for using the car, but they used the road section under investigation even before its reconstruction. Drivers of vehicles by age category were divided into three categories: under 25 years of age (category 1), 42% of drivers aged 26 to 50 years (category 2), and 19% of drivers aged 50 and older (category 3). It was established that 47% of the first drivers' category use automatic road sign recognition tools, 31% of the second category use the TSR system, and only 22% of the third category use the road sign recognition system. Four new road signs were installed during the development of the design schemes for organizing traffic in the middle section at a distance of 50 m. Based on this, an additional survey was conducted on drivers' memorization of specific new signs installed on the investigated section of the road. The results of the survey of drivers of different age categories were taken into account. It was studied that the most perceived number of road signs for the third category of drivers are observed at a distance of 50 to 150 m. At a distance of 50 to 150 m, they concentrate their attention, and after 150 m, they forget about the changed scheme in the traffic organization. In conclusion, drivers, getting used to traffic routes, lose vigilance, and pay less attention to existing information signals, which causes them to make wrong decisions when changing traffic organization on certain road sections. It is proposed to use automatic road sign recognition tools that are not affected by external and internal factors to increase the reliability of drivers and ensure road safety.
37
Jordan, Gihon. "Child Pedestrian–Car Crashes Near Schools Are a Small Percentage of Total Child Pedestrian Crashes in Philadelphia." Transportation Research Record: Journal of the Transportation Research Board 1636, no. 1 (January 1998): 132–37. http://dx.doi.org/10.3141/1636-21.
Повний текст джерелаАнотація:
An analysis was conducted of 2,167 pedestrian-car crashes reported by the Philadelphia Police Department in 1994. Age, sex, location, type and severity of injury, and neighborhood of victim and driver were taken directly from the police reports. The pedestrian’s actions were coded into 43 categories using the description in the police report. The unique aspect of this research is that the distance to the nearest school was measured and included in the database. The impetus to create this database was a politician’s demand that School 15 mph flasher signs be installed at the over 500 schools in Philadelphia. Only three schools in Philadelphia had school flashers when these data were collected in 1994. Most schools had School Crossing signs and School 15 mph speed limit signs where appropriate. There were about 600 school crossing guards in 1994. The data indicate that few children are injured by cars near schools during opening, recess, and closing times. More children are injured en route to or from school, but not near the school. A greater number are injured while playing after returning home from school than are injured during the trip to or from school combined. Thus, an implementation of in-school child traffic safety education, installation of new strong yellow-green School Crossing signs, and targeted and advertised enforcement of motor vehicle laws would be better responses to child traffic safety than the wholesale installation of flashing school speed limit signs. The data also confirm that dart-outs, other nonintersection crossings, traffic signals, and playing in the street are the principal crash types for children. Philadelphia has a very high rate of unlicensed, unregistered, and uninsured drivers (estimated at over 40 percent). Enforcement is lax, and the traffic court dismisses most moving violation cases. Children deserve to be made safer 24 hours a day, 365 days a year. Flashers cannot do that because only a small percentage of crashes occur near schools during school hours and because flashers are ineffective in reducing speeds and car-pedestrian crashes near schools.
38
Ghaedi, Hamed, Douglas Nims, Richard Gostautas, Eric Steinberg, Liangbo Hu, and Kenneth Walsh. "Field Study of Ohio’s Structural Support Inspection Program for Overhead Signs, Traffic Signals, and High-Mast Lights." Transportation Research Record: Journal of the Transportation Research Board 2550, no. 1 (January 2016): 15–21. http://dx.doi.org/10.3141/2550-03.
Повний текст джерела
39
Debnath, Ashim Kumar, Ross Blackman, Narelle Haworth, and Yusuf Adinegoro. "Influence of Remotely Operated Stop–Slow Controls on Driver Behavior in Work Zones." Transportation Research Record: Journal of the Transportation Research Board 2615, no. 1 (January 2017): 19–25. http://dx.doi.org/10.3141/2615-03.
Повний текст джерелаАнотація:
Remotely operated devices for traffic control—portable traffic lights and automated flagger assistance devices—are used to improve flagger safety in a one-lane-each-way work zone with lane closure. Previous research has measured the effectiveness of these devices as driver compliance rates and driver understanding of the devices, but the effects of these devices on driver behavior have not yet been examined comprehensively. Therefore, the influence of remotely operated stop–slow traffic control devices on driver behavior was examined. Video-recorded traffic movements from a rural work zone in the Queensland state of Australia provided driver speeds, deceleration profiles, stopping behavior, and compliance rates for a set of remotely operated devices new to Australia: static red–amber–green lights, static red–amber lights, static red–amber arrow lights, and mechanical stop–slow signs. Pneumatic tube traffic counters were used to collect driver speeds before and after the devices, and an on-road driver survey was conducted to elicit driver understanding of the devices. Results indicated that drivers had difficulty understanding the new devices, particularly the amber light and amber arrow options (which confused drivers about their meaning—to stop or to go). The new remotely operated devices resulted in higher approach speeds, greater variability in approach speeds, and faster deceleration rates than the flagger method. The good compliance rates observed with the remotely operated devices imply that the devices could improve flagger safety by reducing flagger exposure to traffic; however, the negative effects on driver behavior might indicate an increased risk of rear-end crashes in the advance warning area.
40
Nine, Julkar, and Rahul Mathavan. "Traffic Light and Back-light Recognition using Deep Learning and Image Processing with Raspberry Pi." Embedded Selforganising Systems 8, no. 2 (December 21, 2021): 15–19. http://dx.doi.org/10.14464/ess.v8i2.490.
Повний текст джерелаАнотація:
Traffic light detection and back-light recognition are essential research topics in the area of intelligent vehicles because they avoid vehicle collision and provide driver safety. Improved detection and semantic clarity may aid in the prevention of traffic accidents by self-driving cars at crowded junctions, thus improving overall driving safety. Complex traffic situations, on the other hand, make it more difficult for algorithms to identify and recognize objects. The latest state-of-the-art algorithms based on Deep Learning and Computer Vision are successfully addressing the majority of real-time problems for autonomous driving, such as detecting traffic signals, traffic signs, and pedestrians. We propose a combination of deep learning and image processing methods while using the MobileNetSSD (deep neural network architecture) model with transfer learning for real-time detection and identification of traffic lights and back-light. This inference model is obtained from frameworks such as Tensor-Flow and Tensor-Flow Lite which is trained on the COCO data. This study investigates the feasibility of executing object detection on the Raspberry Pi 3B+, a widely used embedded computing board. The algorithm’s performance is measured in terms of frames per second (FPS), accuracy, and inference time.
41
Louise Bentzen, Billie, Janet M. Barlow, and Douglas Gubbé. "Locator Tones for Pedestrian Signals." Transportation Research Record: Journal of the Transportation Research Board 1705, no. 1 (January 2000): 40–42. http://dx.doi.org/10.3141/1705-07.
Повний текст джерелаАнотація:
The two primary problems experienced by visually impaired persons at pedestrian-actuated intersections are determining whether there is a pushbutton and locating the push button. Many countries use accessible pedestrian signals much more widely than has been done in the United States, and a number of these—including Australia, Hong Kong, Sweden, Denmark, Germany, Belgium, and Austria—routinely require the use of a locator tone. Typically emanating from the push-button housing, a pushbutton locator tone indicates to pedestrians that they are expected to push a button to request a pedestrian phase. It enables visually impaired pedestrians to locate the push button quickly and efficiently. Research was undertaken to determine the effect of locator tone repetition rate on efficiency of pedestrians’ location of the push-button pole. Repetition rates of 1.0 and 1.5 Hz resulted in equal pole location speed, faster than that for the 0.5 Hz repetition rate, and were preferred over the 0.5 Hz repetition rate. Locator tones 2 dB above ambient sound resulted in faster pole location than did tones 5 dB and 10 dB above ambient sound. Push-button locator tones should have a standardized repetition rate between 1.0 Hz and 1.2 Hz so that it may be ensured that visually impaired pedestrians can efficiently locate push buttons. Locator tones need be no more than 5 dB louder than ambient traffic sound.
42
Protzmann, Robert, Karl Schrab, Moritz Schweppenhäuser, and Ilja Radusch. "Implementation of a Perception Module for Smart Mobility Applications in Eclipse MOSAIC." SUMO Conference Proceedings 3 (September 29, 2022): 199–214. http://dx.doi.org/10.52825/scp.v3i.123.
Повний текст джерелаАнотація:
Nowadays, smart mobility applications could benefit from environment perception, enabled by evolving sensor technology and processing capabilities available for traffic entities. On the application level, in many cases, information about detected objects is required instead of the raw sensor data. Developing and evaluating the impacts of such applications can be done in co-simulation frameworks, which combine the modeling of different domains such as application, communication, and traffic. Eclipse MOSAIC is a suitable solution for this task, combining the traffic simulation of Eclipse SUMO with other simulators, such as the integrated Application Simulator, or OMNeT++ and ns-3 for modeling communication. However, a model for perceiving surrounding traffic entities, such as vehicles, traffic signals, and traffic signs, is only available to a limited extent. In this paper, we introduce an object-level perception module to the MOSAIC Application simulator. It takes advantage of state-of-the-art spatial indexing methods to get rapid access to traffic objects, especially moving objects, within a defined field of view. We furthermore evaluate the computational performance of the indexing techniques as well as the integration with the traffic simulator SUMO using TraCI and libsumo. With the aid of this model, novel connected applications that analyze or share surrounding objects, e.g. for an improved traffic state estimation, can now be evaluated with Eclipse MOSAIC.
43
Korniienko, V., O. Gerasina, D. Tymofieiev, O. Safarov, and Y. Kovalova. "Models of monitoring of self-like traffic of information and communication networks for attack detection systems." System technologies 6, no. 137 (December 10, 2021): 99–113. http://dx.doi.org/10.34185/1562-9945-6-137-2021-10.
Повний текст джерелаАнотація:
Autoregressive, fractal and multifractal models of network self-similar traffic are con-sidered, which allow to form an adequate reference model (template) of "normal" traffic and to detect traffic anomalies in attack detection and prevention systems. Models of fractal Brownian motion and fractal Gaussian noise were considered as models of fractal motions, because they have self-similarity and long-term dependence properties that correspond to the properties of experimental data, as well as the possibility of their analytical interpretation. When evaluating and identifying processes for the implementation of autoregressive models use adaptive filters-approximators, among which there are neural network and neuro-wavelet. The following were used as multifractal models: a multifractal wavelet model with a beta distribution and a hybrid multifractal wavelet model in which the beta distribution is used on a coarse scale and the dis-tribution of point masses on an accurate scale By modeling as a result of adaptation and learning of models, autocorrelation functions, spectra and variances of model signals qualitatively correspond to the graphs of the experimental signal. In addition, the qualitative and numerical values of the characteristics of the model signals generally correspond to the characteristics of the experimental signal. In this case, beta multifractal wavelet models have a smaller error of determination of characteristics than hybrid multifractal wavelet models, and the relative root mean square error of approximation of the experimental signal using a neural network adaptive filter approximator does not exceed 0.046. Statistical verification by non-parametric criterion of signs allowed to establish the adequacy of experimental and model signals with a significance level of 0.01. Further research should be aimed at developing and using predictive models of self-similar traffic in attack detection and prevention systems, which will increase the efficiency of attack detection.
44
Hololobova, Oksana, Serhii Buriak, Volodymyr Havryliuk, Ihor Skovron, and Oleksii Nazarov. "Mathematical modelling of the communication channel between the rail circuit and the inputs devices of automatic locomotive signalization." MATEC Web of Conferences 294 (2019): 03009. http://dx.doi.org/10.1051/matecconf/201929403009.
Повний текст джерелаАнотація:
In modern practice of operating under traffic safety conditions, the traffic light signal must be transmitted to the locomotive that moves to it, and duplicate in the driver’s cab. However, this communication channel is not protected from external interference. In order to prevent the occurrence of code failure, it is necessary to create conditions under which the automatic locomotive signalling system will distinguish between signals with useful information, from signals with false information. The best way to solve this problem at the first stage is to model the devices. Using the simulation tools of graphical environment of simulation modelling Simulink from Matlab software environment, the software model of the communication channel between the railroad and the input devices of automatic locomotive signalling system was constructed. The created mathematical model with the actual parameters allows us to obtain diagnostic signs of a proper condition, on the basis of which the research is aimed at the identification, recognition and definition of various types of malfunctions, failure, damages and defects in the work of the constituent elements of the system and the signal transmission channel of the automatic locomotive signalling system.
45
Lenné, Michael G., Christina M. Rudin-Brown, Jordan Navarro, Jessica Edquist, Margaret Trotter, and Nebojsa Tomasevic. "Driver behaviour at rail level crossings: Responses to flashing lights, traffic signals and stop signs in simulated rural driving." Applied Ergonomics 42, no. 4 (May 2011): 548–54. http://dx.doi.org/10.1016/j.apergo.2010.08.011.
Повний текст джерела
46
McCarthy, Christine. ""From over-sweet cake to wholemeal bread": the Home & Building years: New Zealand Architecture in the 1940s." Architectural History Aotearoa 5 (October 31, 2008): 1–12. http://dx.doi.org/10.26686/aha.v5i0.6760.
Повний текст джерелаАнотація:
In 1940, when Helen Gosset, writing for the New Zealand Home & Building, asked her readers to "[a]nalyze for a moment the intricate exterior design which meets one's eye from the streets of a modern city," she gave a vivid account of urban life of that decade:A complexity of motor wheels, iron girders, tall window - dotted buildings, flashing electric signs, vivid shop windows, traffic signals, and as a back drop for all this, the bustle of modern industry. These things make up the lives of moderns. Is it any wonder that they find a certain comfort in straight lines and the absence of ornament?
47
Prince Chugh and Ajay Kaushik. "Lane Detection for Self-Driven Vehicles." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 4, 2020): 98–102. http://dx.doi.org/10.46501/ijmtst061219.
Повний текст джерелаАнотація:
For vehicles to have the option to drive without help from anyone else, they have to comprehend their encompassing world like human drivers, so they can explore their way in roads, delay at stop signs and traffic signals, and try not to hit impediments, for example, different vehicles and people on foot. In light of the issues experienced in identifying objects via self-sufficient vehicles an exertion has been made to exhibit path discovery utilizing OpenCV library. The explanation and methodology for picking grayscale rather than coloring, identifying edges in a picture, choosing area of interest, applying Hough Transform and picking polar directions over Cartesian directions has been talked about.
48
Aarushi Mittal and Narinder Kaur. "Lane Detection for Autonomous Vehicles using Open CV Library." International Journal for Modern Trends in Science and Technology 6, no. 12 (December 5, 2020): 176–80. http://dx.doi.org/10.46501/ijmtst061234.
Повний текст джерелаАнотація:
For vehicles to have the option to drive without anyone else, they have to comprehend their encompassing world like human drivers, so they can explore their way in roads, pause at stop signs and traffic signals, and try not to hit impediments, for example, different vehicles and pedestrians. In view of the issues experienced in identifying objects via self-governing vehicles an exertion has been made to show path discovery utilizing OpenCV library. The explanation and method for picking grayscale rather than shading, distinguishing and detecting edges in an image, selecting region of interest, applying Hough Transform and choosing polar coordinates over Cartesian coordinates has been discussed.
49
Adedeji, Jacob Adedayo, Xoliswa Feikie, Thywill Cephas Dzogbewu, and Mohamed Mostafa. "Reaction behaviour of drivers to marked and unmarked road: Ghana perspective." Put i saobraćaj 67, no. 1 (March 22, 2021): 1–6. http://dx.doi.org/10.31075/pis.67.01.01.
Повний текст джерелаАнотація:
Africa is the leading continent globally in the rate of road traffic fatalities, yet it is the least motorized compared to the other five continents. This predicament is said to be one of the leading cause of death among youth and generally, rated as one of the ten causes of death in the world. Exclusively, Ghana’s rate of traffic fatalities is growing despite the efforts invested in reducing it. Nevertheless, more focus needs to be invested in the traffic control systems such as traffic signals, signs or road markings. As this system tends to considerably reduce the number of conflicts and minimize road user’s errors. Furthermore, this system creates drivers’ expectations of the conditions which they will meet ahead and the driving tasks required. If misleading information is provided, or none is available, hazardous situations can result. Overall, this traffic system is inadequate or lacking in most developing countries as there are no proper maintenance strategies in place. Thus, this study investigates and evaluates the reaction of drivers to the marked and unmarked roads. Using random quantitative sampling methods, Ghanaian drivers were interviewed on their experiences when driving on the marked and unmarked road. Overall, this study will highlight the necessity of road markings in reducing traffic fatality rate and the psychological effect of the unavailability of road marking on drivers’ expectation and consequently, the effect on their behaviour in most developing countries.
50
Hunter, William W. "Evaluation of Innovative Bike-Box Application in Eugene, Oregon." Transportation Research Record: Journal of the Transportation Research Board 1705, no. 1 (January 2000): 99–106. http://dx.doi.org/10.3141/1705-15.
Повний текст джерелаАнотація:
An innovative “bike box”—a right-angle extension to a bike lane (BL) at the head of the intersection—was installed with accompanying traffic signs but no extra traffic signals at a busy downtown intersection featuring two one-way streets in Eugene, Oregon, in summer 1998. The box allows bicyclists traveling to the intersection in a left side BL to get to the head of the traffic queue on a red traffic signal indication and then proceed ahead of motor vehicle traffic toward a right side BL when the traffic signal changes to green. Cyclists traveling through the intersection were videotaped before and after placement of the box. The videotapes were coded to evaluate operational behaviors and conflicts with motorists, other bicyclists, and pedestrians. Twenty-two percent of the bicyclists who approached in the left side BL and then crossed to the BL on the right side of the street (the bicyclists for whom the box was most intended) used the box. Many more bicyclists in this target group could have used the box (i.e., they had a red signal indication and enough time to move into the box). A problem with motor vehicle encroachments into the box likely diminished the frequency of use. The rate of conflicts between bicycles and motor vehicles changed little in the before and after periods. No conflicts took place while the bike box was being used as intended.