Journal articles on the topic 'Traffic safety Data processing'
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1
Wiwik, Budiawan, Singgih Saptadi, and Ary Arvianto. "The Development of Data Warehouse to Support Data Mining Technique for Traffic Accident Prediction." E3S Web of Conferences 73 (2018): 12007. http://dx.doi.org/10.1051/e3sconf/20187312007.
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Traffic accidents are one of the major health problems that cause serious death in the world and ranks 9th in the world. Traffic accidents in Indonesia ranks 5th in the world. One effort to improve traffic safety is to design traffic accident prediction models. Prediction models will utilize accident-related data in traffic through data mining processing. The data warehouse offers benefits as a basis for data mining. Building an effective data warehouse requires knowledge and attention to key issues in database design, data acquisition and processing, as well as data access and security. This study is the first step in the development of data mining accidents based prediction system. The output of this initial stage is the design of data warehouses that can provide periodic and incidental data to the data mining process, especially in the prediction of accidents. The method used to design data warehouse is Entity Relationship Diagram (ERD).
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Sun, Yuan, Hao Xu, Jianqing Wu, Jianying Zheng, and Kurt M. Dietrich. "3-D Data Processing to Extract Vehicle Trajectories from Roadside LiDAR Data." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 45 (June 8, 2018): 14–22. http://dx.doi.org/10.1177/0361198118775839.
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High-resolution vehicle data including location, speed, and direction is significant for new transportation systems, such as connected-vehicle applications, micro-level traffic performance evaluation, and adaptive traffic control. This research developed a data processing procedure for detection and tracking of multi-lane multi-vehicle trajectories with a roadside light detection and ranging (LiDAR) sensor. Different from existing methods for vehicle onboard sensing systems, this procedure was developed specifically to extract high-resolution vehicle trajectories from roadside LiDAR sensors. This procedure includes preprocessing of the raw data, statistical outlier removal, a Least Median of Squares based ground estimation method to accurately remove the ground points, vehicle data clouds clustering, a principle component-based oriented bounding box method to estimate the location of the vehicle, and a geometrically-based tracking algorithm. The developed procedure has been applied to a two-way-stop-sign intersection and an arterial road in Reno, Nevada. The data extraction procedure has been validated by comparing tracking results and speeds logged from a testing vehicle through the on-board diagnostics interface. This data processing procedure could be applied to extract high-resolution trajectories of connected and unconnected vehicles for connected-vehicle applications, and the data will be valuable to practices in traffic safety, traffic mobility, and fuel efficiency estimation.
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Cai, Xiaoyu, Cailin Lei, Bo Peng, Xiaoyong Tang, and Zhigang Gao. "Road Traffic Safety Risk Estimation Method Based on Vehicle Onboard Diagnostic Data." Journal of Advanced Transportation 2020 (February 26, 2020): 1–13. http://dx.doi.org/10.1155/2020/3024101.
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Currently, research on road traffic safety is mostly focused on traffic safety evaluations based on statistical indices for accidents. There is still a need for in-depth investigation on preaccident identification of safety risks. In this study, the correlations between high-incidence locations for aberrant driving behaviors and locations of road traffic accidents are analyzed based on vehicle OBD data. A road traffic safety risk estimation index system with road traffic safety entropy (RTSE) as the primary index and rapid acceleration frequency, rapid deceleration frequency, rapid turning frequency, speeding frequency, and high-speed neutral coasting frequency as secondary indices is established. A calculation method of RTSE is proposed based on an improved entropy weight method. This method involves three aspects, namely, optimization of the base of the logarithm, processing of zero-value secondary indices, and piecewise calculation of the weight of each index. Additionally, a safety risk level determination method based on two-step clustering (density and k-means clustering) is also proposed, which prevents isolated data points from affecting safety risk classification. A risk classification threshold calculation method is formulated based on k-mean clustering. The results show that high-incidence locations for aberrant driving behaviors are consistent with the locations of traffic accidents. The proposed methods are validated through a case study on four roads in Chongqing with a total length of approximately 38 km. The results show that the road traffic safety trends characterized by road safety entropy and traffic accidents are consistent.
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Zhang, Qiuchen, Zhen Yang, and Dazhi Sun. "Automated Data Collection and Safety Analysis at Intersections Based on a Novel Video Processing System." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 4 (April 2019): 136–44. http://dx.doi.org/10.1177/0361198119838979.
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A novel video-based system is presented which collects trajectories and motion parameters of all objects at intersections. First, a modified ViBe method is used to extract the foreground of moving objects. Then, an object-point-contour matching approach is developed for pairing, tracking, and generating trajectories. Finally, raw trajectories are corrected through post-processing and motion parameters are estimated after object classification. This system demonstrates better performance while tracking tardy and shadowed objects compared with previous studies. The accuracies of 86% and 91% are obtained for traffic counts and velocity validation, respectively. This paper also presents a sample safety analysis using traffic conflict technology to demonstrate the possible implementation for traffic management and safety analysis.
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B, Moh Baktiar, St Maryam, and Lambang Basri Said. "Moderasi Variabel Penegakan Hukum Berlalulintas Terhadap Pengaruh Disiplin Dan Keselamatan Berlalu Lintas Di Kabupaten Pinrang." INTEK: Jurnal Penelitian 6, no. 1 (May 25, 2019): 52. http://dx.doi.org/10.31963/intek.v6i1.1125.
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Abstract—This study aims to analyze the influence of trafficdiscipline and enforcement of traffic law on traffic safety inPinrang Regency and analyze the relationship between lawenforcement moderation and the influence of traffic discipline ontraffic safety in Pinrang Regency. Data collection techniques usedin this study were questionnaires, measurement of variables inthis study using a Likert scale. The data processing in this studyuses the SmartPLS3 Program. Valid data to be sampled are asmany as 100 taken by Slovin techniques, the sample of this studyis the people in Pinrang Regency. Based on the results of thisstudy concluded that traffic discipline and traffic lawenforcement have a positive effect on traffic safety in the districtof Pinrang and law enforcement does not mediate thestrengthening of the influence of traffic discipline on the trafficsafety of road users.
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Dyduch, Janusz, and Mieczysław Kornaszewski. "Selection of exploitation data from railway traffic control devices for the purposes of gathering and analyzing data processes from railway transport objects." Transportation Overview - Przeglad Komunikacyjny 2019, no. 5 (May 1, 2019): 43–53. http://dx.doi.org/10.35117/a_eng_19_05_04.
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The evaluation of the technical systems reliability and safety requires collecting and processing of reliable data which characterizes the processes. The data from current exploitation is particularly important for decision-making processes. It can be used for creation of occurring exploitation phenomena models and allows to determine the expected object behaviour in the future. The railway traffic control devices often work in very difficult exploitation and environmental conditions. The information about their technical condition can be gathered and used for a proper prophylaxis as well as a predictive maintenance of railway traffic. It will allow to choose a maintenance strategy which will consist in optimal use of railway traffic control devices.
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Bui, Ngoc Dung, Dinh Tran Ngoc Huy, and Tuan Thanh Nguyen. "Application of traffic conflict technique for traffic safety evaluation at intersection based on image processing." LAPLAGE EM REVISTA 7, no. 3A (September 2, 2021): 134–42. http://dx.doi.org/10.24115/s2446-6220202173a1379p.134-142.
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Traffic accidents occur frequently at intersections area because of conflicts among vehicles. Many researchers have been assessing traffic safety, however most of them are based on accident data that happened and caused injury as well as economic damage. Recently, conflict techniques provide general views and solutions to prevent early collisions. This paper proposes a method of conflict analysis using image processing technique and fuzzy comprehensive evaluation. Based on vehicles detection, the conflict parameters collected from two intersections in Hanoi, Vietnam were processed and evaluated by fuzzy comprehensive evaluation to give out the safety level of conflict points. The experimental results show that the proposed methods have successfully shown the distribution and location of dangerous conflict points according to the actual situation of the two intersections. Based on our results, authorities can consider reorganizing traffic light pattern at these intersections to reduce possible collisions.
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Kim and Lee. "Adaptive Information Visualization for Maritime Traffic Stream Sensor Data with Parallel Context Acquisition and Machine Learning." Sensors 19, no. 23 (November 29, 2019): 5273. http://dx.doi.org/10.3390/s19235273.
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Excessive information significantly increases the mental burden on operators of critical monitoring services such as maritime and air traffic control. In these fields, vessels and aircraft have sensors that transmit data to a control center. Because of the large volume of collected data, it is infeasible for monitoring stations to display all of the information on monitoring screens that have limited sizes. This paper proposes a method for automatically selecting maritime traffic stream data for display from a large number of candidates in a context-aware manner. Safety is the most important concern in maritime traffic control, and special care must be taken to avoid collisions between vessels at sea. It presents an architecture for an adaptive information visualization system for a maritime traffic control service. The proposed system adaptively determines the information to be displayed based on the safety evaluation scores and expertise of vessel traffic service operators. It also introduces a method for safety context acquisition to assess the risk of collisions between vessels, using parallel and distributed processing of maritime stream data transmitted by sensors on the vessels at sea. It provides an information-filtering, knowledge extraction method based on the work logs of traffic service operators, using a machine learning technique to generate a decision tree. We applied the proposed system architecture to a large dataset collected at a port. Our results indicate that the proposed system can adaptively select traffic information according to port conditions and to ensure safety and efficiency.
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II Kim, Kwang, Keon Myung Lee, and Jang Young Ahn. "Methods of ship trajectory data processing for applying artificial neural network in port area." International Journal of Engineering & Technology 7, no. 2.12 (April 3, 2018): 145. http://dx.doi.org/10.14419/ijet.v7i2.12.11112.
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Background/Objectives: In Vessel Traffic Service (VTS), prediction of the flow of vessel traffic is essential to serve safety information and control ship traffic. However, it is difficult to predict a ship’s speed due to many external forces and environmental conditions. This study proposes a data processing method to convert ship speed data to categorical data by dividing ship navigating routes into several gate lines.Methods/Statistical analysis: A ship’s trajectory is converted to each route’s gate line speed. To determine the gate line speed, we convertedthe previous and subsequent gate line speeds into category data. The input and output category data were applied to a multilayer perceptron network using as input variablesthe previous speed variance category, ship type, and ship length, and as output variable the subsequent speed variance.Findings: These results are useful because categorical data can be applied to various neural network models. As a result of the conducted experiments, the accuracy of the model improved when many gate lines are included.Improvements/Applications: The study results can be applied topredict ship traffic flow for VTS operators.
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Kim, Yonghoon, Jun-Ho Huh, and Mokdong Chung. "Traffic Inference System Using Correlation Analysis with Various Predicted Big Data." Electronics 10, no. 3 (February 2, 2021): 354. http://dx.doi.org/10.3390/electronics10030354.
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Currently, most of the transportation systems require changes to intelligent transportation systems, but most of them focus on efficient transportation rather than on improvement in human life. Sometimes, traffic systems are designed for economic value, and safety-related issues are neglected. A traffic information system that reflects various kinds of environmental information related to people’s safety must be able to reflect not only the existing economic goals but also a safe traffic environment. The traffic environment can be thought of as safety and direct information such as rainfall, including information on specific days when many people are scheduled to be gathered for certain events nearby. Intelligent transportation systems using this information can provide safety-related information for traveling to a specific area or for business trips. In addition, traffic congestion is a social problem and is directly related to a comfort life for individuals. Therefore, addressing various social and environmental factors could make human life more stable and reduce stress as a result. To do that, we need to estimate the impact on traffic based on environmental Big Data. The data can generally be divided into structured data and unstructured data. In inference, structured data analysis is relatively easy due to the precise meaning of the data. Nonetheless, it can be very difficult to predict environmentally sensitive data, such as traffic volume in intelligent transportation systems. To cope with this problem, there are a few systems for handling unstructured data to find out specific events that affect the traffic volume and improve its reliability. This paper shows that it is possible to estimate the exact volume of traffic using correlation analysis with various predicted data. Thus, we may apply this technique to the existing intelligent transportation system to predict the exact volume of traffic with environmentally sensitive data including various unstructured data.
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Dontu, A. I., L. Gaiginschi, and A. Sachelarie. "Automatically collecting data traffic in intersections by using video analytics software for vehicle counting." IOP Conference Series: Materials Science and Engineering 1262, no. 1 (October 1, 2022): 012063. http://dx.doi.org/10.1088/1757-899x/1262/1/012063.
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Traffic volume and composition data are important for infrastructure planning and increase traffic safety. The actual survey techniques depend on the goals of the survey, the amount of traffic data and the human and financial resources available. The purpose of this research is to improve classical collecting data by creating an automatically collecting data traffic with the help of a video analytics software. Authors present in this paper a new method for automatically collecting data traffic using Camlytics, a video analytics software, which is automatic processing the videos. This method is less time consuming, don’t need a significant human resource and it is easy to apply. Also, this new method for counting the traffic may be used by the municipality for making a better fluidisation of traffic in the city by corelating the data traffic with the Intelligent Transportation Systems (I.T.S.).
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Soilán, M., B. Riveiro, A. Sánchez-Rodríguez, and L. M. González-deSantos. "APPLICATION OF MLS DATA TO THE ASSESSMENT OF SAFETY-RELATED FEATURES IN THE SURROUNDING AREA OF AUTOMATICALLY DETECTED PEDESTRIAN CROSSINGS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2 (May 30, 2018): 1067–74. http://dx.doi.org/10.5194/isprs-archives-xlii-2-1067-2018.
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During the last few years, there has been a huge methodological development regarding the automatic processing of 3D point cloud data acquired by both terrestrial and aerial mobile mapping systems, motivated by the improvement of surveying technologies and hardware performance. This paper presents a methodology that, in a first place, extracts geometric and semantic information regarding the road markings within the surveyed area from Mobile Laser Scanning (MLS) data, and then employs it to isolate street areas where pedestrian crossings are found and, therefore, pedestrians are more likely to cross the road. Then, different safety-related features can be extracted in order to offer information about the adequacy of the pedestrian crossing regarding its safety, which can be displayed in a Geographical Information System (GIS) layer. These features are defined in four different processing modules: Accessibility analysis, traffic lights classification, traffic signs classification, and visibility analysis. The validation of the proposed methodology has been carried out in two different cities in the northwest of Spain, obtaining both quantitative and qualitative results for pedestrian crossing classification and for each processing module of the safety assessment on pedestrian crossing environments.
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Xu, Chen, Decun Dong, Dongxiu Ou, and Changxi Ma. "Time-of-day Control Double-Order Optimization of Traffic Safety and Data-Driven Intersections." International Journal of Environmental Research and Public Health 16, no. 5 (March 9, 2019): 870. http://dx.doi.org/10.3390/ijerph16050870.
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This paper proposes a novel two-order optimization model of the division of time-of-day control segmented points of road intersection to address the limitations of the randomness of artificial experience, avoid the complex multi-factor division calculation, and optimize the traditional model over traffic safety and data-driven methods. For the first-order optimization—that is, deep optimization of the model input data—we first increase the dimension of traditional traffic flow data by data-driven and traffic safety methods, and develop a vector quantity to represent the size, direction, and time frequency with conflict point traffic of the total traffic flow at a certain intersection for a period by introducing a 3D vector of intersection traffic flow. Then, a time-series segmentation algorithm is used to recurse the distance amongst adjacent vectors to obtain the initial scheme of segmented points, and the segmentation points are finally divided by the combination of the preliminary scheme. For the second-order optimization—that is, model adaptability analysis—the traffic flow data at intersections are subjected to standardised processing by five-number summary. The different traffic flow characteristics of the intersection are categorised by the K central point clustering algorithm of big data, and an applicability analysis of each type of intersection is conducted by using an innovated piecewise point division model. The actual traffic flow data of 155 intersections in Yuecheng District, Shaoxing, China, in 2016 are tested. Four types of intersections in the tested range are evaluated separately by the innovated piecewise point division model and the traditional total flow segmentation model on the basis of Synchro 7 simulation software. It is shown that when the innovated double-order optimization model is used in the intersection according to the ‘hump-type’ traffic flow characteristic, its control is more accurate and efficient than that of the traditional total flow segmentation model. The total delay time is reduced by approximately 5.6%. In particular, the delay time in the near-peak-flow buffer period is significantly reduced by approximately 17%. At the same time, the traffic accident rate has also dropped significantly, effectively improving traffic safety at intersections.
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Steinbergs, Raitis, and Maris Kligis. "Improving Traffic Safety By Using Waze User Reports." IOP Conference Series: Materials Science and Engineering 1202, no. 1 (November 1, 2021): 012031. http://dx.doi.org/10.1088/1757-899x/1202/1/012031.
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Abstract Road inspection regularity and existing types made by road maintenance crew have not been good enough to be aware what is really happening on the roads. Road users' contribution in road traffic safety is very important to ensure fast reaction on different road hazards. It is important to ensure not only the most common ways to report road hazards on state roads by phone, by email and on social media, but also expand data sources options in modern and user-friendly way. Waze navigation application already had functionality to report road hazards – to warn other application users, but no one acted to solve these road hazards until someone reported them through existing communication channels supported by Latvian State roads or Latvian road maintainer. To ensure better road traffic safety and faster reaction time on road hazards solving, Latvian road maintainer gained access to Waze report feed, and, in corporation with Riga Technical university, made a system for analysing and processing Waze data. As the result - Latvian roads maintainer can improve road safety by faster reaction to road hazards reported by Waze users. Today, up to 70 % from total reports processed by Latvian road maintainer are generated by Waze.
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Меженков and A. Mezhenkov. "EXPERIMENTAL VERIFICATION OF THE CRITERION FOR EVALUATING TRAFFIC SAFETY AT REGULATE CROSSROADS." Alternative energy sources in the transport-technological complex: problems and prospects of rational use of 2, no. 2 (December 17, 2015): 666–71. http://dx.doi.org/10.12737/19494.
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In work substantiates and defines the object of experimental research, which is the transport flows at intersections with traffic light regulation with strict software control. After the collection and processing of source data have been identified according the criteria of safety assessment statistics of road accident. Based on the results of experimental research, clarified the criterion for evaluating traffic safety at regulate crossroads, the characteristics of the interaction of transport flows on them
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Zamyshliaev, A. М. "Premises of the creation of a digital traffic safety management system." Dependability 19, no. 4 (December 17, 2019): 45–52. http://dx.doi.org/10.21683/1729-2646-2019-19-4-45-52.
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Aim.The digital transformation of the traffic safety management system in JSC RZD involves top-level integration with the operating processes of all business units in terms of integral assessment of the risk of possible events and achievement of specified indicators. The result will be the merger of the traffic safety management system with the processes of all levels of the company’s management enabled by an integrated intelligent system for managing processes and services whose functionality includes real-time traffic safety management.Methods. The paper uses system analysis of existing approaches and methods of processing of large quantities of structured and unstructered data.Results. The paper examines the development stages of train traffic safety management, as well as automated information and control systems that enable traffic safety management. General trends in the creation of systems for collection and processing of information are analyzed. The applicability of such technologies as Big Data, Data Mining, Data Science as part of advanced control systems is shown. The paper examines the performance of the above technologies by analyzing the effect of various factors on the average daily performance of a locomotive, where, at the first level, such factors as average daily run of a locomotive, average trainload are taken into consideration; at the second level, the focus is on the service speed, locomotive turnover at station, etc.; at the sixth level, the focus is on the type of locomotive, its technical state, etc. It is shown that statistical methods of factor analysis and link analysis combined with such other methods of Data Mining as methods of simulation and prediction, the average daily performance of a locomotive can be planned proactively. The author proposes a procedure of migration towards a digital traffic safety management system that would be based on models of interaction of safety and dependability factors of all railway facilities at all railway levels of hierarchy, as well as in association with other factors that have no direct relation to dependability, yet affect the safety of the transportation process.Conclusions. The primary benefit of migration towards Big Data consists in the development of a dynamic model of traffic safety, the elimination of human factor in control systems. Most importantly, it enables the creation within the Russian Railways company (JSC RZD) of an integrated intelligent process and service management system that enables real-time traffic safety management. An extensive process of development and deployment within the company of the URRAN Single Corporate Platform (SCP) enabled executive decision support as regards risk-based functional dependability and safety of transportation facilities. Thus, the URRAN SCP sets the stage for the digital transformation of the traffic safety management system in JSC RZD.
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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.
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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.
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Jamaludin, Ahmad Shahir, Ahmad Noor Syukri Zainal Abidin, Mohd Nizar Muhd Razali, Azzuhana Roslan, Roziana Shahril, Zulhaidi Mohd Jawi, and Khairil Anwar Abu Kassim. "Potential Application of Artificial Neural Network (ANN) Analysis Method on Malaysian Road Crash Data." Journal of Modern Manufacturing Systems and Technology 5, no. 2 (August 25, 2021): 95–105. http://dx.doi.org/10.15282/jmmst.v5i2.6706.
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By allowing the movement of commodities and people, road transportation benefits both nations and people. This provides improved access to work opportunities, educational attainment, recreation, and healthcare, all of which have a direct and indirect influence on people. The influence on road transportation, on the other hand, has a detrimental impact on people's health. When addressing road traffic accidents, it is common known that it has merely become a global pandemic, with over a million people dying on the road each year. Malaysia, as a growing country, has identified road safety as a major issue that must be addressed. Reliable road safety statistics are critical for comprehending, assessing, and monitoring the nature and scope of the road safety problem and its solutions, for setting ambitious but realistic safety targets, for designing and implementing effective road safety policies, and for monitoring their success. Several approaches are presently utilized by road safety researchers to produce road safety indicators. In Malaysia, nearly all decisions made by the country's higher authorities to enhance road safety are based on data supplied by relevant stakeholders. As a result, having the proper application of analysis as well as the trustworthiness of the data itself is critical. This article will give a review of the possible use of the Artificial Neural Network (ANN) Analysis technique on traffic road collision data and what it may provide to assist monitor or forecast road safety issues, specifically in Malaysia. A new era in the field of road accident investigation is being ushered in by the development and application of analytical methodologies, which are creating previously unimaginable situations. Due to the convergence of recent advancements in accident research models and the availability of potentially new sources of traffic data, this paradigm shift has been made possible. The study of road crashes has benefited significantly from the development of more advanced data processing methodologies and frameworks, thus the researchers will able to extract significant conclusions from the study of traffic data thanks to the application of these approaches.
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Wang, Xingmin, Shengyin Shen, Debra Bezzina, James R. Sayer, Henry X. Liu, and Yiheng Feng. "Data Infrastructure for Connected Vehicle Applications." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 5 (April 9, 2020): 85–96. http://dx.doi.org/10.1177/0361198120912424.
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Ann Arbor Connected Vehicle Test Environment (AACVTE) is the world’s largest operational, real-world deployment of connected vehicles (CVs) and connected infrastructure, with over 2,500 vehicles and 74 infrastructure sites, including intersections, midblocks, and highway ramps. The AACVTE generates a massive amount of data on a scale not seen in the traditional transportation systems, which provides a unique opportunity for developing a wide range of connected vehicle (CV) applications. This paper introduces a data infrastructure that processes the CV data and provides interfaces to support real-time or near real-time CV applications. There are three major components of the data infrastructure: data receiving, data pre-processing, and visualization including the performance measurements generation. The data processing algorithms include signal phasing and timing (SPaT) data compression, lane phase mapping identification, trajectory data map matching, and global positioning system (GPS) coordinates conversion. Simple performance measures are derived from the processed data, including the time–space diagram, vehicle delay, and observed queue length. Finally, a web-based interface is designed to visualize the data. A list of potential CV applications including traffic state estimation, traffic control, and safety, which can be built on this connected data infrastructure is discussed.
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Babar, Muhammad, Muhammad Usman Tariq, Ahmed S. Almasoud, and Mohammad Dahman Alshehri. "Privacy-Aware Data Forensics of VRUs Using Machine Learning and Big Data Analytics." Security and Communication Networks 2021 (November 28, 2021): 1–9. http://dx.doi.org/10.1155/2021/3320436.
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The present spreading out of big data found the realization of AI and machine learning. With the rise of big data and machine learning, the idea of improving accuracy and enhancing the efficacy of AI applications is also gaining prominence. Machine learning solutions provide improved guard safety in hazardous traffic circumstances in the context of traffic applications. The existing architectures have various challenges, where data privacy is the foremost challenge for vulnerable road users (VRUs). The key reason for failure in traffic control for pedestrians is flawed in the privacy handling of the users. The user data are at risk and are prone to several privacy and security gaps. If an invader succeeds to infiltrate the setup, exposed data can be malevolently influenced, contrived, and misrepresented for illegitimate drives. In this study, an architecture is proposed based on machine learning to analyze and process big data efficiently in a secure environment. The proposed model considers the privacy of users during big data processing. The proposed architecture is a layered framework with a parallel and distributed module using machine learning on big data to achieve secure big data analytics. The proposed architecture designs a distinct unit for privacy management using a machine learning classifier. A stream processing unit is also integrated with the architecture to process the information. The proposed system is apprehended using real-time datasets from various sources and experimentally tested with reliable datasets that disclose the effectiveness of the proposed architecture. The data ingestion results are also highlighted along with training and validation results.
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Yashina, Marina V., Alexey I. Mokhov, Maria A. Belova, Alexey V. Kostsov, and Pavel I. Pospelov. "On-Board Video-Evaluation Algorithm of Transverse Safety Clearance for Ahead Road-Vehicle." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 10 (June 30, 2020): 128. http://dx.doi.org/10.3991/ijim.v14i10.14619.
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<p class="0abstract">Traffic flows are becoming more intense over time as a result of global automobilization. Road transport specialists are developing and analyzing different approaches to control traffic, to design and to build new highways. Decreasing total amount of accidents and congestion avoidance on a road are the main goals of this research. General traffic flow features and car localization are the most important types of data to be obtained, processed and analyzed in modern conditions as cars are highly maneuverable. Proper usage of this data allows building new high-quality traffic control systems taking in account all of its significant features. We have developed a system for video-processing from camera fixed on car torpedo. Research presented in this paper suggests an algorithm of transverse safety clearance evaluation by analyzing a video from vehicle on-board camera.</p>
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Teng, Ying Xiang, Hu Gang Zhao, Jie Yang, and Jin Lu Sheng. "Water Traffic Safety Evaluation Based on the Grey Correlation Grade Analysis." Applied Mechanics and Materials 571-572 (June 2014): 295–98. http://dx.doi.org/10.4028/www.scientific.net/amm.571-572.295.
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Aiming at the deficiency of the water traffic safety evaluation method in our country, the water traffic safety evaluation model was established after full discussing, which is based on grey relational grade analysis. The water traffic safety status of shipping companies was evaluated through their correlation, which is on the basis of the Five index method, after the original data in a reasonable manner of dimensionless processing. Through a case of a shipping group, it is proved that the grey correlation greed analysis method can evaluate enterprise's security situation simply and effectively on the basis of original index of death toll, the number of accidents above General level, the number of ship destroyed and direct economic loss. Additionally, the evaluation is more objective and scientific.
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Wang, Chongjiao, Changrong Yao, Siguang Zhao, Shida Zhao, and Bin Qiang. "The Theory and Method of Data Acquisition of Mixed Traffic Popular People and Nonmotor Vehicles Based on Image Processing." Mobile Information Systems 2022 (April 22, 2022): 1–10. http://dx.doi.org/10.1155/2022/9699162.
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In road mixed traffic, pedestrians and nonmotor vehicles have a great impact on the driving of motor vehicles. This kind of influence not only threatens the road traffic safety but also leads to the increase of delay and the decrease of traffic capacity. The purpose of this paper is to study the theory and method of data acquisition of mixed traffic popular people and nonmotor vehicles based on image processing technology. Aiming at the problem that the basic state space model solves the phenomenon of “failure” such as mutual interference between mixed objects, this paper proposes a KF tracking model based on a fuzzy matching method to realize the effective and accurate tracking of mixed traffic objects. The experimental results show that, after extracting the morphological features of the detected pedestrian and nonmotor vehicle images and using the method of pattern recognition to classify, recognize, and count the mixed traffic objects, through the comparison of the two trajectory lines, we can see that the tracking accuracy of the algorithm is high under the mutual interference of pedestrian and nonmotor vehicle. Excluding the detection error, the pedestrian tracking error is less than 10 pixels, the average error is 2.366 pixels, the maximum error of nonmotor vehicle tracking is 19 pixels, and the average error is 2.5 pixels.
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Rahman, Mizanur, Mhafuzul Islam, Jon Calhoun, and Mashrur Chowdhury. "Real-Time Pedestrian Detection Approach with an Efficient Data Communication Bandwidth Strategy." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 6 (May 7, 2019): 129–39. http://dx.doi.org/10.1177/0361198119843255.
Full textAbstract:
Vehicle-to-pedestrian communication could significantly improve pedestrian safety at signalized intersections. However, it is unlikely that pedestrians will typically be carrying a low latency communication-enabled device with an activated pedestrian safety application in their hand-held device all the time. Because of this, multiple traffic cameras at a signalized intersection could be used to accurately detect and locate pedestrians using deep learning, and broadcast safety alerts related to pedestrians to warn connected and automated vehicles around signalized intersections. However, the unavailability of high-performance roadside computing infrastructure and the limited network bandwidth between traffic cameras and the computing infrastructure limits the ability of real-time data streaming and processing for pedestrian detection. In this paper, we describe an edge computing-based real-time pedestrian detection strategy that combines a pedestrian detection algorithm using deep learning and an efficient data communication approach to reduce bandwidth requirements while maintaining high pedestrian detection accuracy. We utilize a lossy compression technique on traffic camera data to determine the tradeoff between the reduction of the communication bandwidth requirements and a defined pedestrian detection accuracy. The performance of the pedestrian detection strategy is measured in relation to pedestrian classification accuracy with varying peak signal-to-noise ratios. The analyses reveal that we detect pedestrians by maintaining a defined detection accuracy with a peak signal-to-noise ratio 43 dB while reducing the communication bandwidth from 9.82 Mbits/sec to 0.31 Mbits/sec, a 31× reduction.
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Khan, Muhammad Arsalan, Wim Ectors, Tom Bellemans, Davy Janssens, and Geert Wets. "Unmanned Aerial Vehicle–Based Traffic Analysis: Methodological Framework for Automated Multivehicle Trajectory Extraction." Transportation Research Record: Journal of the Transportation Research Board 2626, no. 1 (January 2017): 25–33. http://dx.doi.org/10.3141/2626-04.
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Unmanned aerial vehicles (UAVs), commonly referred to as drones, are one of the most dynamic and multidimensional emerging technologies of the modern era. This technology has recently found multiple potential applications within the transportation field, ranging from traffic surveillance applications to traffic network analysis. To conduct a UAV-based traffic study, extremely diligent planning and execution are required followed by an optimal data analysis and interpretation procedure. In this study, however, the main focus was on the processing and analysis of UAV-acquired traffic footage. A detailed methodological framework for automated UAV video processing is proposed to extract the trajectories of multiple vehicles at a particular road segment. Such trajectories can be used either to extract various traffic parameters or to analyze traffic safety situations. The proposed framework, which provides comprehensive guidelines for an efficient processing and analysis of a UAV-based traffic study, comprises five components: preprocessing, stabilization, georegistration, vehicle detection and tracking, and trajectory management. Until recently, most traffic-focused UAV studies have employed either manual or semiautomatic processing techniques. In contrast, this paper presents an in-depth description of the proposed automated framework followed by a description of a field experiment conducted in the city of Sint-Truiden, Belgium. Future research will mainly focus on the extension of the applications of the proposed framework in the context of UAV-based traffic monitoring and analysis.
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Park, Sangmin, Sungho Park, Harim Jeong, Ilsoo Yun, and Jaehyun (Jason) So. "Scenario-Mining for Level 4 Automated Vehicle Safety Assessment from Real Accident Situations in Urban Areas Using a Natural Language Process." Sensors 21, no. 20 (October 19, 2021): 6929. http://dx.doi.org/10.3390/s21206929.
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As the research and development activities of automated vehicles have been active in recent years, developing test scenarios and methods has become necessary to evaluate and ensure their safety. Based on the current context, this study developed an automated vehicle test scenario derivation methodology using traffic accident data and a natural language processing technique. The natural language processing technique-based test scenario mining methodology generated 16 functional test scenarios for urban arterials and 38 scenarios for intersections in urban areas. The proposed methodology was validated by determining the number of traffic accident records that can be explained by the resulting test scenarios. That is, the resulting test scenarios are valid and represent a matching rate between the test scenarios and the increased number of traffic accident records. The resulting functional scenarios generated by the proposed methodology account for 43.69% and 27.63% of the actual traffic accidents for urban arterial and intersection scenarios, respectively.
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Ferreira De Menezes, Daniel, Eduardo Da Silva Felix, Jaqueline Silva De Souza Pinheiro, and Zaida Maria Marques Tavares. "IMPACTS OF TECHNOLOGY IN MANUAL PROCESSES IN THE MIDST OF ORGANIZATIONAL SECURITY." International Journal of Advanced Research 10, no. 11 (November 30, 2022): 1271–80. http://dx.doi.org/10.21474/ijar01/15790.
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The article aims to study a military organization and its management of vehicle and pedestrian traffic in the old format, both collection and processing and storage of this data, and propose a solution that facilitates such processes through a mobile application through the android operating system and the use of technologies belonging to smartphones, such as cameras, QRcode for a complete and enjoyable experience for both users and people responsible for the organizational management of the site. Usability tests were performed in the field where the user was allowed to have access to the application in full operation to generate traffic data during its use. Finally, it is concluded that the application provided better management, safety, comfort and better data processing.
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Lee, Jeong-Seok, Woo-Ju Son, Hyeong-Tak Lee, and Ik-Soon Cho. "Verification of Novel Maritime Route Extraction Using Kernel Density Estimation Analysis with Automatic Identification System Data." Journal of Marine Science and Engineering 8, no. 5 (May 24, 2020): 375. http://dx.doi.org/10.3390/jmse8050375.
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A maritime route is used by sea transportation vessels to access the trading ports, and route design standards for the safety of maritime traffic have been established in various countries and organizations. However, no quantitative safety verification method related to route design currently exists. In this study, a novel maritime route was created and compared with the original route in Incheon, the Republic of Korea, based on the relevant automatic identification system (AIS) data. The attendant traffic density was revealed via kernel density estimation analysis of the AIS data, with the results used to create the boundary of the novel route through an image processing technique. The boundary and the centerline of the maritime route were determined using a line smoothing technique. For safety verification, the centerline of the original route and that of the novel maritime route were compared in terms of sinuosity, intersection angle, and route change envelope (RCE). The sinuosity analysis demonstrated that the route was stable in terms of the outer harbor limit, while the intersection angle analysis demonstrated that the novel maritime route intersection angle was stable. The RCE was used to objectively compare the absolute values of the distance change in the centerline.
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Behbahani, Hamid, Sayyed Mohsen Hosseini, Alireza Taherkhani, Hemin Asadi, and Seyed Alireza Samerei. "Proposing New Methods to Estimate the Safety Level in Different Parts of Freeway Interchanges." Advances in Civil Engineering 2018 (2018): 1–17. http://dx.doi.org/10.1155/2018/8702854.
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Since attention to the safety of traffic facilities including freeway interchanges has been increased during recent years, accident prediction models are being developed. Simulation-based surrogate safety measures (SSMs) have been used in the absence of real collision data. But, obtaining different outputs from different SSMs as safety indicators had led to a complexity of using them as the collision avoidance system basis. Additionally, applying SSM requires trajectory data which can be hardly obtained from video processing or calibrated microsimulations. Estimating safety level in different parts of freeway interchanges through a new proposed method was considered in this paper. Fuzzy logic was applied to combine the outputs of different SSMs, and an index called no-collision potential index (NCPI) was defined. 13608 calibrated simulations were conducted on different ramps, weaving, merge, and diverge areas with different geometrical and traffic characteristics, and NCPI was determined for every case. The geometrical and traffic characteristics formed input data of two safety estimator models developed by Artificial Neural Network and Particle Swarm Optimization. Ten freeway interchanges were investigated to calibrate the simulations and to ensure the validity of the fuzzy method and accuracy of the models. Results showed an appropriate and accurate development of the models.
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Olumuyiwa, Olugbenga, and Yuhua Chen. "Virtual CANBUS and Ethernet Switching in Future Smart Cars Using Hybrid Architecture." Electronics 11, no. 21 (October 23, 2022): 3428. http://dx.doi.org/10.3390/electronics11213428.
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Smart cars have gained much attention in recent years due to the introduction of several safety and convenience features. In this paper, we propose a virtual CANBUS architecture that will improve the safety and data processing in future smart cars with the hybrid use of Ethernet technology deployed in conjunction with a CANBUS system to take advantage of the virtualization, speed, and quality of data processing. Data will be routed intelligently across the dual data paths of the traditional CANBUS and the Ethernet. The virtualized nature, with the help of a series of smart nodes and network traffic analyzers, will allocate the needed resources at the right time during the execution of different processes. This enables the possibility of routing data traffic over both Ethernet and CANBUS connections. The architecture is backward compatible with older vehicles and therefore takes advantage of the existing CANBUS system. The proposed architecture ensures that different segments are isolated from each other so that a breakdown in a segment does not bring down the entire system. The experimental results demonstrate the benefits of the proposed solution, which is to switch between two data pathways depending on the traffic loads. While the CANBUS is sufficient with low-bandwidth data, the Ethernet will create a better performance with high-bandwidth processes. The virtualized environment creates virtual topologies among communicating nodes, greatly simplifying the network management and enhancing the data traffic performance as the bandwidth requirement and the number of processors in future smart cars continue to scale.
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Benaissa, Khireddine, Salim Bitam, and Abdelhamid Mellouk. "BSM-Data Reuse Model Based on In-Vehicular Computing." Applied Sciences 10, no. 16 (August 6, 2020): 5452. http://dx.doi.org/10.3390/app10165452.
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Basic Safety Messages that are frequently generated from multiple connected vehicles can play a primordial role in providing transport data see credible and reliable information they contain. Otherwise, when considering the way Basic Safety Messages (BSMs) are treated, multiple deficiencies prevent the latter to be capable of constituting a precious data source. As we know, data become more useful the more widely are used, which is the exact opposite of what happens with the BSMs that exist only temporarily, used locally, considered disposable, and are never stored. In this paper, we introduce a data reuse model that retains collected BSMs, stores, and processes them inside the vehicle constituting a continuous data source holding retained snapshots along the roadway. Our model provided a primary data source available on a large scale, considered to be a worthy dataset for machine learning tasks, capable of visualizing different traffic-related indicators to enhance analytics and support decisions-making. In the study case, we set up an in-vehicle data platform, where we achieved an 80% of BSMs size reduction and provided a rich set of APIs to serve applications. We also adopted the Artificial Neural Networks (ANN) as an information processing paradigm for performing traffic volume prediction, where the obtained results have reached over 99% of accuracy.
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Shestakova, E. B., and E. V. Kazaku. "Road Traffic Safety: High-Quality Welding - Ensuring Cost-Effectiveness and Safety." Occupational Safety in Industry, no. 11 (November 2021): 41–46. http://dx.doi.org/10.24000/0409-2961-2021-11-41-46.
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Welding industry is one of the leaders in the field of investments in the development of technologies and innovations aimed at ensuring functional cost-effectiveness, improving safety and speed of movement in the digital age. Ensuring the reliability and safety of the railway tracks requires the development of new scientific approaches to the use of welding technologies in the construction and operation of a high-speed railway. The main criterion for evaluating efficiency of the new approaches is the quality of the resulting welded joints and the productivity of the welding processes used. Rail connections should be made in such a way from a technical point of view that there are no restrictions during operation, the service life of the rails is not reduced, the percentage of defects does not increase, and the noise level is within the normal limits. The purpose of the study is to substantiate the cost-effectiveness and safety of the introduction of new contact welding technologies in Russia. Technical and economic analysis of welding methods is given, including using the rail welding machines — a new generation of equipment for the contact butt welding of high-strength rails during the construction and reconstruction of a high-speed railway. The Up-to-date technologies are based on the process of contact welding by pulsating reflow, on the one hand ensuring high productivity of the process, and on the other — reducing the influence of the human factor on it. This is achieved through a set of options for controlling the welding process, with online monitoring of the operational characteristics, with the software for storage and the possibility of using robotic welding in the future in the field of digital doubles and processing archived data based on the artificial intelligence as part of the implementation of Smart City projects. Feasibility study is also presented concerning the efficiency of the introduction of contact welding technology by the pulsed reflow on high-speed railway projects of Russia based on reducing the rail joint failures and improving the characteristics of the welding process and safe operation.
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Cui, Hong Wei. "Research and Design of Vehicle Dynamic Safety Warning System." Advanced Materials Research 662 (February 2013): 944–47. http://dx.doi.org/10.4028/www.scientific.net/amr.662.944.
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Vehicle dynamic safety warning system based on data fusion is researched and designed in this paper. Dynamic information of vehicle, road and environment can be collected in real time. By analyzing and processing these data, active safety warning information is achieved for individual vehicle on different work condition. Safety parameters of vehicle and road condition are detected. Warning information is transferred by wireless network in real time. Research work in this paper is applied to road traffic control area. This vehicle warning system is of value to removing incipient fault of running vehicle, improving reliability, rational maintaining and proper inspection.
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Popovych, Natalia, Olha Belenchuk, Tetyana Bondar, and Yevhen Tepliuk. "DETERMINATION OF THE ROAD SAFETY RATING FOR SELECTING THE PRIORITY OF CARRYING OUT THE ROAD SAFETY INSPECTION." Dorogi i mosti 2022, no. 25 (March 17, 2022): 222–30. http://dx.doi.org/10.36100/dorogimosti2022.25.222.
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Abstract Introduction. Compared to European countries, the level of road safety in Ukraine is extremely unsatisfactory due to high mortality and road traffic injuries. Improving road safety is one of the important social problems of our time, which is associated with the preservation of human life and health. Problem statement. The problem of road safety in Ukraine is well visible due to the number of dead and injured people on the roads. Road accidents cause huge social losses for citizens and place a heavy burden on the health care system and the economy as a whole. Reducing injuries from road accidents and saving people’s lives is one of the most important tasks for our country. Purpose. In the article the evaluation of safety level on roads of national importance will be conducted (according to certain indicators), which allows to make a conclusion about the compliance of the road network or individual sections of roads with traffic conditions and, accordingly, to decide on planning and prioritization of traffic safety measures. Materials and methods. The study used the method of statistical data processing for the analysis of roads by safety level. Results. The rating of highways according to the level of safety has been established to determine the priority of the road safety inspection in conditions of limited funding. Conclusions. According to the value of the weighted average coefficient of accident rate and severity of consequences of road accidents, it is possible to draw a conclusion about the general level of road safety. This allows to assess the level of safety on the road network as a whole in Ukraine and within a particular region, which helps to develop and implement measures to improve traffic safety on the most dangerous sections of roads. Key words: road, accident, road safety, traffic accidents, dead, road network rating, safety level, injured.
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Anthony, Muhamad Bob. "Persepsi antara Risiko Keselamatan Berkendaraan dengan Perilaku Pemakaian Safety Belt pada Driver Truk." Jurnal INTECH Teknik Industri Universitas Serang Raya 4, no. 2 (December 25, 2018): 53. http://dx.doi.org/10.30656/intech.v4i2.927.
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Deaths and injuries from traffic accidents have become health problems for people throughout the world including Indonesia. The saddest data from the victims who died due to traffic accidents found that 10,428 people were killed in 2017 because the drivers did not use seat belts. This research aims to see the relationship between the perception of safety risk i.e. the ability, knowledge and environmental factors with the behavior of the use of safety belts in truck drivers in mining companies. This research is a comparative causal research i.e. research that states the relationship of one variable causes other variables. What is affected is the dependent variable, namely the use of safety belt behavior and the influencing variable is the independent variable, namely the perception of the risk of driving safety. Participants are 25 mining company truck drivers. The data obtained is then processed and analyzed using the SPSS version 16. Based on the results of data processing and analysis, it is found that the ability, knowledge and work environment factors have an influence on the safety belt usage behavior.
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Xu, Jing. "Network Safety Policy Research for Analyzing Static and Dynamic Traffic Volume on the Basis of Data Mining." Open Electrical & Electronic Engineering Journal 8, no. 1 (December 31, 2014): 787–95. http://dx.doi.org/10.2174/1874129001408010787.
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With popularization of network, higher requirement is proposed to intrusion detection system IDS for network safety consideration. The traditional electronic data processing is combined with safety auditing, which has become a necessary part of constituting integrated network safety technology at present, thus the methods as optimal matching mode and statistics, etc of intrusion detection system shall be adopted. This project shall respectively make comprehensive description to current situations of intrusion detection research via the aspects of intrusion detection research method (anomaly detection, misuse detection), intrusion detection system monitoring object (network based, host based), to comprehensively analyze the impact of intrusion detection system to system architecture. On this basis a network-based anomaly intrusion detection system NAIDS is designed to network anomaly intrusion, the association rules mining and frequent scenario mining are adopted to scan the intrusion characteristic, through static mining mode and dynamic mining mode, safety detection is conducted at single layer and domain layer, new type attack can be detected via improved NAIDS system. Next, NAIDS system performance shall be evaluated by aiming at various intrusion data. Generally speaking, the system performance can detect the rejection service attack and detection attack.
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Leligou, Helen C., Periklis Chatzimisios, Lambros Sarakis, Theofanis Orphanoudakis, Panagiotis Karkazis, and Theodore Zahariadis. "An 802.11p Compliant System Prototype Supporting Road Safety and Traffic Management Applications." International Journal of Wireless Networks and Broadband Technologies 3, no. 1 (January 2014): 1–17. http://dx.doi.org/10.4018/ijwnbt.2014010101.
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During the last decades Intelligent Transportation Systems (ITS) have been attracting the interest of an increasing number of researchers, engineers and entrepreneurs, as well as citizens and civil authorities, since they can contribute towards improving road transport safety and efficiency and ameliorate environmental conditions and life quality. Emerging technologies yield miniaturized sensing, processing and communication devices that enable a high degree of integration and open the way for a large number of smart applications that can exploit automated fusion of information and enable efficient decisions by collecting, processing and communicating a large number of data in real-time. The cornerstone of these applications is the realization of an opportunistic wireless communication system between vehicles as well as between vehicles and infrastructure over which the right piece of information reaches the right location on time. In this paper, the authors present the design and implementation of representative safety and traffic management applications. Specifically the authors discuss the hardware and software requirements presenting a use case based on the NEC Linkbird-MX platform, which supports IEEE 802.11p based communications. The authors show how the functionality of IEEE 802.11p can be exploited to build efficient road safety and traffic management applications over mobile opportunistic systems and discuss practical implementation issues.
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Olszewski, Piotr, Witold Czajewski, Beata Osińska, Piotr Szagała, and Paweł Włodarek. "Investigation of traffic conflicts at signalised intersections in Warsaw." MATEC Web of Conferences 262 (2019): 05009. http://dx.doi.org/10.1051/matecconf/201926205009.
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Although traffic safety situation in general is improving, the numbers of pedestrians and cyclists hit when crossing a road have not significantly decreased recently. Based on police accident records for years 2010-2014, some 735 pedestrians and 505 cyclists were hit by motor vehicles in Warsaw. Investigation reported in this paper is a part of the European project InDeV. One aim of the project is to find correlation between accidents and traffic conflicts and thus provide a solid base for using surrogate safety measures as safety diagnostic tools. Three typical signalised intersections in Warsaw were selected for video recording. Relevant encounters between motor vehicles and vulnerable road users (pedestrians and cyclists) were identified and analysed using programs RUBA and T-Analyst. The paper describes the semiautomatic video data processing and problems regarding some technical and methodological aspects of conflict detection. Based on video analysis of 24 hours of recording for each intersection, preliminary characteristics of encounters between pedestrians/cyclists and motorised vehicles have been developed. Statistical distributions of encounter parameters such as time-to-collision (TTC) and post-encroachment time (PET) are presented. These will be used in the development of appropriate safety indicators.
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Buselli, Irene, Luca Oneto, Carlo Dambra, Christian Verdonk Gallego, Miguel García Martínez, Anthony Smoker, Nnenna Ike, Tamara Pejovic, and Patricia Ruiz Martino. "Natural language processing for aviation safety: extracting knowledge from publicly-available loss of separation reports." Open Research Europe 1 (September 23, 2021): 110. http://dx.doi.org/10.12688/openreseurope.14040.1.
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Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the loss of separation (LoS) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports, with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps, authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a general taxonomy developed by EUROCONTROL and intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board, authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports and to classify them according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real world data coming from two different sources. In the future, authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies.
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Buselli, Irene, Luca Oneto, Carlo Dambra, Christian Verdonk Gallego, Miguel García Martínez, Anthony Smoker, Nnenna Ike, Tamara Pejovic, and Patricia Ruiz Martino. "Natural language processing for aviation safety: extracting knowledge from publicly-available loss of separation reports." Open Research Europe 1 (February 18, 2022): 110. http://dx.doi.org/10.12688/openreseurope.14040.2.
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Background: The air traffic management (ATM) system has historically coped with a global increase in traffic demand ultimately leading to increased operational complexity. When dealing with the impact of this increasing complexity on system safety it is crucial to automatically analyse the losses of separation (LoSs) using tools able to extract meaningful and actionable information from safety reports. Current research in this field mainly exploits natural language processing (NLP) to categorise the reports,with the limitations that the considered categories need to be manually annotated by experts and that general taxonomies are seldom exploited. Methods: To address the current gaps,authors propose to perform exploratory data analysis on safety reports combining state-of-the-art techniques like topic modelling and clustering and then to develop an algorithm able to extract the Toolkit for ATM Occurrence Investigation (TOKAI) taxonomy factors from the free-text safety reports based on syntactic analysis. TOKAI is a tool for investigation developed by EUROCONTROL and its taxonomy is intended to become a standard and harmonised approach to future investigations. Results: Leveraging on the LoS events reported in the public databases of the Comisión de Estudio y Análisis de Notificaciones de Incidentes de Tránsito Aéreo and the United Kingdom Airprox Board,authors show how their proposal is able to automatically extract meaningful and actionable information from safety reports,other than to classify their content according to the TOKAI taxonomy. The quality of the approach is also indirectly validated by checking the connection between the identified factors and the main contributor of the incidents. Conclusions: Authors' results are a promising first step toward the full automation of a general analysis of LoS reports supported by results on real-world data coming from two different sources. In the future,authors' proposal could be extended to other taxonomies or tailored to identify factors to be included in the safety taxonomies.
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Vukša, Srđan, Pero Vidan, Mihaela Bukljaš, and Stjepan Pavić. "Research on Ship Collision Probability Model Based on Monte Carlo Simulation and Bi-LSTM." Journal of Marine Science and Engineering 10, no. 8 (August 15, 2022): 1124. http://dx.doi.org/10.3390/jmse10081124.
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The efficiency and safety of maritime traffic in a given area can be measured by analyzing traffic density and ship collision probability. Maritime traffic density is the number of ships passing through a given area in a given period of time. It can be measured using vessel tracking systems, such as the Automatic Identification System (AIS). The information provided by AIS is real-time data designed to improve maritime safety. However, the AIS data can also be used for scientific research purposes to improve maritime safety by developing predictive models for collisions in a research area. This article proposes a ship collision probability estimation model based on Monte Carlo simulation (MC) and bidirectional long short-term memory neural network (Bi-LSTM) for the maritime region of Split. The proposed model includes the processing of AIS data, the verification of AIS data, the determination of ports and ship routes, MC and the collision probability, the Bi-LSTM learning process based on MC, the ship collision probability for new or existing routes, and the traffic density. The results of MC, i.e., traffic/vessel route and density, and collision probability for the study area can be used for Bi-LSTM training with the aim of estimating ship collision probability. This article presents the first part of research that includes MC in detail, followed by a preliminary result based on one day of processed AIS data used to simulate MC and propose a model architecture that implements Bi-LSTM for ship collision probability estimation.
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Pamuła, Teresa, and Wiesław Pamuła. "Detection of Safe Passage for Trains at Rail Level Crossings Using Deep Learning." Sensors 21, no. 18 (September 18, 2021): 6281. http://dx.doi.org/10.3390/s21186281.
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The detection of obstacles at rail level crossings (RLC) is an important task for ensuring the safety of train traffic. Traffic control systems require reliable sensors for determining the state of anRLC. Fusion of information from a number of sensors located at the site increases the capability for reacting to dangerous situations. One such source is video from monitoring cameras. This paper presents a method for processing video data, using deep learning, for the determination of the state of the area (region of interest—ROI) vital for a safe passage of the train. The proposed approach is validated using video surveillance material from a number of RLC sites in Poland. The films include 24/7 observations in all weather conditions and in all seasons of the year. Results show that the recall values reach 0.98 using significantly reduced processing resources. The solution can be used as an auxiliary source of signals for train control systems, together with other sensor data, and the fused dataset can meet railway safety standards.
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Minenko, Evgen, Olexandr Pyna, Olga Belenchuk, and Tetyana Bondar. "ANALYSIS AND RESULTS OF MEASURES TO ENSURE ROAD SAFETY IN UKRAINE FOR THE PERIOD." Dorogi i mosti 2021, no. 24 (October 1, 2021): 134–48. http://dx.doi.org/10.36100/dorogimosti2021.24.134.
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Introduction. Undesirable consequences of fast increase of the vehicle fleet are an increase in the level of accident rate and the number of deaths and injuries in traffic accidents (hereinafter accidents). The positive experience of other countries shows that objectives indicators for reducing the number of traffic accident victims by implementing reasonable measures to eliminate the dominant causes of accidents is the most effective way to achieve the desired result in improving traffic safety. Problem statement. Considering the enormous economic lost caused by traffic accidents, improving of traffic safety has been a priority in the policies of many countries around the world in recent decades. In this regard, the international community pays considerable attention to the development of targeted programs and implementation of traffic safety measures directed at preventing the road traffic injuries. In particular, on March 2, 2010, the UN General Assembly adopted Resolution No. 64/255 «Improving Traffic Safety Worldwide», which proclaimed the 2011–2020 «Decade of Traffic Safety Actions» to reduce deaths from injuries sustained as a result of traffic accident — by 50%. Ukraine, through which seven international transport corridors run [1], did not stay away from solving the common problem and joined in 2011 to the UN initiative regarding reducing the death rate due to traffic accidents by at least 30 % [2]. For evaluation of the achieved result, it is important to analyze the accident statistics and determine whether the planned results were achieved through the implementation of planned measures to improve traffic safety. Purpose. The article considers the dynamics of accident rate and the number of traffic accidents victims in Ukraine for the period 2011–2020, as well as analyzes the main causes of traffic accidents on the public roads, including roads of state importance, to evaluate the outcome of the Decade of Traffic Safety Actions and providing the recommendations for further decreasing of mortality on domestic roads. Materials and methods. The study used the method of statistical processing of data on the number of accidents and their victims in Ukraine and the method of systematic analysis of risk factors that contributed to the accidents. Results. It is determined that the total number of fatalities in traffic accidents for the period 2011–2020 decreased in Ukraine by 27.8 %, and on the public roads — by 44.0 %. However, considering that since 2014 there are no data on traffic accidents in Autonomous Republic of Crimea and partly in Donetsk and Luhansk regions, the indicator of decreasing of deaths per 100 traffic accidents and the result shows more modest achievements: in Ukraine it was possible to reduce deaths by 100 traffic accidents per 100 14 %, and on the public roads — only 5.4 %. Conclusions. Accident rate analysis provides an information basis for understanding the scale of the problem of traffic injuries, evaluating the results of implemented measures to improve traffic safety, the dynamics of positive or negative developments, and allows to plan reasonably the measures to improve road conditions to reduce mortality on roads.
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Guido, Giuseppe, Alessandro Vitale, Frank Fedel Saccomanno, Demetrio Carmine Festa, Vittorio Astarita, Daniele Rogano, and Vincenzo Gallelli. "Using Smartphones as a Tool to Capture Road Traffic Attributes." Applied Mechanics and Materials 432 (September 2013): 513–19. http://dx.doi.org/10.4028/www.scientific.net/amm.432.513.
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Road network management under critical conditions is achievable by adopting technologies that trace vehicles and capture unsafety events to provide users with real time traffic information. Most common approaches used to acquire vehicle tracking data are based on video image processing algorithms and satellite navigation systems. However, many studies are increasingly focused on the emerging smartphone technologies for tracking vehicles. The aim of this study is to present a procedure for acquiring vehicle tracking data from smartphone sensors, supporting managers of transportation systems to take effective decisions on their networks, especially in conjunction with special events and/or critical road safety issues.
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Liu, Bing, Tao Zhang, and Weicheng Hu. "Intelligent Traffic Flow Prediction and Analysis Based on Internet of Things and Big Data." Computational Intelligence and Neuroscience 2022 (June 15, 2022): 1–12. http://dx.doi.org/10.1155/2022/6420799.
Full textAbstract:
Nowadays, the problem of road traffic safety cannot be ignored. Almost all major cities have problems such as poor traffic environment and low road efficiency. Large-scale and long-term traffic congestion occurs almost every day. Transportation has developed rapidly, and more and more advanced means of transportation have emerged. However, automobile is one of the main means of transportation for people to travel. In the world, there are serious traffic jams in almost all cities. The excessive traffic flow every day leads to the paralysis of the urban transportation system, which brings great inconvenience and impact to people’s travel. Various countries have also actively taken corresponding measures, i.e., traffic diversion, number restriction, or expanding the scale of the road network, but these measures can bring little effect. Traditional intelligent traffic flow forecasting has some problems, such as low accuracy and delay. Aiming at this problem, this paper uses the model of the combination of Internet of Things and big data to apply and analyze its social benefits in intelligent traffic flow forecasting and analyzes its three-tier network architecture model, namely, perception layer, network layer, and application layer. Research and analyze the mode of combining cloud computing and edge computing. From the multiperspective linear discriminant analysis algorithm of the combination method of combining the same points and differences between data and data into multiple atomic services, intelligent traffic flow prediction based on the combination of Internet of Things and big data is performed. Through the monitoring and extraction of relevant traffic flow data, data analysis, processing and storage, and visual display, improve the accuracy and effectiveness and make it easier to improve the prediction accuracy of overall traffic flow. The traffic flow prediction of the system of Internet of Things and big data is given through the case experiment. The method proposed in this paper can be applied in intelligent transportation services and can predict the stability of transportation and traffic flow in real time so as to optimize traffic congestion, reduce manual intervention, and achieve the goal of intelligent traffic management.
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Miyamoto, Ayaka, Mayank V. Bendarkar, and Dimitri N. Mavris. "Natural Language Processing of Aviation Safety Reports to Identify Inefficient Operational Patterns." Aerospace 9, no. 8 (August 17, 2022): 450. http://dx.doi.org/10.3390/aerospace9080450.
Full textAbstract:
With the growth in commercial aviation traffic and the need for improved environmental performance, strategies to lower emissions that can be implemented in the near term are necessary. Since novel technology takes time to enter the market, operational improvements that employ existing aircraft and require no new infrastructure are fit for this goal. While quantified data collected throughout aviation, such as arrival/departure statistics and flight data, have been well-utilized, text data collected through safety reports have not been leveraged to their full extent. In this paper, a methodology is presented that can use aviation text data to identify high-level causes of flight delays and cancellations, using delays as a metric of operational inefficiency. The dataset is extracted from the Aviation Safety Reporting System (ASRS), which includes voluntary safety incident reports in text narrative and metadata formats. The methodology uses natural language processing tools, K Means clustering, and dimensionality reduction by t-Distributed Stochastic Neighbor Embedding (t-SNE) to categorize and visualize narratives. The method identified 7 major clusters and a total of 23 sub-clusters. A comparison between the subclusters’ topics and the causes of flight delays revealed by the quantified data shows that the ASRS database provides a unique safety perspective to delay cause identification, as illustrated by the method’s identification of maintenance as the main cause of delays, rather than weather.
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Liu, Shijie, Zhaoyou Ma, Xinming Guo, Xucai Zhuang, Yonghong Chen, Jianqing Wu, and Jianping Xing. "Research and development of intelligent safety sensor integration devices for autonomous driving." Journal of Physics: Conference Series 2196, no. 1 (February 1, 2022): 012002. http://dx.doi.org/10.1088/1742-6596/2196/1/012002.
Full textAbstract:
Abstract Grasping the status of vehicles timely and accurately is the key to avoid traffic accidents and collecting data from multi-sensors is significant for development of road-side intelligent sensor technology. In this paper, LiDAR (Light Detection and Ranging) and camera sensors are tested, and the sensor data can be processed and analyzed, and the display can be integrated. In this paper, a multi-sensor data acquisition integration device based on Raspberry Pi is proposed, which can realize the optimal processing of data and simultaneous acquisition display function. This method avoids the tedium of using an IPC and improves the efficiency of data acquisition and sensor integration.
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Zheng, Shu Kang, Min Li, Qi Zhu, Xiao Min Liu, Hao Dong Shen, Xue Wu Zhang, Zhuo Zhang, and Xin Nan Fan. "Video-Based Traffic Flow Parameters Monitoring and Integrated Traffic Information System." Applied Mechanics and Materials 462-463 (November 2013): 77–84. http://dx.doi.org/10.4028/www.scientific.net/amm.462-463.77.
Full textAbstract:
Road traffic flow parameters are the important basic information for traffic safety management, traffic condition evaluation and decision-making. This project designs a video-based traffic flow parameters monitoring terminal (ITS monitoring terminal), which is based on MENLOW embedded platform and expands its hardware and software resources. CCD cameras are used to capture image in video sequences in traffic environment. Image processing and analysis technologies are used to track vehicles and analyze the vehicle conditions in real time, and a vision measurement model which computes the traffic flow parameters, such as length, width, speed, distance between two vehicles, traffic flow density, and occupancy ratios, etc. is constructed. Furthermore, BP neural network is used to classify vehicles. ITS terminals interconnected with each other through public network or private network (optical ring network of transport agency, WLAN, Internet, and 3G) and connected with monitoring center of transport agency, which achieves dynamic data exchange and share among ITS terminals. It realized a wide-area distributed and integrated transport information system which synthesizes transport information guidance, traffic tracking; condition evaluation, decision-making, and real time transport information release.
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Skorobogatchenko, D. A., V. V. Borovik, and A. I. Frolovichev. "Assessment automation of road traffic safety with account for road conditions of an individual itinerary." Journal of Physics: Conference Series 2091, no. 1 (November 1, 2021): 012051. http://dx.doi.org/10.1088/1742-6596/2091/1/012051.
Full textAbstract:
Abstract The paper substantiates the need to develop an automated system for traffic safety assessment in urban agglomerations, taking into account road conditions. The authors suggest a methodology for assessment of road traffic accidents, which makes it possible to take into account a wide range of factors affecting them. The methodology is based on complementing the traditional approach of final accident rate calculation with algorithms for collecting and analyzing data using Big Data tools, in particular, convolutional neural networks, fuzzy neural networks such as ANFIS, and cluster analysis using the k-means method. All accident rates are grouped according to the principle of homogeneity of acquisition of information for their calculation. Further, one of the data processing tools is applied to each group. As a result, labor intensity is reduced and the effectiveness of the application of the method of final accident rates increases. For practical calculations, the authors have developed a client-server application that uses data on geometric characteristics, current traffic situation, weather and climatic effects at the time of the trip along a specific itinerary. By means of application use, the analysis of traffic safety on a number of routes in Volgograd was carried out and the results are presented in comparison with the calculations made via the traditional method. It is shown that the use of information about the current situation on a specific section of the road network in terms of the current time significantly increases the accuracy of calculations.
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Dudek, Ewa, and Michał Kozłowski. "The Concept of Risk Tolerability Matrix Determination for Aeronautical Data and Information Chain." Journal of KONBiN 43, no. 1 (October 1, 2017): 69–94. http://dx.doi.org/10.1515/jok-2017-0040.
Full textAbstract:
Abstract This article is a continuation of the Authors’ study on the ways to ensure the quality and safety of aeronautical data and information in the entire process (considered as the supply chain) of those data and information creation, collection, processing and publication. In its content attention was paid to air traffic proactive safety management aa well as the need to manage identified incompatibilities. The risk assessment and tolerability matrices arising from ICAO specifications were presented, and then on their bases, the concept of such matrices determination for aeronautical data and information chain was developed. In addition, the criteria for consequences’/effects’ of incompatibilities appearance assessment related strictly to air transport were elaborated. In the summary directions for further analysis were pointed out, leading to carrying out a full risk assessment analysis of the discussed chain with the use of the FMEA method.