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Artykuły w czasopismach na temat „Traffic event detection”

Autor: Grafiati
Data publikacji: 3 czerwca 2025
Data aktualizacji: 31 lipca 2025

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1

Kim, Yuna, Sangho Song, Hyeonbyeong Lee, et al. "Regional Traffic Event Detection Using Data Crowdsourcing." Applied Sciences 13, no. 16 (2023): 9422. http://dx.doi.org/10.3390/app13169422.

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Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies have proposed crowdsourcing to resolve traffic problems, in which drivers provide real-time traffic information using mobile devices to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time and cost. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data
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Lee, Soomok, Sanghyun Lee, Jongmin Noh, Jinyoung Kim, and Harim Jeong. "Special Traffic Event Detection: Framework, Dataset Generation, and Deep Neural Network Perspectives." Sensors 23, no. 19 (2023): 8129. http://dx.doi.org/10.3390/s23198129.

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Identifying early special traffic events is crucial for efficient traffic control management. If there are a sufficient number of vehicles equipped with automatic event detection and report gadgets, this enables a more rapid response to special events, including road debris, unexpected pedestrians, accidents, and malfunctioning vehicles. To address the needs of such a system and service, we propose a framework for an in-vehicle module-based special traffic event and emergency detection and safe driving monitoring service, which utilizes the modified ResNet classification algorithm to improve t
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Wang, Jia, Minh Ngueyn, and Weiqi Yan. "A Framework of Event-Driven Traffic Ticketing System." International Journal of Digital Crime and Forensics 9, no. 1 (2017): 39–50. http://dx.doi.org/10.4018/ijdcf.2017010103.

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This paper presents a new scheme to implement an event-driven traffic ticketing system. The system consists of two modules, namely, (1) event detection module, (2) database management module to execute information retrieval and deliver traffic tickets. In this paper, the notable contribution is an automatic detection of traffic ticketing events from video footages and immediate notification of such event when a car is passing the white stop line before a red traffic light. The results show the authors' work is efficient and could detect the events precisely.
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Navi, Ramesh, and Aruna M. G. "Traffic Event Detection using Computer Vision." IOSR Journal of Computer Engineering 16, no. 3 (2014): 25–32. http://dx.doi.org/10.9790/0661-16322532.

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Zhiwei Zhang, Zhiwei Zhang, Guiyuan Tang Zhiwei Zhang, Baoquan Ren Guiyuan Tang, Baoquan Ren Baoquan Ren, and Yulong Shen Baoquan Ren. "TV-ADS: A Smarter Attack Detection Scheme Based on Traffic Visualization of Wireless Network Event Cell." 網際網路技術學刊 25, no. 2 (2024): 301–11. http://dx.doi.org/10.53106/160792642024032502012.

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<p>To protect the increasing cyberspace assets, attack detection systems (ADSs) as well as intrusion detection systems (IDSs) have been equipped in various network environments. Recently, with the development of big data, machine learning, deep learning, neural networks and other artificial intelligence (AI) technologies, more and more ADSs/IDSs based on Artificial Intelligence are presented in academia and industry. Particularly, depending on the outstanding performance and efficiency in recognizing and classifying images, computer vision algorithms have been employed to detect maliciou
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K.V.G.N., Naidu*1 &. P. Sireesha2. "TWITTER ANALYSISFOR IDENTIFICATION OF REAL-TIME TRAFFIC." INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT 4, no. 5 (2017): 148–51. https://doi.org/10.5281/zenodo.573507.

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Social networks have been recently empployed as a source of information for event detection, with specific reference to road traffic activity congestion and accidents or earthquack reporting system. In our paper, we present a real-time detection of traffic from Twitter stream analysis. The system fetches tweets from Twitter as per a several search criteria; process tweets by applying text mining methods; lastly performs the classification of tweets. The aim is to assign suitable class label to every tweet, as related with an activity of traffic event or not. The traffic detection system or fra
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Xu, S., S. Li, R. Wen, and W. Huang. "TRAFFIC EVENT DETECTION USING TWITTER DATA BASED ON ASSOCIATION RULES." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W5 (May 29, 2019): 543–47. http://dx.doi.org/10.5194/isprs-annals-iv-2-w5-543-2019.

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<p><strong>Abstract.</strong> Social media platforms allow millions of people worldwide to instantly share their thoughts online. Many people use social media to share traffic related experiences and events with online posts. A large amount of traffic related data can be obtained from these online posts – especially geosocial media data, where posts are tagged with geolocation information such as coordinates or place names. By extracting traffic events from geosocial media data, drivers can adapt to changing traffic conditions, while traffic management departmen
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Priyanka, Shinde, and M. Dongre Manoj. "TRAFFIC CONGESTION DETECTION WITH COMPLEX EVENT PROCESSING IN VANET." JournalNX - a Multidisciplinary Peer Reviewed Journal RIT PG Con-18 (April 22, 2018): 270–74. https://doi.org/10.5281/zenodo.1413800.

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Magnitude of urban population and unplanned development of cities have led to road traffic congestion in major cities and added increasing pressure on overall road transport and road related infrastructure. Nowadays, intelligent transportation systems such as Vehicular Ad Hoc Networks (VANET) are used for distributed road traffic. VANET is a wireless network that gathers complex and randomly generated data related to distributed traffic along with other information such as weather con ditions on real time basis. In this paper, the Author has attempted to implement a system on Event Driven Arch
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Wu, Hao, Jiahao Yang, Ming-Dong Yuan, and Xin Li. "Heuristic Optimal Scheduling for Road Traffic Incident Detection Under Computational Constraints." Sensors 24, no. 22 (2024): 7221. http://dx.doi.org/10.3390/s24227221.

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The intelligent monitoring of road surveillance videos is a crucial tool for detecting and predicting traffic anomalies, swiftly identifying road safety risks, rapidly addressing potential hazards, and preventing accidents or secondary incidents. With the vast number of surveillance cameras in operation, conducting traditional real-time video analysis across all cameras at once requires substantial computational resources. Alternatively, methods that employ periodic camera patrol analysis frequently overlook a significant number of anomalous traffic events, thereby hindering the effectiveness
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A Mohamed, M., N. Jamil, A. F Abidin, M. M Din, W. N S W Nik, and A. R Mamat. "Entity-based Parameterization for Distinguishing Distributed Denial of Service from Flash Events." International Journal of Engineering & Technology 7, no. 2.14 (2018): 5. http://dx.doi.org/10.14419/ijet.v7i2.14.11142.

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In a perfect condition, there are only normal network traffic and sometimes flash event traffics due to some eye-catching or heart-breaking events. Nevertheless, both events carry legitimate requests and contents to the server. Flash event traffic can be massive and damaging to the availability of the server. However, it can easily be remedied by hardware solutions such as adding extra processing power and memory devices and software solution such as load balancing. In contrast, a collection of illegal traffic requests produced during distributed denial of service (DDoS) attack tries to cause
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11

Mihelj, Jernej, Yuan Zhang, Andrej Kos, and Urban Sedlar. "Crowdsourced Traffic Event Detection and Source Reputation Assessment Using Smart Contracts." Sensors 19, no. 15 (2019): 3267. http://dx.doi.org/10.3390/s19153267.

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Real-time data about various traffic events and conditions—offences, accidents, dangerous driving, or dangerous road conditions—is crucial for safe and efficient transportation. Unlike roadside infrastructure data which are often limited in scope and quantity, crowdsensing approaches promise much broader and comprehensive coverage of traffic events. However, to ensure safe and efficient traffic operation, assessing trustworthiness of crowdsourced data is of crucial importance; this also includes detection of intentional or unintentional manipulation, deception, and spamming. In this paper, we
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Wan, Li, Zhenjiang Li, Changan Zhang, Guangyong Chen, Panming Zhao, and Kewei Wu. "Algorithm Improvement for Mobile Event Detection with Intelligent Tunnel Robots." Big Data and Cognitive Computing 8, no. 11 (2024): 147. http://dx.doi.org/10.3390/bdcc8110147.

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Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in an edge computing mobile device using an intelligent tunnel robot. The enhanc
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13

Yang, Li. "Detection of Real-Time Event in Intelligent Traffic System Based on RFID." Advanced Materials Research 926-930 (May 2014): 1314–17. http://dx.doi.org/10.4028/www.scientific.net/amr.926-930.1314.

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To solve the demand of real-time event detection in the RFID-based Intelligent Transportation Systems , using Complex Event Processing technology to establish a rule model to detect events.The model allows users to customize the Basic Events and Complex Events, using the rule files describe the complex events modes, clearly expressed the timing and gradation relationships between RFID events, meeting the needs of real-time event detection in the Intelligent Transportation System ,achieving the appropriate rules engine,. Finally, test and verify the effectiveness of the rules file and the rules
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14

Su, Ziyi, Qingchao Liu, Chunxia Zhao, and Fengming Sun. "A Traffic Event Detection Method Based on Random Forest and Permutation Importance." Mathematics 10, no. 6 (2022): 873. http://dx.doi.org/10.3390/math10060873.

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Although the video surveillance system plays an important role in intelligent transportation, the limited camera views make it difficult to observe many traffic events. In this paper, we collect and combine the traffic flow variables from the multi-source sensors, and propose a PITED method based on Random Forest (RF) and Permutation importance (PI) for traffic event detection. This model selects the suitable traffic flow variables by means of permutation arrangement of importance, and establishes the whole process of acquisition, preprocessing, quantization, modeling and evaluation. Moreover,
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15

Olariu, Stephan, and Dimitrie C. Popescu. "SEE-TREND: SEcurE Traffic-Related EveNt Detection in Smart Communities." Sensors 21, no. 22 (2021): 7652. http://dx.doi.org/10.3390/s21227652.

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It has been widely recognized that one of the critical services provided by Smart Cities and Smart Communities is Smart Mobility. This paper lays the theoretical foundations of SEE-TREND, a system for Secure Early Traffic-Related EveNt Detection in Smart Cities and Smart Communities. SEE-TREND promotes Smart Mobility by implementing an anonymous, probabilistic collection of traffic-related data from passing vehicles. The collected data are then aggregated and used by its inference engine to build beliefs about the state of the traffic, to detect traffic trends, and to disseminate relevant traf
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Sun, Shaowei, and Mingzhou Liu. "A framework for detecting and predicting highway traffic anomalies via multimodal fusion and heterogeneous graph neural networks." PLOS One 20, no. 6 (2025): e0326313. https://doi.org/10.1371/journal.pone.0326313.

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This paper presents a novel framework for detecting and predicting abnormal traffic events on highways. Current traffic monitoring systems often rely on single data sources, which limits their detection accuracy and robustness in complex environments. To address these limitations, we propose a framework based on multimodal deep fusion and heterogeneous graph neural networks (HGNNs), incorporating an Ensemble Contrastive Pessimistic Likelihood Estimation (CPLE) algorithm to optimize performance. The framework integrates static and dynamic traffic data, such as video images, traffic flow, vehicl
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Mamdoohi, Sohrab, and Elise Miller-Hooks. "Machine Learning and Reverse Methods for a Deeper Understanding of Public Roadway Improvement Action Impacts during Execution." Journal of Advanced Transportation 2022 (September 27, 2022): 1–22. http://dx.doi.org/10.1155/2022/6385236.

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The execution of public roadway maintenance, rehabilitation, and restoration activities disturb normal traffic flows, resulting in roadway capacity reduction, inducing travel time delays, and promoting traffic safety concerns. While they improve public roadway performance once complete, the impacts endured in executing these actions is significant. This work seeks a deeper understanding of the effects of improvement actions on traffic by juxtaposing their effects against those arising from traffic incidents that cause similar capacity reductions and related negative externalities. This is acco
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Jones, Angelica Salas, Panagiotis Georgakis, Yannis Petalas, and Renukappa Suresh. "Real-time traffic event detection using Twitter data." Infrastructure Asset Management 5, no. 3 (2018): 77–84. http://dx.doi.org/10.1680/jinam.17.00022.

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Kashyap, Sonam, and Mohit Gupta. "A Review on Various Traffic Event Detection Techniques." International Journal of Computer Sciences and Engineering 6, no. 9 (2018): 575–83. http://dx.doi.org/10.26438/ijcse/v6i9.575583.

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Ambardekar, Amol, Mircea Nicolescu, George Bebis, and Monica Nicolescu. "Visual traffic surveillance framework: classification to event detection." Journal of Electronic Imaging 22, no. 4 (2013): 041112. http://dx.doi.org/10.1117/1.jei.22.4.041112.

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Fanaee-T, Hadi, and João Gama. "Event detection from traffic tensors: A hybrid model." Neurocomputing 203 (August 2016): 22–33. http://dx.doi.org/10.1016/j.neucom.2016.04.006.

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Wang, Di, Ahmad Al-Rubaie, Sandra Stinčić Clarke, and John Davies. "Real-Time Traffic Event Detection From Social Media." ACM Transactions on Internet Technology 18, no. 1 (2017): 1–23. http://dx.doi.org/10.1145/3122982.

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AlDhanhani, Ahmed, Ernesto Damiani, Rabeb Mizouni, and Di Wang. "Framework for traffic event detection using Shapelet Transform." Engineering Applications of Artificial Intelligence 82 (June 2019): 226–35. http://dx.doi.org/10.1016/j.engappai.2019.04.002.

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Yang, Xiangyu, Giannis Bekoulis, and Nikos Deligiannis. "Traffic event detection as a slot filling problem." Engineering Applications of Artificial Intelligence 123 (August 2023): 106202. http://dx.doi.org/10.1016/j.engappai.2023.106202.

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Zhou, Lei, Yi Lin, and Haochen Bai. "A Highway Abnormal Event Discrimination and Detection Method." Highlights in Science, Engineering and Technology 126 (January 10, 2025): 28–34. https://doi.org/10.54097/6t4gyw49.

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With the continuous increase in highway traffic volume, abnormal events occur frequently, seriously affecting traffic fluidity and safety. This study aims to develop an efficient abnormal event detection method. Based on simulation data generated by the VISSIM software, the K-means clustering algorithm is applied, with speed, density, and occupancy rate as key feature parameters, aiming to effectively identify abnormal events on highways. The research results show that, after comparing the performance of models using different combinations of feature parameters, the K-means model using speed a
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Sun, De Gang, Kun Yang, Xiang Jing, Bin Lv, and Yan Wang. "Abnormal Network Traffic Detection Based on Conditional Event Algebra." Applied Mechanics and Materials 644-650 (September 2014): 1093–99. http://dx.doi.org/10.4028/www.scientific.net/amm.644-650.1093.

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Network anomaly traffic detection can discover network abnormal behavior and unknown network attacks. But anomaly detection system in current has the disadvantage of the high rate of false positives. Because the condition is not sufficient and high-order conditional reasoning cannot be computed, it leads inaccurate detection of abnormal behavior. In this paper, an analysis method for abnormal network traffic detection is presented. The method firstly applied conditional event algebra for abnormal network traffic detection of Denial-of-Service (DoS) attacks on the 10% trainset of KDD Cup 99 dat
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Zhang, Xiaodan, Yongsheng Chen, and Guichen Tang. "Research on Traffic Acoustic Event Detection Algorithm Based on Sparse Autoencoder." MATEC Web of Conferences 308 (2020): 05002. http://dx.doi.org/10.1051/matecconf/202030805002.

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Road traffic monitoring is very important for intelligent transportation. The detection of traffic state based on acoustic information is a new research direction. A vehicles acoustic event classification algorithm based on sparse autoencoder is proposed to analysis the traffic state. Firstly, the multidimensional Mel-cepstrum features and energy features are extracted to form a feature vector of 125 features; Secondly, based on the computed features, the five-layers autoencoder is trained. Finally, vehicle audio samples are collected and the trained autoencoder is tested. The experimental res
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Sourek, Gustav, and Filip Zelezny. "Efficient Extraction of Network Event Types from NetFlows." Security and Communication Networks 2019 (February 6, 2019): 1–18. http://dx.doi.org/10.1155/2019/8954914.

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To perform sophisticated traffic analysis, such as intrusion detection, network monitoring tools firstly need to extract higher-level information from lower-level data by reconstructing events and activities from as primitive information as individual network packets or traffic flows. Aggregating communication data into meaningful entities is an open problem and existing, typically clustering-based, solutions are often highly suboptimal, producing results that may misinterpret the extracted information and consequently miss many network events. We propose a novel method for the extraction of v
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Lu, Jiazhong, Fengmao Lv, Zhongliu Zhuo, et al. "Integrating Traffics with Network Device Logs for Anomaly Detection." Security and Communication Networks 2019 (June 13, 2019): 1–10. http://dx.doi.org/10.1155/2019/5695021.

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Advanced cyberattacks are often featured by multiple types, layers, and stages, with the goal of cheating the monitors. Existing anomaly detection systems usually search logs or traffics alone for evidence of attacks but ignore further analysis about attack processes. For instance, the traffic detection methods can only detect the attack flows roughly but fail to reconstruct the attack event process and reveal the current network node status. As a result, they cannot fully model the complex multistage attack. To address these problems, we present Traffic-Log Combined Detection (TLCD), which is
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Kokkinos, Konstantinos, and Eftihia Nathanail. "Exploring an Ensemble of Textual Machine Learning Methodologies for Traffic Event Detection and Classification." Transport and Telecommunication Journal 21, no. 4 (2020): 285–94. http://dx.doi.org/10.2478/ttj-2020-0023.

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AbstractLate research has established the critical environmental, health and social impacts of traffic in highly populated urban regions. Apart from traffic monitoring, textual analysis of geo-located social media responses can provide an intelligent means in detecting and classifying traffic related events. This paper deals with the content analysis of Twitter textual data using an ensemble of supervised and unsupervised Machine Learning methods in order to cluster and properly classify traffic related events. Voluminous textual data was gathered using innovative Twitter APIs and managed by B
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Zhang, Ning, Yi Shi, and Wei Huang. "Traffic Event Automatic Detection Based on OGS-DTW Algorithm." Journal of Highway and Transportation Research and Development (English Edition) 6, no. 1 (2012): 54–60. http://dx.doi.org/10.1061/jhtrcq.0000091.

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Zhao, Meng, Jie Chen, Zhikai Yang, Yaping Liu, and Shuo Zhang. "HomeMonitor: An Enhanced Device Event Detection Method for Smart Home Environment." Sensors 22, no. 23 (2022): 9389. http://dx.doi.org/10.3390/s22239389.

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As more and more smart devices are deployed in homes, the communication between these smart home devices and elastic computing services may face some risks of privacy disclosure. Different device events (such as the camera on, video on, etc.) will generate different data traffic during communication. However, the current smart home system lacks monitoring of these device events, which may cause the disclosure of private data collected by these devices. In this paper, we present our device event monitor system, HomeMonitor. HomeMonitor runs in the OpenWRT system and supports complete event moni
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Mredula, Motahara Sabah, Noyon Dey, Md Sazzadur Rahman, Imtiaz Mahmud, and You-Ze Cho. "A Review on the Trends in Event Detection by Analyzing Social Media Platforms’ Data." Sensors 22, no. 12 (2022): 4531. http://dx.doi.org/10.3390/s22124531.

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Social media platforms have many users who share their thoughts and use these platforms to organize various events collectively. However, different upsetting incidents have occurred in recent years by taking advantage of social media, raising significant concerns. Therefore, considerable research has been carried out to detect any disturbing event and take appropriate measures. This review paper presents a thorough survey to acquire in-depth knowledge about the current research in this field and provide a guideline for future research. We systematically review 67 articles on event detection by
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Jyoti, Snehi, Bhandari Abhinav, Baggan Vidhu, and Snehi Ritu Manish. "Diverse Methods for Signature based Intrusion Detection Schemes Adopted." International Journal of Recent Technology and Engineering (IJRTE) 9, no. 2 (2020): 44–49. https://doi.org/10.35940/ijrte.A2791.079220.

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Intrusion Detection Systems (IDS) is used as a tool to detect intrusions on IT networks, providing support in network monitoring to identify and avoid possible attacks. Most such approaches adopt Signature-based methods for detecting attacks which include matching the input event to predefined database signatures. Signature based intrusion detection acts as an adaptable device security safeguard technology. This paper discusses various Signature-based Intrusion Detection Systems and their advantages; given a set of signatures and basic patterns that estimate the relative importance of each int
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Arnold, M., M. Hoyer, and S. Keller. "CONVOLUTIONAL NEURAL NETWORKS FOR DETECTING BRIDGE CROSSING EVENTS WITH GROUND-BASED INTERFEROMETRIC RADAR DATA." ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-1-2021 (June 17, 2021): 31–38. http://dx.doi.org/10.5194/isprs-annals-v-1-2021-31-2021.

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Abstract. This study focuses on detecting vehicle crossings (events) with ground-based interferometric radar (GBR) time series data recorded at bridges in the course of critical infrastructure monitoring. To address the challenging event detection and time series classification task, we rely on a deep learning (DL) architecture. The GBR-displacement data originates from real-world measurements at two German bridges under normal traffic conditions. As preprocessing, we only apply a low-pass filter. We develop and evaluate a one-dimensional convolutional neural network (CNN) to achieve a solely
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Leon-Garcia, Fernando, Jose Palomares, and Joaquin Olivares. "D2R-TED: Data—Domain Reduction Model for Threshold-Based Event Detection in Sensor Networks." Sensors 18, no. 11 (2018): 3806. http://dx.doi.org/10.3390/s18113806.

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The reduction of sensor network traffic has become a scientific challenge. Different compression techniques are applied for this purpose, offering general solutions which try to minimize the loss of information. Here, a new proposal for traffic reduction by redefining the domains of the sensor data is presented. A configurable data reduction model is proposed focused on periodic duty–cycled sensor networks with events triggered by threshold. The loss of information produced by the model is analyzed in this paper in the context of event detection, an unusual approach leading to a set of specifi
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Murenin, I. "Detection of Anomalies in the Traffic of IoT Devices." Proceedings of Telecommunication Universities 7, no. 4 (2021): 128–37. http://dx.doi.org/10.31854/1813-324x-2021-7-4-128-137.

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The article proposes an approach to finding anomalies in the traffic of IoT devices based on time series analysis and assessing normal and abnormal behavior using statistical methods. The main goal of the proposed approach is to combine statistical methods for detecting anomalies using unlabeled data and plotting key characteristics of device profiles. Within this approach the following techniques for traffic analysis has been developed and implemented: a technique for a feature extraction, a normal behavior boundary building technique and an anomaly detection technique. To evaluate the propos
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Wan, Fu Cai, Cheng Xiang Yin, and Xiao Wei Han. "Abnormal Event Detection of Traffic Intersection Based on SAT Algorithm." Applied Mechanics and Materials 602-605 (August 2014): 1650–53. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1650.

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SAT (Separating Axis Theorem) is an algorithm applied in two-dimensional games of collision detection in the convex polygon. This paper applied SAT algorithm into video monitoring at intersections to detect the vehicles collision incidents. Established separation axis to extracted vehicles and projected the target vehicles into separation axis. Through comparing the scalar value in separation axis to determine whether the target vehicles collision. It's proved that the method is effective by using Matlab software to the simulation test in this algorithm.
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Mukesh, G. Jadhav, and R. Jadhav Sachin. "Analysis of Traffic Sign Detection and Recognition." Journal of Android and IOS Applications and Testing 4, no. 2 (2019): 11–17. https://doi.org/10.5281/zenodo.3271352.

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<em>Accidents are happens due to avoidance of traffic sign.</em><em>As per worldwide street insights almost 1.3 million individuals bite the dust in street mishap in every year [ASIRT, 2014]. In the event that there is a programmed location and acknowledgment framework, it can instantly report the right traffic signs to the driver and furthermore diminish the weight of the driver. At the point when the driver disregards a traffic sign, the framework can give a convenient cautioning</em><em>.&nbsp; In this seminar we studied five different papers.</em><em>Colour segmentation, Edge Extraction, T
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S. Abdulkarem, Huda, and Ammar D. Alethawy. "DDOS ATTACK DETECTION AND MITIGATION AT SDN ENVIROMENT." Iraqi Journal of Information & Communications Technology 4, no. 1 (2021): 1–9. http://dx.doi.org/10.31987/ijict.4.1.115.

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Abstract- Software-Defined Networking (SDN) is a promising sample that allows the programming behind the network’s operation with some abstraction level from the underlying networking devices .the insistence to detect and mitigate Distributed Denial of Service (DDoS) which introduced by network devices tries to discover network security weaknesses and the negative effects of some types of Distributed Denial of Service (DDoS) attacks. An SDN-based generic solution to mitigate DDoS attacks when and where they originate. Briefly, it compares at runtime the expected trend of normal traffic against
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Zhang, Chengcui. "A Survey of Visual Traffic Surveillance Using Spatio-Temporal Analysis and Mining." International Journal of Multimedia Data Engineering and Management 4, no. 3 (2013): 42–60. http://dx.doi.org/10.4018/jmdem.2013070103.

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The focus of this survey is on spatio-temporal data mining and database retrieval for visual traffic surveillance systems. In many traffic surveillance applications, such as incident detection, abnormal events detection, vehicle speed estimation, and traffic volume estimation, the data used for reasoning is really in the form of spatio-temporal data (e.g. vehicle trajectories). How to effectively analyze these spatio-temporal data to automatically find its inherent characteristics for different visual traffic surveillance applications has been of great interest. Examples of spatio-temporal pat
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Yang, Lu, Ahmad Sufril Azlan Bin Mohamed, and Majid Khan Bin Majahar Ali. "An Improved Object Detection and Trajectory Prediction Method for Traffic Conflicts Analysis." Promet - Traffic&Transportation 35, no. 4 (2023): 462–84. http://dx.doi.org/10.7307/ptt.v35i4.173.

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Although computer vision-based methods have seen broad utilisation in evaluating traffic situations, there is a lack of research on the assessment and prediction of near misses in traffic. In addition, most object detection algorithms are not very good at detecting small targets. This study proposes a combination of object detection and tracking algorithms, Inverse Perspective Mapping (IPM), and trajectory prediction mechanisms to assess near-miss events. First, an instance segmentation head was proposed to improve the accuracy of the object frame box detection phase. Secondly, IPM was applied
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Li, Yao, and Ping Luo. "Teaching Management and Monitoring Abnormal Network Behaviors Under COVID-19." International Journal of Distributed Systems and Technologies 12, no. 2 (2021): 55–63. http://dx.doi.org/10.4018/ijdst.2021040106.

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Due to the epidemic of COVID-19, more social activities have been moved to the internet, such as online education and online learning. The education management to avoid burst events is a basic requirement of online education, especially when a huge number of persons are visiting at the same time. In order to monitor the abnormal and burst access in online education systems, this paper proposes an anomaly detection method by using data flow to mining high frequency events among massive network traffic data during online education. First, the data flow in traffic network is described as a specia
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Aliari, Sanaz, and Kaveh F. Sadabadi. "Automatic Detection of Major Freeway Congestion Events using Wireless Traffic Sensor Data: Machine Learning Approach." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (2019): 436–42. http://dx.doi.org/10.1177/0361198119843859.

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Monitoring the dynamics of traffic in major corridors can provide invaluable insight for traffic planning purposes. An important requirement for this monitoring is the availability of methods to automatically detect major traffic events and to annotate the abundance of travel data. This paper introduces a machine learning-based approach for reliable detection and characterization of highway traffic congestion events from hundreds of hours of traffic speed data. Indeed, the proposed approach is a generic approach for detection of changes in any given time series, which is the wireless traffic s
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Dhole, Yash. "Vehicle Accident Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30145.

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Road traffic accidents pose a serious threat to public safety and cause a large number of victims every year. Of course, in order to reduce these risks, the demand for traffic accidents must increase. This brief presents a new solution using technology to improve road safety. The collision detection system is designed to detect and alert police and emergency services in the event of a collision. The system uses a combination of sensors, cameras, and machine learning algorithms to accurately identify accidents, analyze their severity, and send important information to first responders; thus, re
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Liu, Haiqing, Na Li, Deyong Guan, and Laxmisha Rai. "Data Feature Analysis of Non-Scanning Multi Target Millimeter-Wave Radar in Traffic Flow Detection Applications." Sensors 18, no. 9 (2018): 2756. http://dx.doi.org/10.3390/s18092756.

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The millimeter-wave radar has been widely used in traffic applications. However, little research has been done to install the millimeter-wave radar on the top of a road for detecting road traffic flow at a downward looking direction. In this paper, the vehicle parameters, including the distance, angle and radar cross-section energy, are collected by practical experiments in the aforementioned application scenario. The data features are analyzed from the dimensions of single parameter sampling characteristics and multi-parameter relationships. Further, the correlations of different parameter se
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Pei, Lili, Zhaoyun Sun, Yuxi Han, Wei Li, and Huaixin Zhao. "Highway Event Detection Algorithm Based on Improved Fast Peak Clustering." Mathematical Problems in Engineering 2021 (February 20, 2021): 1–13. http://dx.doi.org/10.1155/2021/7318216.

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Aiming at the mining of traffic events based on large amounts of highway data, this paper proposes an improved fast peak clustering algorithm to process highway toll data. The highway toll data are first analyzed, and a data cleaning method based on the sum of similar coefficients is proposed to process the original data. Next, to avoid the shortcomings of the excessive subjectivity of the original algorithm, an improved fast peak clustering algorithm is proposed. Finally, the improved algorithm is applied to highway traffic condition analysis and abnormal event mining to obtain more accurate
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Wang, Song, Xu Xie, Kedi Huang, Junjie Zeng, and Zimin Cai. "Deep Reinforcement Learning-Based Traffic Signal Control Using High-Resolution Event-Based Data." Entropy 21, no. 8 (2019): 744. http://dx.doi.org/10.3390/e21080744.

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Reinforcement learning (RL)-based traffic signal control has been proven to have great potential in alleviating traffic congestion. The state definition, which is a key element in RL-based traffic signal control, plays a vital role. However, the data used for state definition in the literature are either coarse or difficult to measure directly using the prevailing detection systems for signal control. This paper proposes a deep reinforcement learning-based traffic signal control method which uses high-resolution event-based data, aiming to achieve cost-effective and efficient adaptive traffic
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Bao, Lixia, Qiulan Wang, Wenliang Qu, and Xianglun Mo. "Research on Highway Traffic Event Detection Method Based on Image Processing." IOP Conference Series: Earth and Environmental Science 791, no. 1 (2021): 012193. http://dx.doi.org/10.1088/1755-1315/791/1/012193.

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Mane, Deepak T., Sunil Sangve, Sahil Kandhare, Saurabh Mohole, Sanket Sonar, and Satej Tupare. "Real-Time Vehicle Accident Recognition from Traffic Video Surveillance using YOLOV8 and OpenCV." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (2023): 250–58. http://dx.doi.org/10.17762/ijritcc.v11i5s.6651.

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The automatic detection of traffic accidents is a significant topic in traffic monitoring systems. It can reduce irresponsible driving behavior, improve emergency response, improve traffic management, and encourage safer driving practices. Computer vision can be a promising technique for automatic accident detection because it provides a reliable, automated, and speedy accident detection system that can improve emergency response times and ultimately save lives. This paper proposed an ensemble model that uses the YOLOv8 approach for efficient and precise event detection. The model framework's
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