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Journal articles on the topic 'Traffic pattern recognition'

Author: Grafiati

Published: 1 June 2024

Last updated: 31 July 2025

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1

Zhang, Yuanqiang, and Weifeng Li. "Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data." Sensors 22, no. 16 (2022): 6307. http://dx.doi.org/10.3390/s22166307.

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Maritime traffic pattern recognition plays a major role in intelligent transportation services, ship monitoring, route planning, and other fields. Facilitated by the establishment of terrestrial networks and satellite constellations of the automatic identification system (AIS), large quantities of spatial and temporal information make ships’ paths trackable and are useful in maritime traffic pattern research. The maritime traffic pattern may vary with changes in the traffic environment, so the recognition method of the maritime traffic pattern should be adaptable to changes in the traffic envi

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Wu, Jian, Zhiming Cui, Victor S. Sheng, Yujie Shi, and Pengpeng Zhao. "Mixed Pattern Matching-Based Traffic Abnormal Behavior Recognition." Scientific World Journal 2014 (2014): 1–12. http://dx.doi.org/10.1155/2014/834013.

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A motion trajectory is an intuitive representation form in time-space domain for a micromotion behavior of moving target. Trajectory analysis is an important approach to recognize abnormal behaviors of moving targets. Against the complexity of vehicle trajectories, this paper first proposed a trajectory pattern learning method based on dynamic time warping (DTW) and spectral clustering. It introduced the DTW distance to measure the distances between vehicle trajectories and determined the number of clusters automatically by a spectral clustering algorithm based on the distance matrix. Then, it

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WANG, JING, PENGJIAN SHANG, and XIAOJUN ZHAO. "A NEW TRAFFIC SPEED FORECASTING METHOD BASED ON BI-PATTERN RECOGNITION." Fluctuation and Noise Letters 10, no. 01 (2011): 59–75. http://dx.doi.org/10.1142/s0219477511000405.

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Short-term traffic forecasting has played a key role in supporting the need of proactive and dynamic traffic control system. K-nearest neighbor (KNN) nonparametric regression models have been widely used in traffic prediction. KNN models give predictions based on the future state of traffic speed that is completely determined by the current state, but with no dependence on the past sequences of traffic speed that produced the current state. In fact, traffic speed is not completely random in nature, and some patterns repeat in the traffic stream. In this paper, we proposed a methodology called

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Hong, Rongrong, Wenming Rao, Dong Zhou, Chengchuan An, Zhenbo Lu, and Jingxin Xia. "Commuting Pattern Recognition Using a Systematic Cluster Framework." Sustainability 12, no. 5 (2020): 1764. http://dx.doi.org/10.3390/su12051764.

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Identifying commuting patterns for an urban network is important for various traffic applications (e.g., traffic demand management). Some studies, such as the gravity models, urban-system-model, K-means clustering, have provided insights into the investigation of commuting pattern recognition. However, commuters’ route feature is not fully considered or not accurately characterized. In this study, a systematic framework considering the route feature for commuting pattern recognition was developed for urban road networks. Three modules are included in the proposed framework. These modules were

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Hasan, Md Mehedi, and Jun-Seok Oh. "GIS-Based Multivariate Spatial Clustering for Traffic Pattern Recognition using Continuous Counting Data." Transportation Research Record: Journal of the Transportation Research Board 2674, no. 10 (2020): 583–98. http://dx.doi.org/10.1177/0361198120937019.

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Traffic count stations play a key role in measuring roadway characteristics and traffic performance by collecting and monitoring travel behavior and vehicle data. Continuous counting stations (CCSs), which count traffic volumes continuously throughout the year, are used to develop seasonal adjustment factors to convert short-term traffic counts (average daily traffic) to annual average daily traffic (AADT). As data collection is conducted at limited locations, many state Departments of Transportation (DOTs) group the CCSs based on different traffic patterns and estimate the AADT at specific lo

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Ogunkan, Stella Kehinde, and David Victor Ogunkan. "Traffic Pattern Recognition Using IoT Sensors and Machine Learning: A Comprehensive Review." Int'l Journal of Management Innovation Systems 9, no. 1 (2025): 13. https://doi.org/10.5296/ijmis.v9i1.22342.

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The increasing complexity of urban traffic systems presents significant challenges for effective management and congestion reduction. Traditional traffic monitoring methods, often limited by static data and reactive approaches, are inadequate to address dynamic urban mobility issues. This study explores the integration of Internet of Things (IoT) sensors and machine learning (ML) in traffic pattern recognition, which offers real-time, data-driven solutions for proactive traffic management. IoT sensors, such as cameras, GPS, and LIDAR, provide extensive, real-time data on vehicle flow, traffic

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Tettamanti, Tamás, Alfréd Csikós, Krisztián Balázs Kis, Zsolt János Viharos, and István Varga. "PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK." Transport 33, no. 4 (2018): 959–70. http://dx.doi.org/10.3846/16484142.2017.1352027.

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A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in

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Wang, Qi, Min Lu, and Qingquan Li. "Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories." Sensors 20, no. 4 (2020): 1084. http://dx.doi.org/10.3390/s20041084.

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Urban traffic pattern reflects how people move and how goods are transported, which is crucial for traffic management and urban planning. With the development of sensing techniques, accumulated sensor data are captured for monitoring vehicles, which also present the opportunities of big transportation data, especially for real-time interactive traffic pattern analysis. We propose a three-layer framework for the recognition and visualization of multiscale traffic patterns. The first layer computes the middle-tier synopses at fine spatial and temporal scales, which are indexed and stored in a ge

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Qin, Guo Feng, Yu Sun, and Qi Yan Li. "Recognition of Vehicles on Geometric Morphology." Advanced Materials Research 217-218 (March 2011): 27–32. http://dx.doi.org/10.4028/www.scientific.net/amr.217-218.27.

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Detection of vehicles plays an important role in the area of the modern intelligent traffic management. And the pattern recognition is a hot issue in the area of computer vision. This article introduces an Automobile Automatic Recognition System based on image. It begins with the structures of the system. Then detailed methods for implementation are discussed. This system take use of a camera to get traffic images, then after image pretreatment and segmentation, do the works of feature extraction, template matching and pattern recognition, to identify different models and get vehicular traffic

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Ishak, Sherif S., and Haitham M. Al-Deek. "Fuzzy ART Neural Network Model for Automated Detection of Freeway Incidents." Transportation Research Record: Journal of the Transportation Research Board 1634, no. 1 (1998): 56–63. http://dx.doi.org/10.3141/1634-07.

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Pattern recognition techniques such as artificial neural networks continue to offer potential solutions to many of the existing problems associated with freeway incident-detection algorithms. This study focuses on the application of Fuzzy ART neural networks to incident detection on freeways. Unlike back-propagation models, Fuzzy ART is capable of fast, stable learning of recognition categories. It is an incremental approach that has the potential for on-line implementation. Fuzzy ART is trained with traffic patterns that are represented by 30-s loop-detector data of occupancy, speed, or a com

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Sohn, So Young, and Hyungwon Shin. "Pattern recognition for road traffic accident severity in Korea." Ergonomics 44, no. 1 (2001): 107–17. http://dx.doi.org/10.1080/00140130120928.

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Li, Huo You, Jian Jun Li, and Jian Yang Li. "Pattern Recognition of Group Control Object Based on Fuzzy Neural Network." Applied Mechanics and Materials 29-32 (August 2010): 2726–32. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.2726.

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This paper has proposed a concept of Group Control Object, taking an example according to experimental data of elevator group control object of a building; we apply fuzzy logic and neural network to recognize the pattern of the group control object. With the aid of the fuzzy neural network, this task designs to identify the different passenger flow, and classify it into the six models such as the up-peak service model, down-peak service, two way traffic model, four way traffic model, the balanced bi-story traffic model and free duty traffic model. Then it constructs five-level fuzzy neural net

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Kehagias, Dionysios, Athanasios Salamanis, and Dimitrios Tzovaras. "Speed pattern recognition technique for short-term traffic forecasting based on traffic dynamics." IET Intelligent Transport Systems 9, no. 6 (2015): 646–53. http://dx.doi.org/10.1049/iet-its.2014.0213.

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Wang, Wei Zhi, and Bing Han Liu. "An Intelligent Recognition Algorithm on Traffic Safety States." Applied Mechanics and Materials 433-435 (October 2013): 1388–91. http://dx.doi.org/10.4028/www.scientific.net/amm.433-435.1388.

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Traffic safety states can be divided into safe and dangerous according to the attributes of video images of traffic safety states. We propose a synergic neural network recognition model based on prototype pattern by analyzing various methods on intelligent video processing. Our proposed method realizes real time classification of traffic safety states with high accuracy of traffic safety states recognition. The experimental results validate that the accuracy of classification of proposed method arrives at 87.5%, increased by 16.2% compared to traditional neural network methods.

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Belim, S. V., and E. V. Khiryanov. "Hierarchical Traffic Sign Recognition System." Informacionnye Tehnologii 28, no. 8 (2022): 417–23. http://dx.doi.org/10.17587/it.28.417-423.

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A hierarchical system of classifiers for recognizing traffic signs from their images is proposed in the article. Only signs that fit into a square are considered. The traffic signs set is analyzed by their shape, color and basic features. A hierarchy of classes for traffic signs on the details of their images is proposed. The traffic sign image recognition algorithm uses this hierarchy. The algorithm only works with localized signs. The localization algorithm is not considered. Image preprocessing is performed at each level of the hierarchy for traffic sign features. Different classifiers are

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Liang, Juanzhu, Shunyi Xie, and Jinjian Bao. "Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map." Sustainability 16, no. 3 (2024): 1300. http://dx.doi.org/10.3390/su16031300.

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Detecting the spatial organization patterns of urban networks with multiple traffic flows from the perspective of complex networks and traffic behavior will help to optimize the urban spatial structure and thereby promote the sustainable development of the city. However, there are notable differences in regional spatial patterns among the different modes of transportation. Based on the road, railway, and air frequency data, this article investigates the spatial distribution and accessibility patterns of multiple transportation flows in the Yangtze River Economic Belt. Next, we use the TCD (Tra

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Gong, Fa Ming, and Hai Juan Li. "Traffic Sign Detection and Pattern Recognition Based on Binary Tree Support Vector Machines." Advanced Materials Research 204-210 (February 2011): 1394–98. http://dx.doi.org/10.4028/www.scientific.net/amr.204-210.1394.

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This paper presents an automatic road sign detection and recognition system based on binary tree SVM. Color based segmentation techniques are employed for traffic sign detection. The coordinates position of traffic sign in images used for shape classification are obtained by orthogonal projection. An algorithm based on Hough transform was proposed to achieve better shape classification performance.Recognition of traffic signs are implemented using binary tree multi- classifer SVM with geometry semantic feature as the feature vector

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Liu, Jia, Peng Gao, Jian Yuan, and Xuetao Du. "An Effective Method of Monitoring the Large-Scale Traffic Pattern Based on RMT and PCA." Journal of Probability and Statistics 2010 (2010): 1–16. http://dx.doi.org/10.1155/2010/375942.

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Mechanisms to extract the characteristics of network traffic play a significant role in traffic monitoring, offering helpful information for network management and control. In this paper, a method based on Random Matrix Theory (RMT) and Principal Components Analysis (PCA) is proposed for monitoring and analyzing large-scale traffic patterns in the Internet. Besides the analysis of the largest eigenvalue in RMT, useful information is also extracted from small eigenvalues by a method based on PCA. And then an appropriate approach is put forward to select some observation points on the base of th

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de la Escalera, A., J. Ma Armingol, and M. Mata. "Traffic sign recognition and analysis for intelligent vehicles." Image and Vision Computing 21, no. 3 (2003): 247–58. http://dx.doi.org/10.1016/s0262-8856(02)00156-7.

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Ajdinović, Nadina, Semina Nurkić, Jasmina Baraković Husić, and Sabina Baraković. "Recognition of traffic generated by WebRTC communication." Science, Engineering and Technology 1, no. 1 (2021): 15–20. http://dx.doi.org/10.54327/set2021/v1.i1.8.

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Network traffic recognition serves as a basic condition for network operators to differentiate and prioritize traffic for a number of purposes, from guaranteeing the Quality of Service (QoS), to monitoring safety, as well as monitoring and detecting anomalies. Web Real-Time Communication (WebRTC) is an open-source project that enables real-time audio, video, and text communication among browsers. Since WebRTC does not include any characteristic pattern for semantically based traffic recognition, this paper proposes models for recognizing traffic generated during WebRTC audio and video communic

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Ajdinović, Nadina, Semina Nurkić, Husić Jasmina Baraković, and Sabina Baraković. "Recognition of traffic generated by WebRTC communication." Science, Engineering and Technology 1, no. 1 (2021): 15–20. https://doi.org/10.54327/set2021/v1.i1.8.

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Network traffic recognition serves as a basic condition for network operators to differentiate and prioritize traffic for a number of purposes, from guaranteeing the Quality of Service (QoS), to monitoring safety, as well as monitoring and detecting anomalies. Web Real-Time Communication (WebRTC) is an open-source project that enables real-time audio, video, and text communication among browsers. Since WebRTC does not include any characteristic pattern for semantically based traffic recognition, this paper proposes models for recognizing traffic generated during WebRTC audio and video communic

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Saha, Rajib, Mosammat Tahnin Tariq, Mohammed Hadi, and Yan Xiao. "Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling." Journal of Advanced Transportation 2019 (December 30, 2019): 1–12. http://dx.doi.org/10.1155/2019/1628417.

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There has been an increasing interest in recent years in using clustering analysis for the identification of traffic patterns that are representative of traffic conditions in support of transportation system operations and management (TSMO); integrated corridor management; and analysis, modeling, and simulation (AMS). However, there has been limited information to support agencies in their selection of the most appropriate clustering technique(s), associated parameters, the optimal number of clusters, clustering result analysis, and selecting observations that are representative of each cluste

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Hao, Ruochen, Ling Wang, Wanjing Ma, and Chunhui Yu. "Estimating Signal Timing of Actuated Signal Control Using Pattern Recognition under Connected Vehicle Environment." Promet - Traffic&Transportation 33, no. 1 (2021): 153–63. http://dx.doi.org/10.7307/ptt.v33i1.3555.

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The Signal Phase and Timing (SPaT) message is an important input for research and applications of Connected Vehicles (CVs). However, the actuated signal controllers are not able to directly give the SPaT information since the SPaT is influenced by both signal control logic and real-time traffic demand. This study elaborates an estimation method which is proposed according to the idea that an actuated signal controller would provide similar signal timing for similar traffic states. Thus, the quantitative description of traffic states is important. The traffic flow at each approaching lane has b

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Sun, Yizhen, Jianjiang Yu, Jianwei Tian, Zhongwei Chen, Weiping Wang, and Shigeng Zhang. "IoT-IE: An Information-Entropy-Based Approach to Traffic Anomaly Detection in Internet of Things." Security and Communication Networks 2021 (December 30, 2021): 1–13. http://dx.doi.org/10.1155/2021/1828182.

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Security issues related to the Internet of Things (IoTs) have attracted much attention in many fields in recent years. One important problem in IoT security is to recognize the type of IoT devices, according to which different strategies can be designed to enhance the security of IoT applications. However, existing IoT device recognition approaches rarely consider traffic attacks, which might change the pattern of traffic and consequently decrease the recognition accuracy of different IoT devices. In this work, we first validate by experiments that traffic attacks indeed decrease the recogniti

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Moulik, Bedatri, Sanmukh Kaur, and Muhammad Ijaz. "Optimized Energy Consumption of Electric Vehicles with Driving Pattern Recognition for Real Driving Scenarios." Algorithms 18, no. 4 (2025): 204. https://doi.org/10.3390/a18040204.

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Energy management strategies (EMS) in the context of electric or hybrid vehicles can optimize the available energy by minimizing consumption. Most optimization-based EMS are not real-time-applicable for an accurate estimation of future consumption. The performance of these strategies also strongly depends on the driving patterns, which may be influenced by road and traffic conditions, among other factors such as driving style, weather, vehicle type, etc. The primary contribution of this work is to develop a novel two-layer driving pattern recognition (DPR) system for roadway type and traffic c

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Buscema, Paolo Massimo, Giulia Massini, Giovanbattista Raimondi, Giuseppe Caporaso, Marco Breda, and Riccardo Petritoli. "A Pattern Recognition Analysis of Vessel Trajectories." Algorithms 16, no. 9 (2023): 414. http://dx.doi.org/10.3390/a16090414.

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The automatic identification system (AIS) facilitates the monitoring of ship movements and provides essential input parameters for traffic safety. Previous studies have employed AIS data to detect behavioral anomalies and classify vessel types using supervised and unsupervised algorithms, including deep learning techniques. The approach proposed in this work focuses on the recognition of vessel types through the “Take One Class at a Time” (TOCAT) classification strategy. This approach pivots on a collection of adaptive models rather than a single intricate algorithm. Using radar data, these mo

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Xi, Jianfeng, Yunhe Zhao, Zhiqiang Li, Yizhou Jiang, Wenwen Feng, and Tongqiang Ding. "A Recognition Method of Truck Drivers’ Braking Patterns Based on FCM-LDA2vec." International Journal of Environmental Research and Public Health 19, no. 23 (2022): 15959. http://dx.doi.org/10.3390/ijerph192315959.

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Taking truck drivers’ braking patterns as the research objects, this study used a large amount of truck running data. A recognition method of truck drivers’ braking patterns was proposed to determine the distribution of braking patterns during the operation of trucks. First, the segmented data of braking behaviors were collected in order to extract 25 characteristic parameters. Additionally, seven main correlation factors were obtained by dimensionality reduction. The FCM clustering algorithm and CH scores were used to identify nine categories of truck drivers’ braking behaviors. Then the LDA2

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Chen, Juan, Kepei Qi, and Shiyu Zhu. "Traffic travel pattern recognition based on sparse Global Positioning System trajectory data." International Journal of Distributed Sensor Networks 16, no. 10 (2020): 155014772096846. http://dx.doi.org/10.1177/1550147720968469.

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This article mainly uses sparse Global Positioning System trajectory data to identify traffic travel pattern. In this article, the data are preprocessed and the eigenvalues are calculated. Then, the Global Positioning System track points are identified and extracted by walking and non-walking segments. Finally, the three machine learning models of support-vector machine, decision tree, and convolutional neural network are used for comparison experiments. The innovation of this article is to propose a walking and non-walking identification method based on density-based spatial clustering of app

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Oh, Se-do, Young-jin Kim, and Ji-sun Hong. "Urban Traffic Flow Prediction System Using a Multifactor Pattern Recognition Model." IEEE Transactions on Intelligent Transportation Systems 16, no. 5 (2015): 2744–55. http://dx.doi.org/10.1109/tits.2015.2419614.

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YU, Rong, Guoxiang WANG, Jiyuan ZHENG, and Haiyan WANG. "Urban Road Traffic Condition Pattern Recognition Based on Support Vector Machine." Journal of Transportation Systems Engineering and Information Technology 13, no. 1 (2013): 130–36. http://dx.doi.org/10.1016/s1570-6672(13)60097-5.

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Lo, Shih-Ching. "Expectation-maximization based algorithm for pattern recognition in traffic speed distribution." Mathematical and Computer Modelling 58, no. 1-2 (2013): 449–56. http://dx.doi.org/10.1016/j.mcm.2012.11.004.

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Huang, Mingxia, Xuebo Yan, Zhu Bai, Haiqiang Zhang, and Zeen Xu. "Key Technologies of Intelligent Transportation Based on Image Recognition and Optimization Control." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (2020): 2054024. http://dx.doi.org/10.1142/s0218001420540245.

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With the development of digital image processing technology, the application scope of image recognition is more and more wide, involving all aspects of life. In particular, the rapid development of urbanization and the popularization and application of automobiles in recent years have led to a sharp increase in traffic problems in various countries, resulting in intelligent transportation technology based on image processing optimization control becoming an important research field of intelligent systems. Aiming at the application demand analysis of intelligent transportation system, this pape

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Zou, Ying, Dahu Wang, and Leian Liu. "Research on Human Movement Target Recognition Algorithm in Complex Traffic Environment." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 05 (2019): 2050012. http://dx.doi.org/10.1142/s0218001420500123.

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With the increase in the total population of the society and the continuous increase in the number of trips, the traffic pressures faced by people are increasing. With the development and advancement of computer technology, the emergence of intelligent transportation provides a better way to solve the problem of effectively alleviating traffic pressure and reducing the incidence of traffic accidents. In recent years, intelligent traffic monitoring system, as one of the important branches in the field of intelligent transportation, has also received more and more attention. Among them, video-ba

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Mai, Chubin. "The Application of Deep Learning Algorithms in Traffic Sign Recognition." Applied and Computational Engineering 163, no. 1 (2025): 50–58. https://doi.org/10.54254/2755-2721/2025.24721.

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Traffic sign recognition is a critical component in the field of autonomous driving. In practice, recognising a wide range of different symbol classes with very high accuracy, robust performance, and rapid processing speed is essential. Traffic signs are designed for human readability, however, for computer systems, classifying traffic signs remains a complex pattern recognition problem. Image processing and machine learning algorithms are continually improving to improve this capability. Among them, deep-reinforcement learning (DRL) has become a cutting-edge technology that excels in feature

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Lin, Yueh-lung, and Conghua Wen. "Vehicle Vision Robust Detection and Recognition Method." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (2019): 2055020. http://dx.doi.org/10.1142/s0218001420550204.

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With the rapid growth of the global economy, the global car ownership is also increasing year by year, which has caused a series of problems, the most prominent of which is traffic congestion and traffic accidents. In order to solve the traffic problem, all countries are actively studying the intelligent transportation system, and one of the important research contents of the intelligent transportation system is vehicle detection. Vehicle detection based on vision is to capture vehicle images in the driving environment through a camera, and then use computer vision recognition technology for v

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Hu, Yiyang, Zhaohang He, and Huayu Jiang. "Deep Learning's Current and Future Applications in Predicting Vehicle Traffic Flow." Highlights in Science, Engineering and Technology 119 (December 11, 2024): 524–32. https://doi.org/10.54097/dqxg8v21.

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With the acceleration of urbanization and the continuous growth of traffic flow, accurate prediction of highway traffic flow has become a key demand for optimizing traffic management and alleviating congestion. Traditional prediction methods are often inefficient in dealing with complex traffic patterns, while deep learning technology brings new solutions for traffic flow prediction with its powerful feature learning and pattern recognition capabilities. This paper reviews deep learning applications' current state and future trends in highway traffic flow prediction. It introduces deep learnin

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Adala Mahdi Jiyad. "A Hybrid Fuzzy-Neural Network Approach for Advanced Pattern Recognition and Predictive Analytic." Journal of Information Systems Engineering and Management 10, no. 32s (2025): 22–36. https://doi.org/10.52783/jisem.v10i32s.5183.

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Organizations must address cybersecurity as a fundamental issue in the digital era, where they face advanced and continuous cyber threats. Intrusion detection systems based on traditional methods face challenges in multiple traffic classification because they enforce static threshold boundaries and possess restricted learning capabilities. This research envisions a new hybrid fuzzy-neural network solution for sophisticated pattern recognition and predictive analysis in intrusion detection. The main goal is to improve detection accuracy and lower false positives by merging the subtle reasoning

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Shanmugaraj.S et al. "Auto Detection of Number Plate of Person without Helmet." International Journal on Recent and Innovation Trends in Computing and Communication 7, no. 3 (2019): 21–24. http://dx.doi.org/10.17762/ijritcc.v7i3.5252.

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Automated Number Plate Recognition organization would greatly enhance the ability of police to detect criminal commotion that involves the use of motor vehicles. Automatic video investigation from traffic surveillance cameras is a fast-emerging field based on workstation vision techniques. It is a key technology to public safety, intelligent transport system (ITS) and for efficient administration of traffic without wearing helmet. In recent years, there has been an increased scope for involuntary analysis of traffic activity. It defines video analytics as computer-vision-based supervision algo

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Pan, Qingchao, and Haohua Zhang. "Key Algorithms of Video Target Detection and Recognition in Intelligent Transportation Systems." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 09 (2019): 2055016. http://dx.doi.org/10.1142/s0218001420550162.

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With the popularization of video detection and recognition systems and the advancement of video image processing technology, the application research of intelligent transportation systems based on computer vision technology has received more and more attention. It comprehensively utilizes image processing, pattern recognition, artificial intelligence and other technologies. It also involves processing and analyzing the video image sequence collected by the detection system, intelligently understanding the video content and making processing, and dealing with various problems such as accident i

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Ruiqi, Luo, Zhong Xian, Zhong Luo, and Li Lin. "Research on the intelligent judgment of traffic congestion in intelligent traffic based on pattern recognition technology." Cluster Computing 22, S5 (2018): 12581–88. http://dx.doi.org/10.1007/s10586-017-1684-8.

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Saeipour, Parisa, Parvin Sarbakhsh, Saman Salemi, and Fatemeh Bakhtari Aghdam. "A Fuzzy Clustering Approach to Identify Pedestrians’ Traffic Behavior Patterns." Journal of Research in Health Sciences 23, no. 3 (2023): e00592. http://dx.doi.org/10.34172/jrhs.2023.127.

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Background: Pattern recognition of pedestrians’ traffic behavior can enhance the management efficiency of interested groups by targeting access to them and facilitating planning via more specific surveys. This study aimed to evaluate the pedestrians’ traffic behavior pattern by fuzzy clustering algorithm and assess the factors related to higher-risk traffic behavior of pedestrians. Study Design: This study is a secondary methodological study based on the data from a cross-sectional study. Methods: The fuzzy c-means (FCM), as a machine learning clustering method, was conducted to identify the p

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Pagliaro, Antonio, Antonio Alessio Compagnino, and Pierluca Sangiorgi. "Advanced AI and Machine Learning Techniques for Time Series Analysis and Pattern Recognition." Applied Sciences 15, no. 6 (2025): 3165. https://doi.org/10.3390/app15063165.

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Time series analysis and pattern recognition are cornerstones for innovation across diverse domains. In finance, these techniques enable market prediction and risk assessment. Astrophysicists use them to detect various phenomena and analyze data. Environmental scientists track ecosystem changes and pollution patterns, while healthcare professionals monitor patient vitals and disease progression. Transportation systems optimize traffic flow and predict maintenance needs. Energy providers balance grid loads and forecast consumption. Climate scientists model atmospheric changes and extreme weathe

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Tanuwidjaya, Kevin, Ericson -, and Lukman Hakim. "KLASIFIKASI RAMBU LALU LINTAS MENGGUNAKAN DECISION TREE J48 DAN LOCAL BINARY PATTERN." JITTER : Jurnal Ilmiah Teknologi dan Komputer 3, no. 1 (2022): 779. http://dx.doi.org/10.24843/jtrti.2022.v03.i01.p13.

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Automatic steering technology or autopilot in cars is developing rapidly. This feature makes it easier for the driver because the car can run according to the program directions. The driver of course can still take control of the car manually as desired, so it is possible for the driver to violate traffic signs, whether intentionally or not. This study seeks to create a traffic sign recognition system that can help reduce violations committed by drivers knowingly. The test is carried out using a combination of the Local Binary Pattern algorithm as feature extraction and Decision Tree J48 algor

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Escalera, Sergio, Oriol Pujol та Petia Radeva. "Traffic sign recognition system with β -correction". Machine Vision and Applications 21, № 2 (2008): 99–111. http://dx.doi.org/10.1007/s00138-008-0145-z.

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Mrgole, Anamarija L., and Drago Sever. "Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction." PROMET - Traffic&Transportation 29, no. 1 (2017): 13–22. http://dx.doi.org/10.7307/ptt.v29i1.2116.

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The main purpose of this study was to investigate the use of various chaotic pattern recognition methods for traffic flow prediction. Traffic flow is a variable, dynamic and complex system, which is non-linear and unpredictable. The emergence of traffic flow congestion in road traffic is estimated when the traffic load on a specific section of the road in a specific time period is close to exceeding the capacity of the road infrastructure. Under certain conditions, it can be seen in concentrating chaotic traffic flow patterns. The literature review of traffic flow theory and its connection wit

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Adeyemi, Oladimeji, Martins Irhebhude, and Adeola Kolawole. "Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques." Advances in Image and Video Processing 7, no. 5 (2019): 30–42. http://dx.doi.org/10.14738/aivp.75.7205.

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This paper presents a image processing technique for speed breaker, road marking detection and recognition. An Optical Character Recognition (OCR) algorithm was used to recognize traffic signs such as “STOP” markings and a Hough transform was used to detect line markings which serves as a pre-processing stage to determine when the proposed technique does OCR or speed breaker recognition. The stopline inclusion serves as a pre-processing stage that tells the system when to perform stop marking recognition or speed breaker recognition. Image processing techniques was used for the processing of f

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Ge, Jun Wei, Ming Zhao, and Yi Qiu Fang. "A Behavior-Based Rapid Method for P2P Traffic Identification." Applied Mechanics and Materials 380-384 (August 2013): 3661–66. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.3661.

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This paper presents a rapid identification model and analyses the behavioral characteristics which is different from non-P2P applications on link pattern through analysis on three P2P applications. This method classifies P2P applications in the background and improves the recognition efficiency through the effective combination of behavioral characteristics and valid flows filter on the premise of maintaining the recognition accuracy. In the packet processing, matching frequency parameter has been using to increase matching efficiency. The experimental results show that P2P traffic can be effe

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Yang, Sung-Min, and Kang-Hyun Jo. "HOG based Pedestrian Detection and Behavior Pattern Recognition for Traffic Signal Control." Journal of Institute of Control, Robotics and Systems 19, no. 11 (2013): 1017–21. http://dx.doi.org/10.5302/j.icros.2013.13.1858.

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Yin, Shouyi, Peng Ouyang, Leibo Liu, Yike Guo, and Shaojun Wei. "Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature." Sensors 15, no. 1 (2015): 2161–80. http://dx.doi.org/10.3390/s150102161.

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Cerreto, Fabrizio, Bo Friis Nielsen, Otto Anker Nielsen, and Steven S. Harrod. "Application of Data Clustering to Railway Delay Pattern Recognition." Journal of Advanced Transportation 2018 (2018): 1–18. http://dx.doi.org/10.1155/2018/6164534.

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K-means clustering is employed to identify recurrent delay patterns on a high traffic railway line north of Copenhagen, Denmark. The clusters identify behavioral patterns in the very large (“big data”) datasets generated automatically and continuously by the railway signal system. The results reveal the conditions where corrective actions are necessary, showing the cases where recurrent delay patterns take place. Delay profiles and delay change profiles are generated from timestamps to compare different train runs and to partition the set of observations into groups of similar elements. K-mean

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