Articoli di riviste: "Traffic pattern recognition"

1

Zhang, Yuanqiang, e Weifeng Li. "Dynamic Maritime Traffic Pattern Recognition with Online Cleaning, Compression, Partition, and Clustering of AIS Data". Sensors 22, n. 16 (22 agosto 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 environment. To achieve this goal, a dynamic maritime traffic pattern recognition method is presented using AIS data, which are cleaned, compressed, partitioned, and clustered online. Old patterns are removed as expired trajectories are deleted, and new patterns are created as new trajectories are added. This method is suitable for processing massive stream data. Experiments show that when the marine traffic route changes due to the navigation environment, the maritime traffic pattern adjusts automatically.

2

Wu, Jian, Zhiming Cui, Victor S. Sheng, Yujie Shi e 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 clusters sample data points into different clusters. After the spatial patterns and direction patterns learned from the clusters, a recognition method for detecting vehicle abnormal behaviors based on mixed pattern matching was proposed. The experimental results show that the proposed technical scheme can recognize main types of traffic abnormal behaviors effectively and has good robustness. The real-world application verified its feasibility and the validity.

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WANG, JING, PENGJIAN SHANG e XIAOJUN ZHAO. "A NEW TRAFFIC SPEED FORECASTING METHOD BASED ON BI-PATTERN RECOGNITION". Fluctuation and Noise Letters 10, n. 01 (marzo 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 bi-pattern recognition KNN model (BKNN) which uses pattern recognition technique twice in the searching process to predict the future traffic state. Then the proposed BKNN model is applied to predict one day real traffic speed series of two sites, which are located near the North 2nd and 3rd Ring Road in Beijing, respectively. With the optimal neighbor and pattern size, the BKNN model provides good predictions. Moreover, in comparison with the KNN model, PKNN model (a modified model based on KNN), seasonal autoregressive integrated moving average (SARIMA) and the artificial neural networks (ANN), the BKNN model appears to be the most promising and robust of the five models to provide better short-term traffic prediction.

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Hong, Rongrong, Wenming Rao, Dong Zhou, Chengchuan An, Zhenbo Lu e Jingxin Xia. "Commuting Pattern Recognition Using a Systematic Cluster Framework". Sustainability 12, n. 5 (27 febbraio 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 proposed based on automatic license plate recognition (ALPR) data. First, the temporal and spatial features of individual vehicles were extracted based on the trips detected by ALPR sensors, then a hierarchical clustering technique was applied to classify the detected vehicles and the ratio of commuting trips was derived. Based on the ratio of commuting trips, the temporal and spatial commuting patterns were investigated, respectively. The proposed method was finally implemented in a ring expressway of Kunshan, China. The results showed that the method can accurately extract the commuting patterns. Further investigations revealed the dynamic temporal-spatial features of commuting patterns. The findings of this study demonstrate the effectiveness of the proposed method in mining commuting patterns at urban traffic networks.

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Hasan, Md Mehedi, e 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, n. 10 (24 luglio 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 locations by considering seasonal adjustment factors. Computer-based clustering approaches are widely used in grouping continuous traffic data for their accuracy in traffic pattern recognition. However, most of the clustering techniques do not consider the spatial constraints and therefore overlooked the locational proximity and inference from nearby traffic data. In this study, a GIS-based multivariate spatial clustering approach was developed to recognize statewide traffic patterns based on temporal and spatial variables. A total of 12 optimal clusters were automatically computed and labeled based on the proposed clustering algorithm. The proposed clustering approach was compared and validated based on machine learning classifiers. The results showed that it outperformed the traditional Michigan DOT clustering approach and was consistent in nature across different years. The model was applied to estimate the AADT, and good accuracy was detected relative to other approaches. The proposed clustering method offers a new approach to group traffic patterns by simultaneously incorporating proximity and similarity of traffic data.

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Tettamanti, Tamás, Alfréd Csikós, Krisztián Balázs Kis, Zsolt János Viharos e István Varga. "PATTERN RECOGNITION BASED SPEED FORECASTING METHODOLOGY FOR URBAN TRAFFIC NETWORK". Transport 33, n. 4 (5 dicembre 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 incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic.

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Wang, Qi, Min Lu e Qingquan Li. "Interactive, Multiscale Urban-Traffic Pattern Exploration Leveraging Massive GPS Trajectories". Sensors 20, n. 4 (17 febbraio 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 geodatabase. The second layer uses synopses to efficiently extract multiscale traffic patterns. The third layer supports real-time interactive visual analytics for intuitive explorations by end users. An experiment in Shenzhen on taxi GPS trajectories that were collected over one month was conducted. Multiple traffic patterns are recognized and visualized in real-time. The results show the satisfactory performance of proposed framework in traffic analysis, which will facilitate traffic management and operation.

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Qin, Guo Feng, Yu Sun e Qi Yan Li. "Recognition of Vehicles on Geometric Morphology". Advanced Materials Research 217-218 (marzo 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 statistics. Finally, the implementation of the system is introduced. The algorithms of recognized process were verified in this application case.

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Ishak, Sherif S., e 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, n. 1 (gennaio 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 combination of both. Traffic patterns observed at the incident time and location are mapped to a group of categories. Each incident category maps incidents with similar traffic pattern characteristics, which are affected by the type and severity of the incident and the prevailing traffic conditions. Detection rate and false alarm rate are used to measure the performance of the Fuzzy ART algorithm. To reduce the false alarm rate that results from occasional misclassification of traffic patterns, a persistence time period of 3 min was arbitrarily selected. The algorithm performance improves when the temporal size of traffic patterns increases from one to two 30-s periods for all traffic parameters. An interesting finding is that the speed patterns produced better results than did the occupancy patterns. However, when combined, occupancy–speed patterns produced the best results. When compared with California algorithms 7 and 8, the Fuzzy ART model produced better performance.

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Sohn, So Young, e Hyungwon Shin. "Pattern recognition for road traffic accident severity in Korea". Ergonomics 44, n. 1 (gennaio 2001): 107–17. http://dx.doi.org/10.1080/00140130120928.

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Li, Huo You, Jian Jun Li e Jian Yang Li. "Pattern Recognition of Group Control Object Based on Fuzzy Neural Network". Applied Mechanics and Materials 29-32 (agosto 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 networks to apply the classification to the elevator group control, and perform the best group control strategy for each model.

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Kehagias, Dionysios, Athanasios Salamanis e Dimitrios Tzovaras. "Speed pattern recognition technique for short-term traffic forecasting based on traffic dynamics". IET Intelligent Transport Systems 9, n. 6 (1 agosto 2015): 646–53. http://dx.doi.org/10.1049/iet-its.2014.0213.

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Wang, Wei Zhi, e Bing Han Liu. "An Intelligent Recognition Algorithm on Traffic Safety States". Applied Mechanics and Materials 433-435 (ottobre 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., e E. V. Khiryanov. "Hierarchical Traffic Sign Recognition System". Informacionnye Tehnologii 28, n. 8 (15 agosto 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 used at different levels of the hierarchy. Preprocessing at the first level uses the segmentation method. Signs are classified by their form in the first level. The edge segment is highlighted in the image of the traffic sign. The Euclidean distance in color space is used to calculate the edge segment. The edge segment outline defines the shape of the sign. Classification of the sign by its shape is carried out based on comparison with the standards. The classifier defines the pattern closest to the sign contour. The efficiency of the classification in the first level is 99.8 %. The second level classifies signs by the color of the edge segment. The effectiveness of this classification is close to 100 %. Internal sign images are obtained after the edge segment is removed from the image. The following levels classify by internal images. These classifiers use artificial neural networks. The efficiency of the system on the GTSRB collection is 98 %.

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de la Escalera, A., J. Ma Armingol e M. Mata. "Traffic sign recognition and analysis for intelligent vehicles". Image and Vision Computing 21, n. 3 (marzo 2003): 247–58. http://dx.doi.org/10.1016/s0262-8856(02)00156-7.

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Liang, Juanzhu, Shunyi Xie e Jinjian Bao. "Analysis of a Multiple Traffic Flow Network’s Spatial Organization Pattern Recognition Based on a Network Map". Sustainability 16, n. 3 (3 febbraio 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 (Transportation Cluster Detection) community discovery algorithm and integrate it with the Baidu Maps API to obtain real-time time cost data to construct a community detection model of a multiple traffic flow network. We integrate the geographical network and topological network to perform feature extraction and rule mining on the spatial organization model of the urban network in the Yangtze River Economic Belt. The results show that: (1) The multiple traffic flow network of the Yangtze River Economic Belt has significant spatial differentiation. The spatial differentiation of aviation and railway networks is mainly concentrated between regions and within provinces, while the imbalance of highway networks is manifested as an imbalance within regions and between provinces. (2) The accessibility pattern of the highway network in the Yangtze River Economic Belt presents a “core–edge” spatial pattern. The accessibility pattern of the railway network generally presents a spatial pattern of “strong in the east and weak in the west”. Compared with sparse road and railway networks, the accessibility pattern of the aviation network shows a spatial pattern of “time and space compression in western cities”. (3) A total of 24 communities were identified through the TCD algorithm, mainly encompassing six major “urban economic communities” located in Guizhou, Yunnan, Anhui, Sichuan–Chongqing, Hubei–Hunan–Jiangxi, and Jiangsu–Zhejiang–Shanghai. (4) The urban network space organization model of the Yangtze River Economic Belt can be roughly divided into three models: the “single-core” model, with Guizhou, Kunming, and Hefei as the core, the “dual-core” model, constructed by Chengdu–Chongqing, and the “multi-core” model, constructed by Changsha–Wuhan–Nanchang and Shanghai–Nanjing–Hangzhou. This model of urban network spatial organization holds indicative significance in revealing the spatial correlation pattern among prefecture-level city units.

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Gong, Fa Ming, e Hai Juan Li. "Traffic Sign Detection and Pattern Recognition Based on Binary Tree Support Vector Machines". Advanced Materials Research 204-210 (febbraio 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 e 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 the eigen analysis. Finally, some experiments about peer-to-peer traffic pattern recognition and backbone aggregate flow estimation are constructed. The simulation results show that using about 10% of nodes as observation points, our method can monitor and extract key information about Internet traffic patterns.

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Saha, Rajib, Mosammat Tahnin Tariq, Mohammed Hadi e Yan Xiao. "Pattern Recognition Using Clustering Analysis to Support Transportation System Management, Operations, and Modeling". Journal of Advanced Transportation 2019 (30 dicembre 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 cluster. This paper investigates and compares the use of a number of existing clustering methods for traffic pattern identifications, considering the above. These methods include the K-means, K-prototypes, K-medoids, four variations of the Hierarchical method, and the combination of Principal Component Analysis for mixed data (PCAmix) with K-means. Among these methods, the K-prototypes and K-means with PCs produced the best results. The paper then provides recommendations regarding conducting and utilizing the results of clustering analysis.

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Ajdinović, Nadina, Semina Nurkić, Jasmina Baraković Husić e Sabina Baraković. "Recognition of traffic generated by WebRTC communication". Science, Engineering and Technology 1, n. 1 (30 aprile 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 communication based on statistical characteristics and usage of machine learning in Weka tool. Five classification algorithms have been used for model development, such as Naive Bayes, J48, Random Forest, REP tree, and Bayes Net. The results show that J48 and BayesNet have the best performances in this experimental case of WebRTC traffic recognition. Future work will be focused on comparison of a wide range of machine learning algorithms using a large enough dataset to improve the significance of the results.

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Hao, Ruochen, Ling Wang, Wanjing Ma e Chunhui Yu. "Estimating Signal Timing of Actuated Signal Control Using Pattern Recognition under Connected Vehicle Environment". Promet - Traffic&Transportation 33, n. 1 (5 febbraio 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 been compared to fluids. The state of fluids can be indicated by state parameters, e.g. speed or height, and its energy, which includes kinetic energy and potential energy. Similar to the fluids, this paper has proposed an energy model for traffic flow, and it has also added the queue length as an additional state parameter. Based on that, the traffic state of intersections can be descripted. Then, a pattern recognition algorithm was developed to identify the most similar historical states and also their corresponding SPaTs, whose average is the estimated SPaT of this second. The result shows that the average error is 3.1 seconds.

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Buscema, Paolo Massimo, Giulia Massini, Giovanbattista Raimondi, Giuseppe Caporaso, Marco Breda e Riccardo Petritoli. "A Pattern Recognition Analysis of Vessel Trajectories". Algorithms 16, n. 9 (29 agosto 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 models are trained by taking into account aspects such as identifiers, position, velocity, and heading. However, it purposefully excludes positional data to counteract the inconsistencies stemming from route variations and irregular sampling frequencies. Using the given data, we achieved a mean accuracy of 83% on a 6-class classification task.

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Xi, Jianfeng, Yunhe Zhao, Zhiqiang Li, Yizhou Jiang, Wenwen Feng e Tongqiang Ding. "A Recognition Method of Truck Drivers’ Braking Patterns Based on FCM-LDA2vec". International Journal of Environmental Research and Public Health 19, n. 23 (30 novembre 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 LDA2vec model was used to identify the distribution of different braking behavior words in braking patterns, and three categories of truck drivers’ braking patterns were identified. The test results showed that the accuracy of the truck drivers’ braking pattern recognition model based on LDA2vec was higher than 85%, and braking patterns of drivers in the daily operation process could be mined from vehicle operation data. Furthermore, through the monitoring and pre-warning of the braking patterns and targeted training of drivers, traffic accidents could be avoided. At the same time, this paper’s results can be used to protect human life and health and reduce environmental pollution caused by traffic congestion or traffic accidents.

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Sun, Yizhen, Jianjiang Yu, Jianwei Tian, Zhongwei Chen, Weiping Wang e Shigeng Zhang. "IoT-IE: An Information-Entropy-Based Approach to Traffic Anomaly Detection in Internet of Things". Security and Communication Networks 2021 (30 dicembre 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 recognition accuracy of existing IoT device recognition approaches; then, we propose an approach called IoT-IE that combines information entropy of different traffic features to detect traffic anomaly. We then enhance the robustness of IoT device recognition by detecting and ignoring the abnormal traffic detected by our approach. Experimental evaluations show that IoT-IE can effectively detect abnormal behaviors of IoT devices in the traffic under eight different types of attacks, achieving a high accuracy value of 0.977 and a low false positive rate of 0.011. It also achieves an accuracy of 0.969 in a multiclassification experiment with 7 different types of attacks.

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Chen, Juan, Kepei Qi e Shiyu Zhu. "Traffic travel pattern recognition based on sparse Global Positioning System trajectory data". International Journal of Distributed Sensor Networks 16, n. 10 (ottobre 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 applications with noise clustering. The method takes into account the continuous state between the geographical distributions, and it has better noise immunity against the influence of external factors. In this process, this article directly achieves better conversion point recognition results through the Global Positioning System track point information, which lays a good foundation for the accuracy of travel pattern recognition. The experimental results of this article show that compared with threshold-based and multi-layer perceptron–based methods, the recognition method based on density-based spatial clustering of applications with noise clustering has the highest accuracy, reaching 82.20%. Then, support-vector machine, decision tree, and convolutional neural network are used for traffic travel pattern recognition. The F1-score of support-vector machine is the highest, reaching 0.84, and the F1-scores of decision tree and convolutional neural network are 0.78 and 0.80, respectively. Finally, the support-vector machine was used for the recognition test to achieve an accuracy of 76.83%.

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Oh, Se-do, Young-jin Kim e Ji-sun Hong. "Urban Traffic Flow Prediction System Using a Multifactor Pattern Recognition Model". IEEE Transactions on Intelligent Transportation Systems 16, n. 5 (ottobre 2015): 2744–55. http://dx.doi.org/10.1109/tits.2015.2419614.

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YU, Rong, Guoxiang WANG, Jiyuan ZHENG e Haiyan WANG. "Urban Road Traffic Condition Pattern Recognition Based on Support Vector Machine". Journal of Transportation Systems Engineering and Information Technology 13, n. 1 (febbraio 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, n. 1-2 (luglio 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 e Zeen Xu. "Key Technologies of Intelligent Transportation Based on Image Recognition and Optimization Control". International Journal of Pattern Recognition and Artificial Intelligence 34, n. 10 (9 gennaio 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 paper designs a set of high-definition bayonet systems for intelligent transportation. It combines data mining technology and distributed parallel Hadoop technology to design the architecture and analysis of intelligent traffic operation state data analysis. The mining algorithm suitable for the system proves the feasibility of the intelligent traffic operation state data analysis system with the actual traffic big data experiment, and aims to provide decision-making opinions for the traffic state. Using the deployed Hadoop server cluster and the AdaBoost algorithm of the improved MapReduce programming model, the example runs large traffic data, performs traffic analysis and speed–overspeed analysis, and extracts information conducive to traffic control. It proves the feasibility and effectiveness of using Hadoop platform to mine massive traffic information.

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Lin, Yueh-lung, e Conghua Wen. "Vehicle Vision Robust Detection and Recognition Method". International Journal of Pattern Recognition and Artificial Intelligence 34, n. 10 (31 dicembre 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 vehicle detection and recognition. Although computer vision recognition technology has made great progress, how to improve the detection accuracy of the image to be detected is still an important content of visual recognition technology research. Intelligent vehicle visual robust detection and identification of methods of research to reduce the growing incidence of traffic accidents, improve the existing road traffic safety and transportation efficiency, alleviate the degree of driver fatigue problem are of great significance. This paper considers the intelligent vehicle environmental awareness of the key technology to the goal of robust detection and recognition based on machine vision problems for further research. The particle filter is used to extract the local energy of the image to realize the fast segmentation of the region of interest (ROI). In order to further verify the ROI, a measure learning method based on multi-core embedding is proposed, and the semantic classification of ROI is realized by integrating the color, shape and geometric features of ROI. Experimental results show that the algorithm can effectively eliminate false sexy ROI interest, and the algorithm is robust to complex background, illumination changes, perspective changes and other conditions.

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Zou, Ying, Dahu Wang e Leian Liu. "Research on Human Movement Target Recognition Algorithm in Complex Traffic Environment". International Journal of Pattern Recognition and Artificial Intelligence 34, n. 05 (29 agosto 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-based moving target recognition technology involves theoretical knowledge in various fields such as artificial intelligence, image processing, pattern recognition and computer vision. It is an important means to realize “safe city” and “smart city” and a key technology for intelligent monitoring. Therefore, the research on human motion target recognition algorithm in complex traffic environment has important theoretical and practical value. In the field of intelligent traffic monitoring, the moving target detection and recognition effect of video images will have certain influence on the classification and behavior understanding of subsequent moving targets. In this paper, the commonly used moving target detection methods are studied first, and the convergence problem of the traditional Adaboost algorithm is improved. An Adaboost algorithm based on adaptive weight update is proposed, and then the support vector machine (SVM) is used. The algorithm identifies the detected moving target. Finally, through simulation experiments on the acquired video images, the results show that the proposed human motion target recognition algorithm based on adaptive weight update Adaboost and SVM has good feasibility and rationality.

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Ruiqi, Luo, Zhong Xian, Zhong Luo e Li Lin. "Research on the intelligent judgment of traffic congestion in intelligent traffic based on pattern recognition technology". Cluster Computing 22, S5 (15 marzo 2018): 12581–88. http://dx.doi.org/10.1007/s10586-017-1684-8.

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Pan, Qingchao, e Haohua Zhang. "Key Algorithms of Video Target Detection and Recognition in Intelligent Transportation Systems". International Journal of Pattern Recognition and Artificial Intelligence 34, n. 09 (16 dicembre 2019): 2055016. http://dx.doi.org/10.1142/s0218001420550162.

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Abstract (sommario):

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 information judgment, pedestrian and vehicle classification, traffic flow parameter detection, and moving target tracking. It promotes intelligent transportation systems to be more intelligent and practical, and provides comprehensive, real-time traffic status information for traffic management and control. Therefore, the research on the method of traffic information detection based on computer vision has important theoretical and practical significance. The detection and recognition of video targets is an important research direction in the field of intelligent transportation and computer vision. However, due to the background complexity, illumination changes, target occlusion and other factors in the detection and recognition environment, the application still faces many difficulties, and the robustness and accuracy of detection and recognition need to be further improved. In this paper, several key problems in video object detection and recognition are studied, including accurate segmentation of target and background, shadow in complex scenes; accurate classification of extracted foreground targets; and target recognition in complex background. In response to these problems, this paper proposes a corresponding solution.

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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, n. 3 (20 marzo 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 algorithms and systems to extract contextual information from video. In traffic circumstancesnumeroussupervise objectives can be continue by the application of computer vision and pattern gratitude techniques, including the recognition of traffic violations (e.g., illegal turns and one-way streets) and the classification of road users (e.g., vehicles, motorbikes, and pedestrians). Currently most reliable approach is through the acknowledgment of number plates, i.e., automatic number plate recognition (ANPR).

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Escalera, Sergio, Oriol Pujol e Petia Radeva. "Traffic sign recognition system with β -correction". Machine Vision and Applications 21, n. 2 (6 giugno 2008): 99–111. http://dx.doi.org/10.1007/s00138-008-0145-z.

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Saeipour, Parisa, Parvin Sarbakhsh, Saman Salemi e Fatemeh Bakhtari Aghdam. "A Fuzzy Clustering Approach to Identify Pedestrians’ Traffic Behavior Patterns". Journal of Research in Health Sciences 23, n. 3 (29 settembre 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 pattern of traffic behaviors by collecting data from 600 pedestrians in Urmia, Iran via "the Pedestrian Behavior Questionnaire" (PBQ) and using 5 domains of PBQ. Multiple logistic regression was fitted to identify risk factors of traffic behaviors. Results: Results revealed two clusters consisting of lower-risk and higher-risk behaviors. The majority of pedestrians (64.33%) were in the lower-risk cluster. Subjects≤33 years old (Odds ratio [OR]=1.92, P<0.001), subjects with≤6 years of education (OR=1.74, P=0.010), males (OR=1.90, P=0.001), unmarried pedestrians (OR=3.61, P=0.007), and users of public transportation (OR=2.01, P=0.002) were more likely to have higher-risk traffic behavior. Conclusion: We identified traffic behavior patterns of Urmia pedestrians with lower-risk and higher-risk behaviors via FCM. The findings from this study would be helpful for policymakers to promote safety measures and train pedestrians.

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Tanuwidjaya, Kevin, Ericson - e Lukman Hakim. "KLASIFIKASI RAMBU LALU LINTAS MENGGUNAKAN DECISION TREE J48 DAN LOCAL BINARY PATTERN". JITTER : Jurnal Ilmiah Teknologi dan Komputer 3, n. 1 (19 gennaio 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 algorithm as a classification system to recognize traffic signs. It is hoped that this research can provide an overview of the combined ability of these two algorithms in classifying types of traffic signs.

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Yang, Sung-Min, e Kang-Hyun Jo. "HOG based Pedestrian Detection and Behavior Pattern Recognition for Traffic Signal Control". Journal of Institute of Control, Robotics and Systems 19, n. 11 (1 novembre 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 e Shaojun Wei. "Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature". Sensors 15, n. 1 (19 gennaio 2015): 2161–80. http://dx.doi.org/10.3390/s150102161.

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Mrgole, Anamarija L., e Drago Sever. "Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction". PROMET - Traffic&Transportation 29, n. 1 (6 febbraio 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 with chaotic features implies that this kind of method has great theoretical and practical value. Researched methods of identifying chaos in traffic flow have shown certain restrictions in their techniques but have suggested guidelines for improving the identification of chaotic parameters in traffic flow. The proposed new method of forecasting congestion in traffic flow uses Wigner-Ville frequency distribution. This method enables the display of a chaotic attractor without the use of reconstruction phase space.

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Adeyemi, Oladimeji, Martins Irhebhude e Adeola Kolawole. "Speed Breakers, Road Marking Detection and Recognition Using Image Processing Techniques". Advances in Image and Video Processing 7, n. 5 (8 novembre 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 features from the images. Local Binary Pattern (LBP) was extracted as features and employed to train the Support Vector Machine (SVM) classifier for speed breaker recognition. Experimental results shows 79%, 100% “STOP” sign and speed breaker recognitions respectively. The proposed system goes very well for the roads which are constructed with proper painting irrespective of their dimension.

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Ge, Jun Wei, Ming Zhao e Yi Qiu Fang. "A Behavior-Based Rapid Method for P2P Traffic Identification". Applied Mechanics and Materials 380-384 (agosto 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 effectively identified by this method.

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Cerreto, Fabrizio, Bo Friis Nielsen, Otto Anker Nielsen e 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-means clustering can identify and discriminate different patterns affecting the same stations, which is otherwise difficult in previous approaches based on visual inspection. Classical methods of univariate analysis do not reveal these patterns. The demonstrated methodology is scalable and can be applied to any system of transport.

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Kuo, Yau-Hwang, Mong-Fong Horng e Jung-Hsien Chiang. "An Adaptive Fuzzy Clustering Technique for Traffic Prediction of Packet-switched Networks". Journal of Advanced Computational Intelligence and Intelligent Informatics 5, n. 3 (20 maggio 2001): 180–88. http://dx.doi.org/10.20965/jaciii.2001.p0180.

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Traffic prediction is significant to QoS design because it assists efficient management of network resources to improve the reliability and performance of the next generation Internet. The unavoidable traffic variation caused by diverse Internet services complicates traffic prediction, particularly in a multi-hop network. To simplify the complicated statistical analysis used in traditional approaches, an adaptive traffic prediction approach featuring robustness, high accuracy and high adaptability is proposed in this paper. The proposed approach bases on a novel fuzzy clustering algorithm to generalize and unveil the hidden structure of traffic patterns. The unveiled structure represents the characteristics of the target traffic. Therefore, it can be referenced to predict traffic in a limited time period by fuzzy matching. To track the variation of target traffic, the proposed approach adopts an incremental and dynamic on-line clustering procedure so that the prediction can maintain high accuracy under traffic variation. To verify the performance of the proposed approach and investigate its properties, the periodical, Poisson and real video traffic patterns have been used to experiment. The experimental results showed an excellent performance of the developed adaptive predictor. The prediction errors, in average, are near 2.2%, 13.6% and 7.62% for periodical, Poisson and real video traffics, respectively.

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Ellahyani, Ayoub, Mohamed El Ansari, Redouan Lahmyed e Alain Trémeau. "Traffic sign recognition method for intelligent vehicles". Journal of the Optical Society of America A 35, n. 11 (26 ottobre 2018): 1907. http://dx.doi.org/10.1364/josaa.35.001907.

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Atkočiūnas, E., R. Blake, A. Juozapavičius e M. Kazimianec. "Image Processing in Road Traffic Analysis". Nonlinear Analysis: Modelling and Control 10, n. 4 (25 ottobre 2005): 315–32. http://dx.doi.org/10.15388/na.2005.10.4.15112.

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The article presents an application of computer vision methods to traffic flow monitoring and road traffic analysis. The application is utilizing image-processing and pattern recognition methods designed and modified to the needs and constrains of road traffic analysis. These methods combined together gives functional capabilities of the system to monitor the road, to initiate automated vehicle tracking, to measure the speed, and to recognize number plates of a car. Software developed was applied in and approved with video monitoring system, based on standard CCTV cameras connected to wide area network computers.

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Ma, SuYuan, e MingYe Zhao. "Traffic Flow Prediction and Analysis in Smart Cities Based on the WND-LSTM Model". Computational Intelligence and Neuroscience 2022 (2 agosto 2022): 1–9. http://dx.doi.org/10.1155/2022/7079045.

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Aiming at the problem that the road traffic flow in intelligent city is unevenly distributed in time and space, difficult to predict, and prone to traffic congestion, combined with pattern recognition and big data mining technology, this paper proposes a research method to analyze and mine the daily travel patterns of urban vehicles. This paper proposes a WND-LSTM model, which mainly includes data preprocessing, data modelling, and model implementation, to analyze the similarity of travel patterns in seasonal changes. Combining the data mining results with the data mining results, the daily travel model of road traffic vehicles in intelligent city is established. The results of the case study showed that the WND-LSTM model outperformed ARIMA (88.48%), LR (65.79%), SVR (70.46%), KNN (68.21%), SAEs (66.95%), GRU (68.43%), and LSTM (70.41%) in MAPE, respectively, with an average accuracy improvement of 71.25% (MAPE of 0.651%).

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Lu, Kaibin. "Network Anomaly Traffic Analysis". Academic Journal of Science and Technology 10, n. 3 (27 aprile 2024): 65–68. http://dx.doi.org/10.54097/8as0rg31.

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This paper rigorously analyzes two principal methodologies in network traffic anomaly detection: feature detection and anomaly detection. Each methodology exhibits distinct strengths and confronts specific challenges. The study elucidates how the integration of deep learning with artificial immune systems could potentially transform feature detection. Moreover, it illustrates the enhancement of anomaly detection through the synthesis of machine learning techniques with traditional methods. Looking ahead, the paper delineates research trajectories that concentrate on merging deep learning, artificial intelligence, and behavioral analysis. This integration aims to augment the precision, efficiency, and adaptability of network anomaly traffic monitoring systems. Proposed future strategies include advanced methods in data preprocessing, model development, pattern recognition, and adaptive adjustments. These strategies are directed towards fortifying network defenses in response to the dynamically changing spectrum of cyber threats.

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Yao, Jingxuan, e Yuntao Ye. "The effect of image recognition traffic prediction method under deep learning and naive Bayes algorithm on freeway traffic safety". Image and Vision Computing 103 (novembre 2020): 103971. http://dx.doi.org/10.1016/j.imavis.2020.103971.

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Zhang, Chuanwei, Xiangyang Yue, Rui Wang, Niuniu Li e Yupeng Ding. "Study on Traffic Sign Recognition by Optimized Lenet-5 Algorithm". International Journal of Pattern Recognition and Artificial Intelligence 34, n. 01 (12 giugno 2019): 2055003. http://dx.doi.org/10.1142/s0218001420550034.

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Traffic sign recognition (TSR) is a key technology of intelligent vehicles, which is based on visual perception for road information. In view of the fact that the traditional computer vision identification technology cannot meet the requirements of real-time accuracy, the TSR algorithm has been proposed on the basis of improved Lenet-5 algorithm. Firstly, we performed picture noise elimination and image enhancement on selected traffic sign images. Secondly, we used Gabor filter kernel in the convolution layer for convolution operation. In the convolution process, we added normalization layer Batch Normality (BN) after each convolution layer and reduced the data dimension. In the down-sampling layer, we replaced Sigmoid with the Relu activator. Finally, we selected the expanded GTSRB traffic sign database for the comparison experiment on the Caff platform. The experimental results showed that the proposed improved Lenet-5 network test set had the recognition accuracy of 96%, which was better than the method that combined Gabor with Support Vector Machine (SVM) in terms of recognition accuracy and real-time performance.

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