Relevant bibliographies by topics / Traffic pattern recognition

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Traffic pattern recognition'

Author: Grafiati
Published: 1 June 2024
Last updated: 31 July 2025

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Journal articles on the topic "Traffic pattern recognition"

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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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Dissertations / Theses on the topic "Traffic pattern recognition"

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Aydin, Ufuk Suat. "Traffic Sign Recognition." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/12610590/index.pdf.

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Designing smarter vehicles, aiming to minimize the number of driverbased wrong decisions or accidents, which can be faced with during the drive, is one of hot topics of today&rsquo<br>s automotive technology. In the design of smarter vehicles, several research issues can be addressed<br>one of which is Traffic Sign Recognition (TSR). In TSR systems, the aim is to remind or warn drivers about the restrictions, dangers or other information imparted by traffic signs, beforehand. Since the existing signs are designed to draw drivers&rsquo<br>attention by their colors and shapes, processing of thes
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Aven, Matthew. "Daily Traffic Flow Pattern Recognition by Spectral Clustering." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1597.

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This paper explores the potential applications of existing spectral clustering algorithms to real life problems through experiments on existing road traffic data. The analysis begins with an overview of previous unsupervised machine learning techniques and constructs an effective spectral clustering algorithm that demonstrates the analytical power of the method. The paper focuses on the spectral embedding method’s ability to project non-linearly separable, high dimensional data into a more manageable space that allows for accurate clustering. The key step in this method involves solving a norm
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Ali, Abdulamer T. "Computer vision aided road traffic analysis." Thesis, University of Bristol, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.333953.

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Houghton, A. D. "The application of RAPAC to traffic monitoring." Thesis, University of Sheffield, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306208.

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Fields, Matthew James. "Facilitation of visual pattern recognition by extraction of relevant features from microscopic traffic data." [College Station, Tex. : Texas A&M University, 2007. http://hdl.handle.net/1969.1/ETD-TAMU-2036.

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Viens, Francois (Joseph Lucien Francois) Carleton University Dissertation Engineering Electrical. "A neural network approach to detect traffic anomalies in a communication network." Ottawa, 1992.

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Villegas, Ruben M. M. "Statistical processing for telecommunication networks applied to ATM traffic monitoring." Thesis, Loughborough University, 1997. https://dspace.lboro.ac.uk/2134/6760.

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Within the fields of network operation and performance measurement, it is a common requirement that the technologies involved must provide the basis for an effective, reliable, measurable and controllable service. In order to comply with the service performance criteria, the constrains often lead to very complex techniques and methodologies for the simulation, control, test, and measurement processes. This thesis addresses some of the factors that contribute to the overall spectrum of statistical performance measurements in telecommunication services. Specifically, it is concerned with the dev
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Cao, Meng. "Mobile and stationary computer vision based traffic surveillance techniques for advanced ITS applications." Diss., [Riverside, Calif.] : University of California, Riverside, 2009. http://gateway.proquest.com/openurl?url_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:dissertation&res_dat=xri:pqdiss&rft_dat=xri:pqdiss:3350077.

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Thesis (Ph. D.)--University of California, Riverside, 2009.<br>Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Includes bibliographical references. Issued in print and online. Available via ProQuest Digital Dissertations.
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Chen, Hao. "Real-time Traffic State Prediction: Modeling and Applications." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64292.

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Travel-time information is essential in Advanced Traveler Information Systems (ATISs) and Advanced Traffic Management Systems (ATMSs). A key component of these systems is the prediction of the spatiotemporal evolution of roadway traffic state and travel time. From the perspective of travelers, such information can result in better traveler route choice and departure time decisions. From the transportation agency perspective, such data provide enhanced information with which to better manage and control the transportation system to reduce congestion, enhance safety, and reduce the carbon footpr
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Prabhakar, Yadu. "Detection and counting of Powered Two Wheelers in traffic using a single-plane Laser Scanner." Phd thesis, INSA de Rouen, 2013. http://tel.archives-ouvertes.fr/tel-00973472.

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The safety of Powered Two Wheelers (PTWs) is important for public authorities and roadadministrators around the world. Recent official figures show that PTWs are estimated to represent only 2% of the total traffic but represent 30% of total deaths on French roads. However, as these estimated figures are obtained by simply counting the number plates registered, they do not give a true picture of the PTWs on the road at any given moment. This dissertation comes under the project METRAMOTO and is a technical applied research work and deals with two problems: detection of PTWsand the use of a lase
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Books on the topic "Traffic pattern recognition"

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Xavier, Baró, Pujol Oriol, Vitrià Jordi, Radeva Petia, and SpringerLink (Online service), eds. Traffic-Sign Recognition Systems. Sergio Escalera, 2011.

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Escalera, Sergio, Xavier Baró, and Oriol Pujol. Traffic-Sign Recognition Systems. Springer, 2011.

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Traffic Monitoring And Analysis 4th International Workshop Tma 2012 Vienna Austria March 12 2012 Proceedings. Springer, 2012.

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Traffic Monitoring and Analysis Lecture Notes in Computer Science Computer Communication N. Springer, 2011.

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Book chapters on the topic "Traffic pattern recognition"

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Kerner, Boris S. "Spatiotemporal Pattern Recognition, Tracking, and Prediction." In The Physics of Traffic. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-40986-1_22.

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Fernández-Sanjurjo, Mauro, Manuel Mucientes, and Víctor M. Brea. "Real-Time Traffic Monitoring with Occlusion Handling." In Pattern Recognition and Image Analysis. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-31321-0_24.

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Pramanik, Anima, Sobhan Sarkar, Chawki Djeddi, and J. Maiti. "Real-Time Detection of Traffic Anomalies Near Roundabouts." In Pattern Recognition and Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04112-9_19.

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Obagbuwa, Ibidun Christiana, and Morapedi Tshepang Duncan. "Design of an Elevator Traffic System Using MATLAB Simulation." In Computational Intelligence in Pattern Recognition. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3089-8_24.

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Cancela, Brais, Marcos Ortega, and Manuel G. Penedo. "Path Analysis Using Directional Forces. A Practical Case: Traffic Scenes." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_43.

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Vilariño, D. L., D. Cabello, X. M. Pardo, and V. M. Brea. "Video Segmentation for Traffic Monitoring Tasks Based on Pixel-Level Snakes." In Pattern Recognition and Image Analysis. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_124.

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Tang, Wenneng, Yaochen Li, Yifan Li, and Bo Dong. "Efficient Point-Based Single Scale 3D Object Detection from Traffic Scenes." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8432-9_13.

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Gautam, Harsha, Praneet Saurabh, and Ritu Prasad. "Lightweight Secure Routing Over Vehicular Ad Hoc Networks with Traffic Status." In Computational Intelligence in Pattern Recognition. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2449-3_30.

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Zang, Di, Yang Fang, Dehai Wang, Zhihua Wei, Keshuang Tang, and Xin Li. "Long Term Traffic Flow Prediction Using Residual Net and Deconvolutional Neural Network." In Pattern Recognition and Computer Vision. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-03335-4_6.

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Hillebrand, Matthias, Ulrich Kreßel, Christian Wöhler, and Franz Kummert. "Traffic Sign Classifier Adaption by Semi-supervised Co-training." In Artificial Neural Networks in Pattern Recognition. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33212-8_18.

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Conference papers on the topic "Traffic pattern recognition"

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Shukla, Minakshee, Renu Rameshan, and Shikha Gupta. "Anomalous Event Detection in Traffic Audio." In 14th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2025. https://doi.org/10.5220/0013152100003905.

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Wang, Chuanfei. "Research on traffic sign recognition algorithms for autonomous vehicles in smart city traffic systems." In Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), edited by Ji Zhao and Yonghui Yang. SPIE, 2024. http://dx.doi.org/10.1117/12.3037841.

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bao, lei. "Traffic light recognition and detection of unmanned vehicle based on improved YOLOv5." In International Conference on Pattern Recognition and Image Analysis, edited by Mingguang Shan and Tao Lei. SPIE, 2025. https://doi.org/10.1117/12.3056112.

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Ghouse, Mohammed, Saber Farag, and Usman Butt. "Traffic sign recognition based on CNN vs different transfer learning techniques." In 2024 The 5th Symposium on Pattern Recognition and Applications, edited by Xiaodan Pang. SPIE, 2025. https://doi.org/10.1117/12.3057582.

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Tran, Quang Minh, Thanh Duc Ngo, and Tien-Dung Mai. "Contrastive Learning with Weakly Pair Images for Traffic Image Deraining." In 2024 International Conference on Multimedia Analysis and Pattern Recognition (MAPR). IEEE, 2024. http://dx.doi.org/10.1109/mapr63514.2024.10660776.

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Wang, Sijuan, and Zhiqiang You. "Scale-variant traffic sign detection." In Fourth International Workshop on Pattern Recognition, edited by Zhenxiang Chen, Xudong Jiang, and Guojian Chen. SPIE, 2019. http://dx.doi.org/10.1117/12.2540462.

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Buslaev, Alexander, Marina Yashina, Ruslan Abushov, and Igor Kotovich. "Mathematical Problems of Pattern Recognition for Traffic." In 2010 Seventh International Conference on Information Technology: New Generations. IEEE, 2010. http://dx.doi.org/10.1109/itng.2010.245.

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Wang, Yuan-Kai, Ching-Tang Fan, and Jian-Fu Chen. "Traffic Camera Anomaly Detection." In 2014 22nd International Conference on Pattern Recognition (ICPR). IEEE, 2014. http://dx.doi.org/10.1109/icpr.2014.794.

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Kwan, Chiman, and Jin Zhou. "Anomaly detection in low quality traffic monitoring videos using optical flow." In Pattern Recognition and Tracking XXIX, edited by Mohammad S. Alam. SPIE, 2018. http://dx.doi.org/10.1117/12.2303651.

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Tang, Suisui, and Lin-Lin Huang. "Traffic Sign Recognition Using Complementary Features." In 2013 2nd IAPR Asian Conference on Pattern Recognition (ACPR). IEEE, 2013. http://dx.doi.org/10.1109/acpr.2013.63.

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