Bibliografie tematiche / Traffic pattern recognition

Letteratura scientifica selezionata sul tema "Traffic pattern recognition"

Autore: Grafiati

Pubblicato: 1 giugno 2024

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Articoli di riviste sul tema "Traffic pattern recognition":

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.

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.

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.

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.

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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Abstract (sommario):

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.

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.

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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Abstract (sommario):

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.

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.

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.

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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Tesi sul tema "Traffic pattern recognition":

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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Abstract (sommario):

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
s automotive technology. In the design of smarter vehicles, several research issues can be addressed
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
attention by their colors and shapes, processing of these features is one of the crucial parts in these systems. In this thesis, a Traffic Sign Recognition System, having ability of detection and identification of traffic signs even with bad visual artifacts those originate from some weather conditions or other circumstances, is developed. The developed algorithm in this thesis, segments the required color influenced by the illumination of the environment, then reconstructs the shape of partially occluded traffic sign by its remaining segments and finally, identifies it. These three stages are called as &ldquo
Segmentation&rdquo
, &ldquo
Reconstruction&rdquo
and &ldquo
Identification&rdquo
respectively, within this thesis. Due to the difficulty of analyzing partial segments to construct the main frame (a whole sign), the main complexity of the algorithm takes place in the &ldquo
Reconstruction&rdquo
stage.

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 normalized eigenvector problem in order to construct an optimal representation of the original data. While this step greatly enhances our ability to analyze the relationships between data points and identify the natural clusters within the original dataset, it is difficult to comprehend the eigenvalue representation of the data in terms of the original input variables. The later sections of this paper will explore how the careful framing of questions with respect to available data can help researchers extract tangible decision driving results from real world data through spectral clustering analysis.

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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Abstract (sommario):

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 development of three low complexity and effective techniques for real-time traffic generation, control and measurement. These techniques have proved to be accurate and near optimum. In the three cases the work starts with a literature survey of known methodologies, and later new techniques are proposed and investigated by simulating the processes involved. The work is based on the use of high-speed Asynchronous Transfer Mode (ATM) networks. The problem of developing a fast traffic generation technique for the simulation of Variable Bit Rate traffic sources is considered in the first part of this thesis. For this purpose, statistical measures are obtained from the analysis of different traffic profiles or from the literature. With the aid of these measures, a model for the fast generation of Variable Bit Rate traffic at different time resolutions is developed. The simulated traffic is then analysed in order to obtain the equivalent set of statistical measures and these are compared against those observed in real traffic traces. The subject of traffic control comprises a very wide area in communication networks. It refers to the generalised classification of actions such as Connection Admission and Flow Control, Traffic Policing and Shaping. In the second part of this thesis, a method to modify the instantaneous traffic profile of a variable rate source is developed. It is particularly useful for services which have a hard bound on the cell loss probability, but a soft bound on the admissible delay, matching the characteristics of some of the services provided by ATM networks. Finally, this thesis is also concerned with a particular aspect of the operation and management of high speed networks, or OAM functions plane, namely with the monitoring of network resources. A monitoring technique based on numerical approximation and statistical sampling methods is developed and later used to characterise a particular traffic stream, or a particular connection, within a high speed network. The resulting algorithms are simple and computationally inexpensive, but effective and accurate at the same time, and are suitable for real-time processing.

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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Abstract (sommario):

Thesis (Ph. D.)--University of California, Riverside, 2009.
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.

Chen, Hao. "Real-time Traffic State Prediction: Modeling and Applications". Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/64292.

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Abstract (sommario):

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 footprint of the transportation system. The objective of the research presented in this dissertation is to develop a framework that includes three major categories of methodologies to predict the spatiotemporal evolution of the traffic state. The proposed methodologies include macroscopic traffic modeling, computer vision and recursive probabilistic algorithms. Each developed method attempts to predict traffic state, including roadway travel times, for different prediction horizons. In total, the developed multi-tool framework produces traffic state prediction algorithms ranging from short – (0~5 minutes) to medium-term (1~4 hours) considering departure times up to an hour into the future. The dissertation first develops a particle filter approach for use in short-term traffic state prediction. The flow continuity equation is combined with the Van Aerde fundamental diagram to derive a time series model that can accurately describe the spatiotemporal evolution of traffic state. The developed model is applied within a particle filter approach to provide multi-step traffic state prediction. The testing of the algorithm on a simulated section of I-66 demonstrates that the proposed algorithm can accurately predict the propagation of shockwaves up to five minutes into the future. The developed algorithm is further improved by incorporating on- and off-ramp effects and more realistic boundary conditions. Furthermore, the case study demonstrates that the improved algorithm produces a 50 percent reduction in the prediction error compared to the classic LWR density formulation. Considering the fact that the prediction accuracy deteriorates significantly for longer prediction horizons, historical data are integrated and considered in the measurement update in the developed particle filter approach to extend the prediction horizon up to half an hour into the future. The dissertation then develops a travel time prediction framework using pattern recognition techniques to match historical data with real-time traffic conditions. The Euclidean distance is initially used as the measure of similarity between current and historical traffic patterns. This method is further improved using a dynamic template matching technique developed as part of this research effort. Unlike previous approaches, which use fixed template sizes, the proposed method uses a dynamic template size that is updated each time interval based on the spatiotemporal shape of the congestion upstream of a bottleneck. In addition, the computational cost is reduced using a Fast Fourier Transform instead of a Euclidean distance measure. Subsequently, the historical candidates that are similar to the current conditions are used to predict the experienced travel times. Test results demonstrate that the proposed dynamic template matching method produces significantly better and more stable prediction results for prediction horizons up to 30 minutes into the future for a two hour trip (prediction horizon of two and a half hours) compared to other state-of-the-practice and state-of-the-art methods. Finally, the dissertation develops recursive probabilistic approaches including particle filtering and agent-based modeling methods to predict travel times further into the future. Given the challenges in defining the particle filter time update process, the proposed particle filtering algorithm selects particles from a historical dataset and propagates particles using data trends of past experiences as opposed to using a state-transition model. A partial resampling strategy is then developed to address the degeneracy problem in the particle filtering process. INRIX probe data along I-64 and I-264 from Richmond to Virginia Beach are used to test the proposed algorithm. The results demonstrate that the particle filtering approach produces less than a 10 percent prediction error for trip departures up to one hour into the future for a two hour trip. Furthermore, the dissertation develops an agent-based modeling approach to predict travel times using real-time and historical spatiotemporal traffic data. At the microscopic level, each agent represents an expert in the decision making system, which predicts the travel time for each time interval according to past experiences from a historical dataset. A set of agent interactions are developed to preserve agents that correspond to traffic patterns similar to the real-time measurements and replace invalid agents or agents with negligible weights with new agents. Consequently, the aggregation of each agent's recommendation (predicted travel time with associated weight) provides a macroscopic level of output – predicted travel time distribution. The case study demonstrated that the agent-based model produces less than a 9 percent prediction error for prediction horizons up to one hour into the future.
Ph. D.

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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Abstract (sommario):

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 laser scanner to count PTWs in the traffic. Traffic generally contains random vehicles of unknown nature and behaviour such as speed,vehicle interaction with other users on the road etc. Even though there are several technologies that can measure traffic, for example radars, cameras, magnetometers etc, as the PTWs are small-sized vehicles, they often move in between lanes and at quite a high speed compared to the vehicles moving in the adjacent lanes. This makes them difficult to detect. the proposed solution in this research work is composed of the following parts: a configuration to install the laser scanner on the road is chosen and a data coherence method is introduced so that the system is able to detect the road verges and its own height above the road surface. This is validated by simulator. Then the rawd ata obtained is pre-processed and is transform into the spatial temporal domain. Following this, an extraction algorithm called the Last Line Check (LLC) method is proposed. Once extracted, the objectis classified using one of the two classifiers either the Support Vector Machine (SVM) or the k-Nearest Neighbour (KNN). At the end, the results given by each of the two classifiers are compared and presented in this research work. The proposed solution in this research work is a propototype that is intended to be integrated in a real time system that can be installed on a highway to detect, extract, classify and counts PTWs in real time under all traffic conditions (traffic at normal speeds, dense traffic and even traffic jams).

Più fonti

Libri sul tema "Traffic pattern recognition":

Escalera, Sergio. Traffic-Sign Recognition Systems. London: Sergio Escalera, 2011.

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Escalera, Sergio, Xavier Baró e 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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Traffic Monitoring And Analysis First International Workshop Tma 2009 Aachen Germany May 11 2009 Proceedings. Springer, 2009.

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Capitoli di libri sul tema "Traffic pattern recognition":

Kerner, Boris S. "Spatiotemporal Pattern Recognition, Tracking, and Prediction". In The Physics of Traffic, 563–90. Berlin, Heidelberg: 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 e Víctor M. Brea. "Real-Time Traffic Monitoring with Occlusion Handling". In Pattern Recognition and Image Analysis, 273–84. Cham: 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 e J. Maiti. "Real-Time Detection of Traffic Anomalies Near Roundabouts". In Pattern Recognition and Artificial Intelligence, 253–64. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-04112-9_19.

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Obagbuwa, Ibidun Christiana, e Morapedi Tshepang Duncan. "Design of an Elevator Traffic System Using MATLAB Simulation". In Computational Intelligence in Pattern Recognition, 245–54. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-3089-8_24.

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Cancela, Brais, Marcos Ortega e Manuel G. Penedo. "Path Analysis Using Directional Forces. A Practical Case: Traffic Scenes". In Pattern Recognition and Image Analysis, 366–73. Berlin, Heidelberg: 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 e V. M. Brea. "Video Segmentation for Traffic Monitoring Tasks Based on Pixel-Level Snakes". In Pattern Recognition and Image Analysis, 1074–81. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-44871-6_124.

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Gautam, Harsha, Praneet Saurabh e Ritu Prasad. "Lightweight Secure Routing Over Vehicular Ad Hoc Networks with Traffic Status". In Computational Intelligence in Pattern Recognition, 349–57. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2449-3_30.

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Tang, Wenneng, Yaochen Li, Yifan Li e Bo Dong. "Efficient Point-Based Single Scale 3D Object Detection from Traffic Scenes". In Pattern Recognition and Computer Vision, 155–67. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8432-9_13.

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Zang, Di, Yang Fang, Dehai Wang, Zhihua Wei, Keshuang Tang e Xin Li. "Long Term Traffic Flow Prediction Using Residual Net and Deconvolutional Neural Network". In Pattern Recognition and Computer Vision, 62–74. Cham: 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 e Franz Kummert. "Traffic Sign Classifier Adaption by Semi-supervised Co-training". In Artificial Neural Networks in Pattern Recognition, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33212-8_18.

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Atti di convegni sul tema "Traffic pattern recognition":

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

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Buslaev, Alexander, Marina Yashina, Ruslan Abushov e 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 e 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, e Jin Zhou. "Anomaly detection in low quality traffic monitoring videos using optical flow". In Pattern Recognition and Tracking XXIX, a cura di Mohammad S. Alam. SPIE, 2018. http://dx.doi.org/10.1117/12.2303651.

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Tang, Suisui, e 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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Kejing Zhang e Laurie Cuthbert. "Performing traffic pattern prediction in WCDMA networks". In 2008 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2008. http://dx.doi.org/10.1109/icwapr.2008.4635892.

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Fu, Meng-Yin, e Yuan-Shui Huang. "A survey of traffic sign recognition". In 2010 International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR). IEEE, 2010. http://dx.doi.org/10.1109/icwapr.2010.5576425.

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"TRAFFIC LIGHT RECOGNITION USING CIRCULAR SEPARABILITY FILTER". In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003741402770283.

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Qin, Fei, Bin Fang e Hengjun Zhao. "Traffic Sign Segmentation and Recognition in Scene Images". In 2010 Chinese Conference on Pattern Recognition (CCPR). IEEE, 2010. http://dx.doi.org/10.1109/ccpr.2010.5659271.

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Sengar, Vartika, Renu Rameshan e Senthil Ponkumar. "Hierarchical Traffic Sign Recognition for Autonomous Driving". In 9th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0008924703080320.

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