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O’Sullivan, Rory. "Traffic patterns." Canadian Family Physician 69, no. 1 (2023): 47–48. http://dx.doi.org/10.46747/cfp.690147.
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Patil, Vrushal. "Traffic Signal Pattern Algorithm." International Journal for Research in Applied Science and Engineering Technology 11, no. 12 (2023): 126–28. http://dx.doi.org/10.22214/ijraset.2023.57249.
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Abstract: Every day we are witnessing a rapid increase in traffic volume on roads. Traffic signals are made to manage the traffic to get less disturbance during the journey and to avoid collisions. Sometimes these traffic signals might become a reason for a delay due to poor time management at signal timings. The old traffic signal patterns are the main cause of this issue and hence this project of new signalling patterns will help in using traffic signals more efficiently. In the traditional pattern at a crossover only one signal can be opened but using our pattern algorithm more than one signal can be opened and traffic could clear more easily. Even concepts of image processing are used to make the system more automated and intelligent.
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B. Gaididei, Yuri, Carlos Gorria, Rainer Berkemer, et al. "Stochastic control of traffic patterns." Networks & Heterogeneous Media 8, no. 1 (2013): 261–73. http://dx.doi.org/10.3934/nhm.2013.8.261.
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Puangnak, Korn, and Natworapol Rachsiriwatcharabul. "Collection of Road Traffic Incidents in Bangkok from Twitter Data based on Deep Learning Algorithm." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 16, no. 3 (2022): 267–76. http://dx.doi.org/10.37936/ecti-cit.2022163.248535.
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Text processing technology from Twitter to report notification formats that are known in many countries with verification on different languages. This research presents the development of a neural network memory learning model. To solve the problem of classifying incidence patterns and identifying severity of incidents from Thai social media messages. For gathering incident data and reporting incidents externally from a single reporting platform by using deep learning models like MLP, CNN and LSTM which is designed by dividing the study into 3 types, including examination traffic incidence identification pattern that can identify the report as general news or traffic reporting Incident Identification Patterns. These include traffic conditions, accidents, disasters, damaged roads, or other than the aforementioned patterns, and the pattern indicating the severity of the incidence consists of normal level, medium level and lane blocking or stationary levels. The results demonstrated the ability of LSTM learning with the best results in incidence detection and incidence pattern identification at 93.44% and 87.40%, respectively, and the CNN method was able to State the severity of the incidence at best, reaching 91.42%.
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Wang, Longfei, Hong Chen, and Yang Li. "Transition Characteristic Analysis of Traffic Evolution Process for Urban Traffic Network." Scientific World Journal 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/603274.
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The characterization of the dynamics of traffic states remains fundamental to seeking for the solutions of diverse traffic problems. To gain more insights into traffic dynamics in the temporal domain, this paper explored temporal characteristics and distinct regularity in the traffic evolution process of urban traffic network. We defined traffic state pattern through clustering multidimensional traffic time series using self-organizing maps and construct a pattern transition network model that is appropriate for representing and analyzing the evolution progress. The methodology is illustrated by an application to data flow rate of multiple road sections from Network of Shenzhen’s Nanshan District, China. Analysis and numerical results demonstrated that the methodology permits extracting many useful traffic transition characteristics including stability, preference, activity, and attractiveness. In addition, more information about the relationships between these characteristics was extracted, which should be helpful in understanding the complex behavior of the temporal evolution features of traffic patterns.
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Hernández-Vega, Henry, and Carolina Matamoros-Jiménez. "Clustering Approach to Generate Pedestrian Traffic Pattern Groups." Ciencia e Ingeniería Neogranadina 31, no. 2 (2021): 41–60. http://dx.doi.org/10.18359/rcin.4403.
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 This study shows the development of patterns of temporal hourly volume distributions in an urban area in Costa Rica, based on a cluster analysis of pedestrian data. This study aims to establish specific pattern groups for the temporal variation of weekday pedestrian volumes applying cluster analysis in the central business district of Guadalupe in San José. For 46 counting sites, vectors with the weekday hourly factors, the proportion of the daily pedestrian traffic, were estimated. A hierarchical cluster method was implemented to group the vectors of hourly factors from the different counting sites. This method groups elements by minimizing the Euclidean distance between elements of the same group and, at the same time, maximizing the distances from elements of other groups. In addition, the groups found through this analysis are related to land use through buffers of different radios. Eight temporal pattern groups were obtained through cluster analysis. Two pattern groups account for more than two-thirds of the sites included in the study. Fisher’s exact independence test shows that banks and public services could explain some of the patterns observed. The classification of 46 counting sites based on temporal distribution patterns, and the relation with the establishments in the area, allows a simplification of the information and facilitates an understanding of the pedestrian mobility in the area. Further research is required that leads towards geographical elements that could explain the differences in temporal and mobility patterns.
 
 
 
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Jing, Binbin, and Jianmin Xu. "A General Maximum Progression Model to Concurrently Synchronize Left-Turn and through Traffic Flows on an Arterial." Mathematical Problems in Engineering 2018 (2018): 1–11. http://dx.doi.org/10.1155/2018/2453246.
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In the existing bandwidth-based methods, through traffic flows are considered as the coordination objects and offered progression bands accordingly. However, at certain times or nodes in the road network, when the left-turn traffic flows have a higher priority than the through traffic flows, it would be inappropriate to still provide the progression bands to the through traffic flows; the left-turn traffic flows should instead be considered as the coordination objects to potentially achieve better control. Considering this, a general maximum progression model to concurrently synchronize left-turn and through traffic flows is established by using a time-space diagram. The general model can deal with all the patterns of the left-turn phases by introducing two new binary variables into the constraints; that is, these variables allow all the patterns of the left-turn phases to deal with a single formulation. By using the measures of effectiveness (average delay time, average vehicle stops, and average travel time) acquired by a traffic simulation software, VISSIM, the validity of the general model is verified. The results show that, compared with the MULTIBAND, the proposed general model can effectively reduce the delay time, vehicle stops, and travel time and, thus, achieve better traffic control.
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Dobler, Gregory, Jordan Vani, and Trang Tran Linh Dam. "Patterns of urban foot traffic dynamics." Computers, Environment and Urban Systems 89 (September 2021): 101674. http://dx.doi.org/10.1016/j.compenvurbsys.2021.101674.
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Lo Verde, John, Wayland Dong, Samantha Rawlings, and McCall Edwards. "Statistical evaluation of noise due to changes in pandemic traffic patterns." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A22. http://dx.doi.org/10.1121/10.0018013.
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Vehicular traffic noise for free-flowing roadways and highways generally follows statistically predictable flow rates and vehicular mix. The authors’ earlier work in this area examined long-term variations in traffic noise level with the purpose of establishing maximum hourly levels for vehicular sources (“Defining vehicular noise levels to manage risk associated with exterior façade design,” LoVerde, Dong, Rawlings, Internoise 2014 Melbourne) and general average sound level (“Noise prediction of vehicle sources on arterials using measured sound data,” LoVerde, Dong, Rawlings, ASA 2014 Providence; “Methods for estimating the variance in traffic noise distribution from short-duration measurements,” LoVerde, Dong, Rawlings, ICSV 2015 Florence). When the COVID-19 pandemic resulted in lockdowns, traffic patterns at the measurement location in Southern California were affected. The authors’ examination of traffic data revealed that the changes in traffic patterns did not affect noise level significantly, but variability in sound level across the 24-h period increased substantially (“Changes in statistical traffic noise descriptors during COVID-19,” LoVerde, Dong, Edwards, Rawlings, ASA 2021). For this paper, the authors have undertaken comparison of pre-, during-, and post-lockdown vehicular traffic noise levels over a several-month period for the purpose of understanding how measurement of traffic noise is implemented and interpreted.
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Baade, S. C. "SNA route generation using traffic patterns." IBM Systems Journal 30, no. 3 (1991): 250–58. http://dx.doi.org/10.1147/sj.303.0250.
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Deng, Lei, Chih-Chun Wang, Minghua Chen, and Shizhen Zhao. "Timely Wireless Flows With General Traffic Patterns: Capacity Region and Scheduling Algorithms." IEEE/ACM Transactions on Networking 25, no. 6 (2017): 3473–86. http://dx.doi.org/10.1109/tnet.2017.2749513.
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Kerner, Boris S. "Empirical Features of Congested Patterns at Highway Bottlenecks." Transportation Research Record: Journal of the Transportation Research Board 1802, no. 1 (2002): 145–54. http://dx.doi.org/10.3141/1802-17.
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An empirical study was undertaken of congested patterns at highway bottlenecks. On the basis of statistical data it was found that the spatialtemporal structure of congested patterns possesses some predictable features. From these features a classification of congested patterns was made. It was found that the most frequently observed congested pattern is the general pattern (GP). In GP synchronized flow occurs upstream of a bottleneck and wide moving jams spontaneously emerge in that synchronized flow. Capacity in free flow can be about twice as high as capacity in congested traffic upstream of the on-ramp if the GP has formed.
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Ren, Guoqiang, Guang Cheng, and Nan Fu. "Accurate Encrypted Malicious Traffic Identification via Traffic Interaction Pattern Using Graph Convolutional Network." Applied Sciences 13, no. 3 (2023): 1483. http://dx.doi.org/10.3390/app13031483.
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Telecommuting and telelearning have gradually become mainstream lifestyles in the post-epidemic era. The extensive interconnection of massive terminals gives attackers more opportunities, which brings more significant challenges to network traffic security analysis. The existing attacks, often using encryption technology and distributed attack methods, increase the number and complexity of attacks. However, the traditional methods need more analysis of encrypted malicious traffic interaction patterns and cannot explore the potential correlations of interaction patterns in a macroscopic and comprehensive manner. Anyway, the changes in interaction patterns caused by attacks also need further study. Therefore, to achieve accurate and effective identification of attacks, it is essential to comprehensively describe the interaction patterns of malicious traffic and portray the relations of interaction patterns with the appearance of attacks. We propose a method for classifying attacks based on the traffic interaction attribute graph, named G-TIAG. At first, the G-TIAG studies interaction patterns of traffic describes the construction rule of the graphs and selects the attributive features of nodes in each graph. Then, it uses a convolutional graph network with a GRU and self-attention to classify benign data and different attacks. Our approach achieved the best classification results, with 89% accuracy and F1-Score, 88% recall, respectively, on publicly available datasets. The improvement is about 7% compared to traditional machine learning classification results and about 6% compared to deep learning classification results, which finally successfully achieved the classification of attacks.
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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 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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V, Prajwal, Abhay R, Rohith Gowda D, and Savitha G. "AI-Driven Urban Traffic Optimization to Assess Complex Traffic Patterns for Public Traffic Control and Mobility." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 794–98. http://dx.doi.org/10.22214/ijraset.2023.56630.
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Abstract: The project "AI for Urban Traffic Optimization" is a major and novel method for addressing the issues of modern urban transportation networks. This research study highlights the potential of artificial intelligence (AI) in transforming traffic management by merging AI with the Internet of Things (IoT). It has several advantages, such as efficient traffic flow optimization, predictive congestion analysis, adaptive traffic signal management, speedy accident detection and reaction, public transit optimization, and enhanced traffic enforcement. These developments have the potential to reduce traffic congestion, fuel consumption, and pollution, eventually fostering a cleaner and more sustainable urban environment.The study acknowledges the difficulties, such as privacy issues and potential biases, but underlines the importance of rigorous preparation, openness, and public input in order to achieve responsible AI adoption. AI traffic management has a bright future as it continues to improve and transform modern cities, providing efficient, safe, and environmentally friendly transportation solutions. Collaboration among researchers, politicians, and industry stakeholders is critical to advancing this breakthrough technology and creating more livable urban settings
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Maio, Sara, Sandra Baldacci, Marzia Simoni, et al. "Longitudinal Asthma Patterns in Italian Adult General Population Samples: Host and Environmental Risk Factors." Journal of Clinical Medicine 9, no. 11 (2020): 3632. http://dx.doi.org/10.3390/jcm9113632.
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Background: Asthma patterns are not well established in epidemiological studies. Aim: To assess asthma patterns and risk factors in an adult general population sample. Methods: In total, 452 individuals reporting asthma symptoms/diagnosis in previous surveys participated in the AGAVE survey (2011–2014). Latent transition analysis (LTA) was performed to detect baseline and 12-month follow-up asthma phenotypes and longitudinal patterns. Risk factors associated with longitudinal patterns were assessed through multinomial logistic regression. Results: LTA detected four longitudinal patterns: persistent asthma diagnosis with symptoms, 27.2%; persistent asthma diagnosis without symptoms, 4.6%; persistent asthma symptoms without diagnosis, 44.0%; and ex -asthma, 24.1%. The longitudinal patterns were differently associated with asthma comorbidities. Persistent asthma diagnosis with symptoms showed associations with passive smoke (OR 2.64, 95% CI 1.10–6.33) and traffic exposure (OR 1.86, 95% CI 1.02–3.38), while persistent asthma symptoms (without diagnosis) with passive smoke (OR 3.28, 95% CI 1.41–7.66) and active smoke (OR 6.24, 95% CI 2.68–14.51). Conclusions: LTA identified three cross-sectional phenotypes and their four longitudinal patterns in a real-life setting. The results highlight the necessity of a careful monitoring of exposure to active/passive smoke and vehicular traffic, possible determinants of occurrence of asthma symptoms (with or without diagnosis). Such information could help affected patients and physicians in prevention and management strategies.
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HUANG, DING-WEI. "COMPLETE TRAFFIC PATTERNS AROUND A T-SHAPED INTERSECTION." International Journal of Modern Physics C 21, no. 02 (2010): 189–204. http://dx.doi.org/10.1142/s0129183110015063.
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We propose Asymmetric Simple Exclusion Processes to analyze the traffic states around a T-shaped intersection. The system consists of six roadways connected by the intersection. There are nine control-parameters separated into three categories: injection αi, removal βi, and turning Pi, (where i = 1, 2, 3). As these nine parameters change, traffic states on each roadway reveal a two-phase transition: free flow (F) and jam (J). Together, there can be 64 (=26) possible combinations for the traffic phases. We observe 63 distinct phases. We analyze three major causes of congestion: (1) increase of traffic demand simulated by injection αi; (2) decrease of roadway capacity simulated by removal βi; (3) redistribution of traffic pattern simulated by turning Pi. In case (1), congestion can be confined to the roadways heading toward the intersection. In case (2), spillovers can be observed and congestion will pervade the whole system. In case (3), congestion can be triggered by both increasing Pi and decreasing Pi. The phase diagram can be a convenient tool to summarize the results of numerical simulations. We also compare the unsignalized intersection to an intersection regulated by traffic signals. We find that the operation of traffic signals is very inefficient in resolving the congestion around a T-shaped intersection.
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Kohan, Mariano, and Juan M. Ale. "Discovering traffic congestion through traffic flow patterns generated by moving object trajectories." Computers, Environment and Urban Systems 80 (March 2020): 101426. http://dx.doi.org/10.1016/j.compenvurbsys.2019.101426.
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Boyarshinov, M. G., and A. S. Vavilin. "PATTERNS OF TRAFFIC CONGESTION INDICATOR AT SOME INTERSECTIONS OF THE ROAD NETWORK." Intellect. Innovations. Investments, no. 1 (2024): 95–115. http://dx.doi.org/10.25198/2077-7175-2024-1-95.
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The average speed and density of road transport are used as indicators of the congestion situation, but do not allow tracking the evolution (stages of formatting, progressing, and vanishing) of traffic congestion. The authors proposed and justified a quantitative indicator of traffic congestion, which allows in an automated mode to identify the congestion situation on the urban road network using hardware and software video recording systems. The purpose of this study is due to the need to study the quantitative characteristics of the proposed indicator at characteristic intersections of urban roads, which will allow us to develop scientifically based recommendations for predicting congestion situations, substantiating, and making optimal decisions on measures to promptly eliminate traffic congestion. The object of study is the traffic flow at three types of intersections of the Perm city road network, equipped with a photo and video recording software and hardware complex. The subject of the study is the regularities of the evolution of the listed deterministic indicators of traffic flows, which can be used for operational forecasting of the formation, development, and elimination of traffic congestion. The theoretical and methodological approach is based on the methods of mathematical statistics used to process the results of observations of traffic flows at different types of intersections using a «sliding window», calculating the average daily value and standard deviation. The initial data were obtained with the help of hardware and software complexes for fixing violations of traffic rules installed on the street and road network of the Perm city. As a result of the study, the rational parameters of the «sliding window» were determined, ensuring the structuring of the traffic congestion indicator; the facts of the congestion situations formation were revealed; the features of the congestion evolution and the presence of problematic traffic directions for which it is advisable to change the traffic light regulation mode were determined. The theoretical and practical significance of the work consists in checking the operability of the proposed indicator and criterion of traffic congestion, which is of practical interest from the point of view of predicting anomalies in the movement of vehicles on the road network, adjusting the operating modes of traffic lights, etc. It is also possible to use the proposed traffic congestion indicator to assess the effectiveness of traffic light regulation on the Perm city road network. The direction of further research is to study the patterns of traffic congestion at intersections of the urban road network, of various types that are not included in this study.
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Ma, SuYuan, and MingYe Zhao. "Traffic Flow Prediction and Analysis in Smart Cities Based on the WND-LSTM Model." Computational Intelligence and Neuroscience 2022 (August 2, 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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Shahsavari Pour, N., H. Asadi, and M. Pour Kheradmand. "Fuzzy Multiobjective Traffic Light Signal Optimization." Journal of Applied Mathematics 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/249726.
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Traffic congestion is a major concern for many cities throughout the world. In a general traffic light controller, the traffic lights change at a constant cycle time. Hence it does not provide an optimal solution. Many traffic light controllers in current use are based on the “time-of-the-day” scheme, which use a limited number of predetermined traffic light patterns and implement these patterns depending upon the time of the day. These automated systems do not provide an optimal control for fluctuating traffic volumes. In this paper, the fuzzy traffic light controller is used to optimize the control of fluctuating traffic volumes such as oversaturated or unusual load conditions. The problem is solved by genetic algorithm, and a new defuzzification method is introduced. The performance of the new defuzzification method (NDM) is compared with the centroid point defuzzification method (CPDM) by using ANOVA. Finally, an illustrative example is presented to show the competency of proposed algorithm.
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Tanveer, Hashir, Timo Balz, Francesca Cigna, and Deodato Tapete. "Monitoring 2011–2020 Traffic Patterns in Wuhan (China) with COSMO-SkyMed SAR, Amidst the 7th CISM Military World Games and COVID-19 Outbreak." Remote Sensing 12, no. 10 (2020): 1636. http://dx.doi.org/10.3390/rs12101636.
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Vehicle detection from satellite imagery can support different applications, such as security and situational awareness. In the civilian domain, it can provide quantitative evidence to investigate urban mobility and traffic patterns in cities. Satellite synthetic aperture radar (SAR) can help in detecting vehicles in (almost) all weather conditions and during the day and night. In this study, the capability of SAR StripMap imaging mode data to monitor traffic is analyzed using the case study of Wuhan, China. In ordinary times, the bridges crossing the Yangtze river are the key infrastructure allowing for urban mobility in Wuhan. More recently, the city has been the first in the world to be put in lockdown due to the outbreak of the Coronavirus Disease of 2019 (COVID-19). Using a very long time series of 294 COSMO-SkyMed StripMap HIMAGE mode scenes collected from 2011 to 2020, we detected vehicles on seven bridges, estimated their speed, and analyzed the traffic pattern over time. Vehicles are detected based on their azimuth shift caused by their across-track motion. Our goal is to monitor the variations in traffic instead of single-car detection. The results from 2011 to 2019 show a general increase in the number of vehicles crossing the bridges, as new infrastructure was built over the years. Variations in detected vehicle numbers were especially found during the two events of the 7th International Military Sports Council (CISM) Military World Games in October 2019, and the COVID-19 lockdown in early 2020. These events were therefore used for internal validation of our assessment of traffic patterns. On the other side, TomTom traffic index data were used for external validation. The results and their comparison with TomTom data prove the effectiveness of our method in detecting traffic patterns, but also demonstrate that mostly large vehicles (e.g., trucks or buses) are detected. Future work should be carried out to improve the detection rate of smaller vehicles.
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TIAN, JUN-FANG, ZHEN-ZHOU YUAN, BIN JIA, and HONG-QIANG FAN. "PHASE TRANSITIONS AND THE KORTEWEG-DE VRIES EQUATION IN THE DENSITY DIFFERENCE LATTICE HYDRODYNAMIC MODEL OF TRAFFIC FLOW." International Journal of Modern Physics C 24, no. 03 (2013): 1350016. http://dx.doi.org/10.1142/s0129183113500162.
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We investigate the phase transitions and the Korteweg-de Vries (KdV) equation in the density difference lattice hydrodynamic (DDLM) model, which shows a close connection with the gas-kinetic-based model and the microscopic car following model. The KdV equation near the neutral stability line is derived and the corresponding soliton solution describing the density waves is obtained. Numerical simulations are conducted in two aspects. On the one hand, under periodic conditions perturbations are applied to demonstrate the nonlinear analysis result. On the other hand, the open boundary condition with random fluctuations is designed to explore the empirical congested traffic patterns. The phase transitions among the free traffic (FT), widening synchronized flow pattern (WSP), moving localized cluster (MLC), oscillatory congested traffic (OCT) and homogeneous congested traffic (HCT) occur by varying the amplitude of the fluctuations. To our knowledge, it is the first research showing that the lattice hydrodynamic model could reproduce so many congested traffic patterns.
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Afonin, Maksym, and Mykola Postranskyy. "Patterns of changes in the acoustic characteristics on public transport linear segments." Transport technologies 2022, no. 2 (2022): 41–51. http://dx.doi.org/10.23939/tt2022.02.041.
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The problem of noise pollution in cities becomes quite acute as soon as it comes to increasing the level of motorization. However, most researchers study the negative impact of traffic noise in general. In the era of sustainable mobility, there will be a trend to reduce the number of private vehicles on city streets. Still, the problem of acoustic load in residential areas will not be solved since public transport is a rather powerful source of traffic noise. The article solves the problem of determining the patterns of changes in the acoustic load from public transport vehicles at different speed modes and road surface. The article's objects of research are straight sections of public transport lines. The subject of the study is the patterns of changes in the noise level from public transport vehicles at different speeds, their position, and the type of surface. The obtained results indicate that the main range of noise pollution from public transport on straight sections is 75-85 dBA, and this level can vary by 15-20% depending on the type of line (trolley bus, bus, tram) and the type of road surface. The regularities of changes in the level of noise pollution, which were revealed in work, indicate that for each type of surface and type of public transport line, there are such values of traffic speeds, when they are reached, there is an overtime acoustic load on residential areas at specific distances from them. The obtained results differ from the currently existing scientific studies in that they consider the acoustic characteristics of clear public transport lines and not the traffic flow as a whole. Therefore, it becomes possible to determine the maximum and not the equivalent level of noise from public transport. The field of application of the results is transport planning of both new residential areas and areas of existing adjacent buildings. Thus, in the first case, recommendations were made regarding territorial gaps from the construction line to arterial streets with high volume of public transport, depending on its type and surface. On the other hand, recommendations have been established regarding the speed regime of public transport at different distances from existing buildings' lines.
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Raju, Gangavarapu Deva, and Mary Sowjanya Gaddala. "Injury Patterns and Factors Responsible in Fatal Motorcyclist’s Road Traffic Accidents: A Forensic Perspective." Indian Journal of Forensic Medicine and Pathology 14, no. 4 (2021): 793–98. http://dx.doi.org/10.21088/ijfmp.0974.3383.14421.2.
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Road traffic accidents are the major cause of premature death and disability all over the world and motorized two-wheelers accidents account for the majority of such cases particularly in developing countries like India. The aim of the present study is to analyze the pattern of injuries with a focus on head injuries and the environmental factors leading to events. Methodology:A cross-sectional analytic study was conducted in the Department of Forensic Medicine at Osmania General Hospital, Afzalgunj Hyderabad during the period January 1st, 2018 to December 31st, 2018. Results: Abigmajorityofvictimsconstituteaworkingandeconomicallyproductiveage group of 20-40yrs with male predominance (72.46%). Most accidents (22.6%) occurred during 6-9 pm. Hit by other vehicles (44%) followed by self-skid (32%), and hitting the barriers, or stoppers, sudden interruption by animals and pedestrians, the influence of alcoholallconstitutetheremaining.Aboutnearly87%ofinjuriesaremultipleandhead injuries. Skull fractures were seen in the majority with Sub dural Haemorrhage (47.1%) and Sub arachnoid Haemorrhage (43.6%) which lead to death. Conclusions: The involvement of economically productive males was a major concern. Major responsible factors are nighttime driving, road conditions, barriers, sudden interruption by animals, pedestrians, and the influence of alcohol. Injuries were highly frequent in Head and neck region followed by extremities. There is a need to emphasize on use of helmets and improvement in road conditions and safety measures.
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GOMES, LUIZ H., VIRGILIO A. F. ALMEIDA, JUSSARA M. ALMEIDA, FERNANDO D. O. CASTRO, and LUÍS M. A. BETTENCOURT. "QUANTIFYING SOCIAL AND OPPORTUNISTIC BEHAVIOR IN EMAIL NETWORKS." Advances in Complex Systems 12, no. 01 (2009): 99–112. http://dx.doi.org/10.1142/s0219525909002088.
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Email graphs have been used to illustrate the general properties of social networks of communication and collaboration. However, increasingly, the majority of Internet traffic reflects opportunistic rather than symbiotic social relations. Here we use email data drawn from a large university to construct directed graphs of email exchange that quantify the differences between social and opportunistic behavior, represented by legitimate messages and spam, respectively. We show that while structural characteristics typical of other social networks are shared to a large extent by the legitimate component, they are not characteristic of opportunistic traffic. To complement the graph analysis, which suffers from incomplete knowledge of users external to the domain, we study temporal patterns of communication to show dynamical properties of email traffic. The results indicate that social email traffic has lower entropy (higher structural information) than opportunistic traffic for periods covering both working and non-working hours. We see in general that both social and opportunistic traffics are not random, and that social email shows stronger temporal structure with a high probability for long silences and bursts of a few messages. These findings offer insights into the fundamental differences between social and opportunistic behavior in email networks, and may generalize to the structure of opportunistic social relations in other environments.
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Li, Jian, and Kaan Ozbay. "Hurricane Irene Evacuation Traffic Patterns in New Jersey." Natural Hazards Review 16, no. 2 (2015): 05014006. http://dx.doi.org/10.1061/(asce)nh.1527-6996.0000154.
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Ando, Ruo, Youki Kadobayashi, Hiroki Takakura, and Hiroshi Itoh. "Understanding Traffic Patterns of Covid-19 IoC in Huge Academic Backbone Network SINET." International Journal of Network Security & Its Applications 13, no. 6 (2021): 23–36. http://dx.doi.org/10.5121/ijnsa.2021.13603.
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Recently, APT (Advanced Persistent Threats) groups are using the COVID-19 pandemic as part of their cyber operations. In response to cyber threat actors, IoCs (Indicators of Compromise) are being provided to help us take some countermeasures. In this paper, we analyse how the coronavirus-based cyber attack unfolded on the academic infrastructure network SINET (The Science Information Network) based on the passive measurement with IoC. SINET is Japan's academic information infrastructure network. To extract and analyze the traffic patterns of the COVID-19 attacker group, we implemented a data flow pipeline for handling huge session traffic data observed on SINET. The data flow pipeline provides three functions: (1) identification the direction of the traffic, (2) filtering the port numbers, and (3) generation of the time series data. From the output of our pipeline, it is clear that the attacker's traffic can be broken down into several patterns. To name a few, we have witnessed (1) huge burstiness (port 25: FTP and high port applications), (3) diurnal patterns (port 443: SSL), and (3) periodic patterns with low amplitude (port 25: SMTP) We can conclude that some unveiled patterns by our pipeline are informative to handling security operations of the academic backbone network. Particularly, we have found burstiness of high port and unknown applications with the number of session data ranging from 10,000 to 35,000. For understanding the traffic patterns on SINET, our data flow pipeline can utilize any IoC based on the list of IP address for traffic ingress/egress identification and port filtering.
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Gasparyan, G. A., and M. V. Kulakov. "Optimization of standard arrival procedures at Sheremetyevo airport using rnav path terminators." Civil Aviation High Technologies 24, no. 6 (2021): 17–26. http://dx.doi.org/10.26467/2079-0619-2021-24-6-17-26.
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Holding patterns are established at international airports to make the arriving traffic flow smooth and efficient. One of the main aims of holding patterns is to extend the aircraft arrival route, which allows ATC units to arrange the sequence on the arrival routes more effectively. The article considers the current methods and offers new ideas to improve the efficiency of the inbound traffic flow management using Paths and Terminators concept with HA holding patterns for standard arrival routes at Sheremetyevo Airport. As the main idea for optimizing air traffic management on this stage and reducing the workload on the controller, it is proposed to create extra routes in addition to the existing ones which include holding patterns, that will be used when needed to ensure a well-ordered traffic. The probabilistic method is used to calculate the maximum capacity of existing and proposed arrival routes with holding patterns. The proposed options for restructuring the airspace of the Moscow Terminal Control Area with preserving waypoints of starting standard arrival routes are presented.
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Sundresh, Ashutosh. "Unveiling Complex Traffic Patterns: Applying Chaos Theory to Understand Non-Linear Dynamics in Congestion." International Journal of Science and Research (IJSR) 12, no. 9 (2023): 156–59. http://dx.doi.org/10.21275/mr23830183842.
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Heshtaut, P., and S. Huznei. "Road accidents involving wild animals: Patterns and comparative analysis." Science and Innovations, no. 8 (September 12, 2023): 70–75. http://dx.doi.org/10.29235/1818-9857-2023-08-70-75.
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Quantitative characteristics and a comparative analysis of traffic accidents involving wild animals in Belarus and other countries are given. The patterns of distribution of the ungulates number killed in road accidents are presented by seasons, days of the week, sex and age structure. It has been established that the places of accidents concentration are road sections adjacent to large settlements, as well as highways with high traffic.
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Sun, Lishan, Liya Yao, Shuwei Wang, Jing Qiao, and Jian Rong. "Properties Analysis on Travel Intensity of Land Use Patterns." Mathematical Problems in Engineering 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/815963.
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Quantization of the relationship between travel intensity and land use patterns is still a critical problem in urban transportation planning. Achieved researches on land use patterns are restricted to macrodata such as population and area, which failed to provide detail travel information for transportation planners. There is still problem on how to reflect the relationship between transport and land use accurately. This paper presents a study that is reflective of such an effort. A data extraction method is developed to get the travel origin and destination (OD) between traffic zones based on the mobile data of 100,000 residents in Beijing. Then Point of Interests (POIs) data in typical traffic zones was analyzed combined with construction area investigation. Based on the analysis of travel OD and POI data, the average travel intensity of each land use pattern is quantified. Research results could provide a quantitative basis for the optimization of urban transportation planning.
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Akinfala, Olanrewaju Oluwafemi, Emmanuel Enyeribe Ege, and Ladi Folorunso Ogunwolu. "ARRIVAL PATTERNS AND TRAFFIC FLOW CHARACTERISTICS AT SIGNALIZED INTERSECTIONS." FUTA JOURNAL OF ENGINEERING AND ENGINEERING TECHNOLOGY 15, no. 1 (2021): 11–28. http://dx.doi.org/10.51459/futajeet.2021.15.1.208.
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Traffic arrivals at signal intersection approaches is inherently stochastic. This variability is typically reflected by I-ratio and there is a general consensus that the presence or absence of nearby upstream signal affects Variance to Mean Ratio (I-ratio). However, the effect of time resolution on arrival variability and the interaction effect between upstream signal and time resolution is yet to be examined in detail. This can lead to model misspecification and invariably, erroneous outcomes. This work examines the effect of time resolution and intersection type and their interaction on I-ratio and the resultant probability distributions. Traffic arrivals were measured at high time resolution- 10 seconds interval and then aggregated to lower time resolutions (30-150 seconds) at six intersections. Spectral density analysis showed statistically significant periodicity, specifically at 30 seconds interval with p-values < 0.0001 at all connected intersections while observations at isolated intersections lacked periodicity. Two-way ANOVA using I-ratio as the dependent variable and intersection type and time-resolution as the independent variables was performed. Statistically significant effect with F-value 8.606 at p-value < 0.0001 and R2 value 0.32 were observed. Intersection type, time resolution and the interaction between them were statistically significant, with p-values 0.002, < 0.0001 and 0.000 respectively. The combined effect of these factors led to a wide I-ratio range of 0.37-9.2. Negative Binomial, Poisson, and Binomial distributions represented 76.4, 20.4 and 4.2% of all I-ratios observed. Therefore, in contrast to literature which recommends Poisson, Negative Binomial may be a better suited probability distribution for traffic arrivals.
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Ji, Soo-Yeon, Bong Keun Jeong, and Dong H. Jeong. "An Analysis of Temporal Features in Multivariate Time Series to Forecast Network Events." Applied Sciences 13, no. 18 (2023): 10411. http://dx.doi.org/10.3390/app131810411.
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Analyzing network traffic over time is crucial for understanding the changes in network activity. To properly examine network traffic patterns over time, multiple network events in each timestamp need to be converted to time series data. In this study, we propose a new approach to transform network traffic data into time series formats by extracting temporal features to analyze normal/attack patterns. The normal patterns indicate network traffic occurred without any intrusion-related activities, whereas the attack patterns denote potential threats that deviate from the normal patterns. To evaluate the features, long short-term memory (LSTM) is applied to forecast multi-step network normal and attack events. Visual analysis is also performed to enhance the understanding of key features in the network. We compared the performance differences using time scales of 60 and 120 s. Upon evaluation, we found that the temporal features extracted with the 60 s time scale exhibited better performance in forecasting future network events.
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Balasubramaniam, Anandkumar, Thirunavukarasu Balasubramaniam, Anand Paul, and HyunCheol Seo. "Electric Vehicle Usage Pattern Analysis Using Nonnegative Matrix Factorization in Renewable EV-Smart Charging Grid Environment." Mathematical Problems in Engineering 2022 (March 22, 2022): 1–9. http://dx.doi.org/10.1155/2022/9365214.
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The global utilization of electric vehicles (EVs) is exponentially increasing due to the increased availability of cost-efficient EVs and infrastructure managements for the EVs. In spite of the increasing usage of EVs, the problem of EV usage patterns’ analysis and implementing sustainable infrastructure for the EV transportation is still under development. In addition to this, there is a challenging problem of long waiting hours in traffic signals. This study deals with these problems by proposing an architecture that includes EV usage pattern analysis using nonnegative matrix factorization (NMF) technique and renewable solar-powered wireless smart charging grid to effectively utilize or mitigate the long traffic signal waiting hours. The insights from the EV usage patterns are analyzed and presented showing the importance of usage pattern analysis alongside to the presented architecture of renewable solar-powered wireless EV-smart charging grid. These implementations improvise the usage of the EVs and enhancing the transportation experience, which in turn leads to the development of sustainable smart transportation.
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Sun, Tuo, Shihao Zhu, Ruochen Hao, Bo Sun, and Jiemin Xie. "Traffic Missing Data Imputation: A Selective Overview of Temporal Theories and Algorithms." Mathematics 10, no. 14 (2022): 2544. http://dx.doi.org/10.3390/math10142544.
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A great challenge for intelligent transportation systems (ITS) is missing traffic data. Traffic data are input from various transportation applications. In the past few decades, several methods for traffic temporal data imputation have been proposed. A key issue is that temporal information collected by neighbor detectors can make traffic missing data imputation more accurate. This review analyzes traffic temporal data imputation methods. Research methods, missing patterns, assumptions, imputation styles, application conditions, limitations, and public datasets are reviewed. Then, five representative methods are tested under different missing patterns and missing ratios. California performance measurement system (PeMS) data including traffic volume and speed are selected to conduct the test. Probabilistic principal component analysis performs the best under the most conditions.
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Su, Xiaokun, Chenrouyu Zheng, Yefei Yang, Yafei Yang, Wen Zhao, and Yue Yu. "Spatial Structure and Development Patterns of Urban Traffic Flow Network in Less Developed Areas: A Sustainable Development Perspective." Sustainability 14, no. 13 (2022): 8095. http://dx.doi.org/10.3390/su14138095.
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Sustainable development is a scientific development requirement for economic, social, and ecological development and is particularly important for less developed areas to achieve high quality development. Among them, the traffic flow network is a key contributor to economic activity and an inclusive society, as well as influencing the regional ecology, and is an important way to reflect the connection and structure of cities and towns. Based on the literature related to sustainable development, the article takes the passenger traffic data of highways, railways, and aviation of Inner Mongolia in 2021 as the sample and applies the complex network analysis method to analyze the traffic flow network structure and refine the spatial development patterns. The results show that: (1) The highway network is manifested as the connection between the central urban areas and surrounding banner counties and the connection between the adjacent banner counties. The railroad flow is extended and expanded by the railway line with core cities as the development axis. The internal and external connections of Hohhot are the general form of aviation network. The less developed areas under traffic flow network show obvious pointing of core cities and important node towns. (2) Each traffic flow network has the tendency of scale-free and small-world properties. The influence of key town nodes in the traffic flow network is relatively limited. (3) The town connection patterns under the highway, railway, and air flow networks are “single-core and multi-point”, “axis-spoke”, and “hub-spoke”, respectively. The multiple traffic flows support the development framework of towns in less developed areas. This paper also proposes strategies for the regional transport and urban pattern with complementary advantages and high quality and sustainable development in less developed areas.
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Wiwekananda, Agus Bhayu W., I. Wayan Arimbawa, and Ratna Rayeni Natasha Roosseno. "The characteristics and patterns of maxillofacial fractures at Mangusada general hospital, Badung-Bali." International Journal of Research in Medical Sciences 7, no. 6 (2019): 2318. http://dx.doi.org/10.18203/2320-6012.ijrms20192520.
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Background: Trauma is one of the leading causes of death among people under 40 years of age and approximately 10 percent of the cases have been maxillofacial trauma. There was limited number of studies on maxillofacial fractures in Indonesia. Thus, this research attempted to investigate the characteristics and patterns of maxillofacial fractures at Mangusada General Hospital in Badung-Bali.Methods: This research was a cross-sectional descriptive study which conducted at Mangusada General Hospital in the period of 1 January 2016 - 31 December 2017. The 127 samples selected using non-probability sampling. The inclusive criteria involved all maxillofacial trauma cases and the exclusive criteria focused on maxillofacial fractures that received intervention or with incomplete medical records. Each data was collected from the medical records and then analysed descriptively.Results: From 127 samples, male dominated the sample on the gender-based criteria (70.1%) and the highest frequency of all age groups is 21-30 years old on the age group based criteria (23.6%). Maxillary fractures are the most occurring maxillofacial cases, which took up 33.6%. The main cause of the cases is traffic accidents (89.0%).Conclusions: Maxillary fractures are the highest maxillofacial cases at Mangusada General Hospital on 1 January 2016 - 31 December 2016 period of time. The productive male age groups are the most affected groups due to traffic accidents.
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Raihan, Faza Muhammad, and Yusup Miftahuddin. "Penerapan Algoritma Aprioti Pada Riwayat Data Kecelakaan Lalu Lintas." Infotek : Jurnal Informatika dan Teknologi 5, no. 1 (2022): 62–71. http://dx.doi.org/10.29408/jit.v5i1.4402.
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With the increase of vehicle users, traffic accidents tend to happen more often. One of many ways to minimize the occurrence of traffic accidents is to process accident data history using data mining techniques. This technique is utilized in order to gain information regarding the relational pattern of traffic accidents. The data mining technique used is the association rule technique with the Apriori algorithm. One of the stages of analysis that has attracted the eyes of many researches to produce an efficient Apriori algorithm is analyzing the frequency pattern of an association that can be identified with two benchmarks; Support and Confidence. Currently, the determination of the minimum support value will be repeated by the user until it reaches a positive correlation value. This study applies a certain method to determine the minimum support value with the final result of achieving positive correlation on all datas as a reference for the lift ratio value >1 and getting 6 of the most frequent traffic accident patterns
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Dewanto, Evan Boedi, Ahmad Yudianto, and Magda Rosalina Hutagalung. "Wound Pattern Profile in Deceased Victims of Traffic Accidents in Raden Said Sukanto Bhayangkara Hospital Jakarta from January 2017 until December 2018." JUXTA: Jurnal Ilmiah Mahasiswa Kedokteran Universitas Airlangga 13, no. 2 (2022): 79–82. http://dx.doi.org/10.20473/juxta.v13i22022.79-82.
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Highlights:1. Traffic accidents can cause different types of wounds.2. Traffic accident victims were predominantly male at the age of 26-45 years old. 3. The most found wounds were abrasions and the most affected area were head and neck. AbstractIntroduction: A traffic accident is an incident that happens on the road, such as a car crash that started on the road and leads to injury or death or damaged properties in the surrounding environment. Traffic accident injuries have a different pattern from any other events or violence, such as a mechanical injury due to friction with asphalt. The wounds that are usually found on the victims are abrasion, laceration, contusion, and wounds with fracture. About 70% of traffic accidents in Indonesia are happening in Java. The aim of this study was to determine the wound patterns of victims of traffic accidents, particularly deceased victims recorded in the Forensic Department of Raden Said Sukanto Bhayangkara Hospital Jakarta from January 2017 until December 2018.Methods: This was a descriptive study using secondary data. Consecutive sampling methods were used by using an external examination form obtained from the Forensic Department of Raden Said Sukanto Bhayangkara Hospital Jakarta from January 2017 until December 2018. Age, gender, and wound patterns such as abrasion, laceration, contusion, and wounds with fracture data were taken.Results: 67 cases of traffic accidents were recorded. Male victims were the most common victims (87.5%) within the age of 26–45 years old (42.18%). Abrasions were the most common wounds found (44.92%) and head and neck region were the most affected area (44.09%).Conclusion: The deceased victims of traffic accidents were mostly males aged 26–45 years old. The most common wound found were abrasions and the most affected areas were head and neck region.
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Kandel, Mohamed Ahmed, Faris H. Rizk, Lima Hongou, Ahmed Mohamed Zaki, Hakan Khan, and El-Sayed M. El El-Kenawy. "Evaluating the Efficacy of Deep Learning Architectures in Predicting Traffic Patterns for Smart City Development." Journal of Artificial Intelligence and Metaheuristics 6, no. 2 (2023): 26–35. http://dx.doi.org/10.54216/jaim.060203.
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Smart city development necessitates the implementation of effective traffic management strategies. In this vein, various deep learning architectures, including VGG16Net, VGG19Net, GoogLeNet, ResNet-50, and AlexNet, are employed to predict diverse traffic patterns extracted from a comprehensive dataset. Evaluating performance metrics such as accuracy, sensitivity, and specificity reveals discernible variations among models, with ResNet-50 and AlexNet demonstrating superior predictive capabilities. Descriptive statistics and statistical analyses, including ANOVA and the Wilcoxon Signed Rank Test, provide nuanced insights into model differences and significance. The findings bear significant implications for urban planners and policymakers transforming cities into intelligent ecosystems, offering valuable insights for informed decision-making in innovative city development. Improved traffic predictions enhance daily commuting experiences and contribute to the informed development of sustainable urban infrastructure, aligning seamlessly with the ongoing evolution of smart cities toward a more connected and efficient future. Notably, AlexNet exhibits a significant accuracy of 0.931780366 in the context of traffic pattern prediction.
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Jeong, Dong Hyun, Bong-Keun Jeong, and Soo-Yeon Ji. "Multi-Resolution Analysis with Visualization to Determine Network Attack Patterns." Applied Sciences 13, no. 6 (2023): 3792. http://dx.doi.org/10.3390/app13063792.
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Analyzing network traffic activities is imperative in network security to detect attack patterns. Due to the complex nature of network traffic event activities caused by continuously changing computing environments and software applications, identifying the patterns is one of the challenging research topics. This study focuses on analyzing the effectiveness of integrating Multi-Resolution Analysis (MRA) and visualization in identifying the attack patterns of network traffic activities. In detail, a Discrete Wavelet Transform (DWT) is utilized to extract features from network traffic data and investigate their capability of identifying attacks. For extracting features, various sliding windows and step sizes are tested. Then, visualizations are generated to help users conduct interactive visual analyses to identify abnormal network traffic events. To determine optimal solutions for generating visualizations, an extensive evaluation with multiple intrusion detection datasets has been performed. In addition, classification analysis with three different classification algorithms is managed to understand the effectiveness of using the MRA with visualization. From the study, we generated multiple visualizations associated with various window and step sizes to emphasize the effectiveness of the proposed approach in differentiating normal and attack events by forming distinctive clusters. We also found that utilizing MRA with visualization advances network intrusion detection by generating clearly separated visual clusters.
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Marianingrum, Dyah, Kasih Purwati, and Muhammad Raflin Ilhami Pratama. "GAMBARAN POLA LUKA PADA KORBAN KECELAKAAN LALU LINTAS DI RUMAH SAKIT UMUM DAERAH RAJA AHMAD TABIB PROVINSI KEPULAUAN RIAU PERIODE JANUARI-DESEMBER TAHUN 2022." Zona Kedokteran: Program Studi Pendidikan Dokter Universitas Batam 14, no. 1 (2024): 17–25. http://dx.doi.org/10.37776/zked.v14i1.1362.
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Background: Traffic accidents are a leading cause of death in developing countries, and the incidence continues to rise with population growth and increased transportation mobility. This study aims to analyze the pattern of injuries in traffic accident victims treated at Raja Ahmad Tabib Regional General Hospital, Riau Islands Province, during the period from January to December 2022. Methods: This study utilized a retrospective descriptive design with a cross-sectional approach. The population consisted of 1,533 traffic accident victims during the January-December 2022 period, and a sample of 318 individuals was selected using purposive sampling. Data analysis was conducted using descriptive statistics. Results: The study revealed that 69.5% of the victims were aged over 30 years, 66.7% were male, and 84.0% survived the accidents. The most common injury pattern among pedestrians was abrasions (37.9%), motorbike riders most frequently experienced abrasions (48.0%), car drivers mostly suffered from lacerations (43.4%), and car passengers predominantly had contusions (50.0%). Conclusion: This research concludes that the injury patterns in traffic accident victims at Raja Ahmad Tabib Regional General Hospital in the Riau Islands Province during 2022 show that abrasions are dominant among motorbike riders, lacerations are predominant among car drivers, and contusions are prevalent among car passengers.
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Qin, Yanjun, Haiyong Luo, Fang Zhao, Zhongliang Zhao, and Mengling Jiang. "A traffic pattern detection algorithm based on multimodal sensing." International Journal of Distributed Sensor Networks 14, no. 10 (2018): 155014771880783. http://dx.doi.org/10.1177/1550147718807832.
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Nowadays, smartphones are widely and frequently used in people’s daily lives for their powerful functions, which generate an enormous amount of data accordingly. The large volume and various types of data make it possible to accurately identify people’s travel behaviors, that is, transportation mode detection. Using the transportation mode detection, results can increase commuting efficiency and optimize metropolitan transportation planning. Although much work has been done on transportation mode detection problem, the accuracy is not sufficient. In this article, an accurate traffic pattern detection algorithm based on multimodal sensing is proposed. This algorithm first extracts various sensory features and semantic features from four types of sensor (i.e. accelerator, gyroscope, magnetometer, and barometer). These sensors are commonly embedded in commodity smartphones. All the extracted features are then fed into a convolutional neural network to infer traffic patterns. Extensive experimental results show that the proposed scheme can identify four transportation patterns with 94.18% accuracy.
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Neizvestnyi, Sergyi. "DETERMINATION OF THE PERIOD OF EFFICIENT FUNCTIONING OF A ROAD." Dorogi i mosti 2023, no. 27 (2023): 245–52. http://dx.doi.org/10.36100/dorogimosti2023.27.245.
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Introduction. One of the reasons for ordering the reconstruction of a road or part of it is the deterioration of the safety of traffic flows, as a result of which the number of victims and material losses in road traffic accidents (traffic accidents) increases. Interchanges at the same level have the greatest influence on the traffic intensity on the highway, as traffic flows are redistributed at them. Depending on the intensity of traffic on them, the maximum possible intensity of traffic on the road section as a whole will be determined. Problem statement. The analysis of determining the need for road reconstruction in accordance with the regulations established the need to justify and clarify traffic intensity data, according to which it is necessary to assign a road reconstruction and determine the period of effective operation of the road, for this it is necessary to conduct a number of traffic flow studies. Purpose. The purpose of the article is to study the patterns of traffic flows at traffic junctions at the same level and the relationship of the characteristics of traffic flows to each other. The methodology of an experimental study of traffic flow patterns at traffic intersections at one level consists in surveying the real conditions of traffic flows, namely, the traffic intervals between cars on the sections of the race between the intersections with a traffic intensity of 300 to 600 vehicles per hour per lane, to further establish the dependence of the availability and number of free movement intervals in the traffic flow.
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Liu, Jingru, Rusong Wang, and Jianxin Yang. "A scenario analysis of Beijing's private traffic patterns." Journal of Cleaner Production 15, no. 6 (2007): 550–56. http://dx.doi.org/10.1016/j.jclepro.2006.06.002.
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Einbeck, Jochen, and Jo Dwyer. "Using principal curves to analyse traffic patterns on freeways." Transportmetrica 7, no. 3 (2011): 229–46. http://dx.doi.org/10.1080/18128600903500110.
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Delavary, Milad, Zahra Ghayeninezhad, and Martin Lavallière. "Evaluating the Impact of Increased Fuel Cost and Iran’s Currency Devaluation on Road Traffic Volume and Offenses in Iran, 2011–2019." Safety 6, no. 4 (2020): 49. http://dx.doi.org/10.3390/safety6040049.
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Trends and underlying patterns should be identified in the timely distribution of road traffic offenses to increase traffic safety. In this study, a time series analysis was used to study the incidence rate of road traffic violations on Iranian rural roads. Road traffic volume and offenses data from March 2011 to October 2019 were aggregated. Interrupted time series were used to evaluate the impact of increasing fuel cost in June of 2013 and July of 2014 and the currency devaluation of Rial vs. US dollars in July of 2017 on trends and patterns, traffic volume, and number of offenses. A change-point detection (CPD) analysis was also used to identify singular changes in the frequency of traffic offenses. Results show a general decline in the number of overtaking and speeding offenses of −24.31% and −13.23%, respectively, due to the first increase in fuel cost. The second increase only reduced overtaking by 20.97%. In addition, Iran’s currency devaluation reduced the number of overtaking offenses by 26.39%. Modeling a change-point detection and a Mann-Kendall Test of traffic offenses in Iran, it was found that the burden of violations was reduced.
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Xiong, Liyan, Weihua Ding, Xiaohui Huang, and Weichun Huang. "CLSTAN: ConvLSTM-Based Spatiotemporal Attention Network for Traffic Flow Forecasting." Mathematical Problems in Engineering 2022 (July 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/1604727.
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Traffic flow forecasting is the essential part of intelligent transportation sSystem (ITS), which can fully protect traffic safety and improve traffic system management capability. Nevertheless, it is still a challenging problem, which is influenced by many complex factors, including regional distribution and external factors (e.g., holidays and weather). To combine various factors to forecast traffic flow, we presented a novel neural network structure called ConvLSTM-based Spatiotemporal Attention Network (CLSTAN). Specifically, our proposed model is composed of four modules: a preliminary feature extraction module, a spatial attention module, a temporal attention module, and an information fusion module. The spatiotemporal attention module can efficiently learn the complex spatiotemporal patterns of traffic flow through the attention mechanism. The spatial attention module uses a series of initial traffic flow maps as input and obtains the weights of the various regions through a ConvLSTM. The temporal attention module uses the spatially weighted traffic flow map as input and acquires the complex spatiotemporal patterns of traffic flow by a ConvLSTM that introduces an attention mechanism. Finally, the information fusion module integrates spatiotemporal information from multiple time dimensions to forecast future traffic flow. Moreover, to confirm the validity of our method, our experiments were conducted extensively on the TaxiBJ and BikeNYC datasets, and ultimately, CLSTAN performed better than other baseline experiments.
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Tišljarić, Leo, Sofia Fernandes, Tonči Carić, and João Gama. "Spatiotemporal Road Traffic Anomaly Detection: A Tensor-Based Approach." Applied Sciences 11, no. 24 (2021): 12017. http://dx.doi.org/10.3390/app112412017.
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The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related to traffic anomaly detection. They outperform other models regarding simultaneously capturing spatial and temporal components, which are of immense importance in traffic dataset analysis. This paper presents a tensor-based method for extracting the spatiotemporal road traffic patterns represented with the speed transition matrices, with the goal of anomaly detection. A novel anomaly detection approach is presented, which relies on computing the center of mass of the observed traffic patterns. The method was evaluated on a large road traffic dataset and was able to detect the most anomalous parts of the urban road network. By analyzing spatial and temporal components of the most anomalous traffic patterns, sources of anomalies can be identified. Results were validated using the extracted domain knowledge from the Highway Capacity Manual. The anomaly detection model achieved a precision score of 92.88%. Therefore, this method finds its usages for safety experts in detecting potentially dangerous road segments, urban traffic planners, and routing applications.