Journal articles on the topic 'Traffic flow reconstruction'
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Wilkie, David, Jason Sewall, and Ming Lin. "Flow reconstruction for data-driven traffic animation." ACM Transactions on Graphics 32, no. 4 (July 21, 2013): 1–10. http://dx.doi.org/10.1145/2461912.2462021.
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Kriso, S., J. Peinke, R. Friedrich, and P. Wagner. "Reconstruction of dynamical equations for traffic flow." Physics Letters A 299, no. 2-3 (July 2002): 287–91. http://dx.doi.org/10.1016/s0375-9601(02)00288-8.
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Hu, Kong, Shun Ying Zhu, and Hong Wang. "The Research of Freeway Single Horn-B-Type Interchange Situ Reconstruction and Expansion Traffic Organization." Applied Mechanics and Materials 361-363 (August 2013): 1895–99. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.1895.
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Interchange is the important conversion node of freeway. In the process of Interchange reconstruction and expansion, how to ensure the traffic of Interchange in all directions operate normally, to reduce the impact of the construction on traffic flow and to ensure the security of the roads traffic flow smoothly is very crucially. Through analyzing the characteristics of the driver, road and traffic flow, to study and determine the Interchange situ reconstruction and expansion traffic organization plan and security measures during construction traffic organization.
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Bilotta, Stefano, and Paolo Nesi. "Traffic flow reconstruction by solving indeterminacy on traffic distribution at junctions." Future Generation Computer Systems 114 (January 2021): 649–60. http://dx.doi.org/10.1016/j.future.2020.08.017.
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Zhang, Xue Li. "Path Reconstruction of Intelligent Traffic Based on Positive Feedback System." Applied Mechanics and Materials 513-517 (February 2014): 3160–64. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.3160.
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Traffic congestion are prevalent in worldwide cities. The imbalance between demand and supply of urban traffic is the root cause of this problem. So taking effective measures to regulate traffic demand, and guiding the traffic problems of the supply and demand balance is the best way to solve traffic congestion. This paper improves the TDM measure, and combines with intelligent information platform for the design of a new urban transport demand management adaptability of dynamic traffic data analysis platform. The platform supported by the technology of wireless sensor communications, intelligent terminals, the Internet and cloud computing is facing with the dynamic needs of traffic flow and traffic congestion state to carry out the operations of spatiotemporal data mining, clustering, and track detection, and to apply it into the traffic hot spots, abnormal driving track, traffic congestion trends and traffic flow detection and analysis, which has a good reference value for the improvement of management and service level of traffic intelligent systems.
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Bilotta, Stefano, and Paolo Nesi. "Estimating CO2 Emissions from IoT Traffic Flow Sensors and Reconstruction." Sensors 22, no. 9 (April 28, 2022): 3382. http://dx.doi.org/10.3390/s22093382.
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CO2 emissions from burning fossil fuels make a relevant contribution to atmospheric changes and climate disruptions. In cities, the contribution by traffic of CO2 is very relevant, and the general CO2 estimation can be computed (i) on the basis of the fuel transformation in energy using several factors and efficiency aspects of engines and (ii) by taking into account the weight moved, distance, time, and emissions factor of each specific vehicle. Those approaches are unsuitable for understanding the impact of vehicles on CO2 in cities since vehicles produce CO2 depending on their specific efficiency, producer, fuel, weight, driver style, road conditions, seasons, etc. Thanks to today’s technologies, it is possible to collect real-time traffic data to obtain useful information that can be used to monitor changes in carbon emissions. The research presented in this paper studied the cause of CO2 emissions in the air with respect to different traffic conditions. In particular, we propose a model and approach to assess CO2 emissions on the basis of traffic flow data taking into account uncongested and congested conditions. These traffic situations contribute differently to the amount of CO2 in the atmosphere, providing a different emissions factor. The solution was validated in urban conditions of Florence city, where the amount of CO2 is measured by sensors at a few points where more than 100 traffic flow sensors are present (data accessible on the Snap4City platform). The solution allowed for the estimation of CO2 from traffic flow, estimating the changes in the emissions factor on the basis of the seasons and in terms of precision. The identified model and solution allowed the city’s distribution of CO2 to be computed.
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Tang, Wenyun, Jiahui Chen, Chao Sun, Hanbing Wang, and Gen Li. "Traffic Demand Estimations Considering Route Trajectory Reconstruction in Congested Networks." Algorithms 15, no. 9 (August 28, 2022): 307. http://dx.doi.org/10.3390/a15090307.
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Traffic parameter characteristics in congested road networks are explored based on traffic flow theory, and observed variables are transformed to a uniform format. The Gaussian mixture model is used to reconstruct route trajectories based on data regarding travel routes containing only the origin and destination information. Using a bi-level optimization framework, a Bayesian traffic demand estimation model was built using route trajectory reconstruction in congested networks. Numerical examples demonstrate that traffic demand estimation errors, without considering a congested network, are within ±12; whereas estimation demands considering traffic congestion are close to the real values. Using the Gaussian mixture model’s technology of trajectory reconstruction, the mean of the traffic demand root mean square error can be stabilized to approximately 1.3. Traffic demand estimation accuracy decreases with an increase in observed data usage, and the designed iterative algorithm can predict convergence with 0.06 accuracy. The evolution rules of urban traffic demands and road flows in congested networks are uncovered, and a theoretical basis for alleviating urban traffic congestion is provided to determine traffic management and control strategies.
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Haj-Salem, H., and J. P. Lebacque. "Reconstruction of False and Missing Data with First-Order Traffic Flow Model." Transportation Research Record: Journal of the Transportation Research Board 1802, no. 1 (January 2002): 155–65. http://dx.doi.org/10.3141/1802-18.
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In previous studies, two traffic data-cleaning algorithms were developed at the Institut National de Recherche sur les Transports on the basis of filtering techniques and statistical approaches. Because of their mathematical structure (linearity of the process), both algorithms present a high level of inaccuracy in the case of nonhomogeneous traffic conditions at the location of the measurement stations (for example, free flow upstream and congestion downstream, or vice versa). A new algorithm for solving the traffic data-cleaning problem on the basis of real-time application of a dynamic first-order modeling approach was devised to take into account the nonlinearity of the traffic phenomenon. The developed algorithm, named PROPAGE, was tested using real data measurements, including a wide spectrum of traffic conditions. Compared with results from previous algorithms, the results obtained were more accurate.
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Zhu, Huai Zhong, Shun Ying Zhu, Yu Wu, and Shuang Deng. "Evaluation of Traffic Organization Scheme during Highway Reconstruction Period." Applied Mechanics and Materials 361-363 (August 2013): 2007–11. http://dx.doi.org/10.4028/www.scientific.net/amm.361-363.2007.
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An appropriate traffic organization scheme of highway reconstruction can ensure the normal operation of the traffic flow, and reduce the influence of reconstruction. Based on evaluation principles, this paper will analysis and screen index systems such as level of service, negative economic benefit and traffic environmental impact. The evaluation criterion of each index will be ascertained based on existing research results of level of service for highway. Then an integrated approach combined with dynamic comprehensive evaluation and analytic hierarchy process is proposed to evaluate the traffic organization during the highway construction. Last, we combine an example of a highway reconstruction project to analysis the proposed integrated approach.
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Hui, Meng, Lin Bai, YanBo Li, and QiSheng Wu. "Highway Traffic Flow Nonlinear Character Analysis and Prediction." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/902191.
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In order to meet the highway guidance demand, this work studies the short-term traffic flow prediction method of highway. The Yu-Wu highway which is the main road in Chongqing, China, traffic flow time series is taken as the study object. It uses phase space reconstruction theory and Lyapunov exponent to analyze the nonlinear character of traffic flow. A new Volterra prediction method based on model order reduction via quadratic-linear systems (QLMOR) is applied to predict the traffic flow. Compared with Taylor-expansion-based methods, these QLMOR-reduced Volterra models retain more information of the system and more accuracy. The simulation results using this new Volterra model to predict short time traffic flow reveal that the accuracy of chaotic traffic flow prediction is enough for highway guidance and could be a new reference for intelligent highway management.
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Mrgole, Anamarija L., and Drago Sever. "Incorporation of Duffing Oscillator and Wigner-Ville Distribution in Traffic Flow Prediction." PROMET - Traffic&Transportation 29, no. 1 (February 6, 2017): 13–22. http://dx.doi.org/10.7307/ptt.v29i1.2116.
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The main purpose of this study was to investigate the use of various chaotic pattern recognition methods for traffic flow prediction. Traffic flow is a variable, dynamic and complex system, which is non-linear and unpredictable. The emergence of traffic flow congestion in road traffic is estimated when the traffic load on a specific section of the road in a specific time period is close to exceeding the capacity of the road infrastructure. Under certain conditions, it can be seen in concentrating chaotic traffic flow patterns. The literature review of traffic flow theory and its connection with chaotic features implies that this kind of method has great theoretical and practical value. Researched methods of identifying chaos in traffic flow have shown certain restrictions in their techniques but have suggested guidelines for improving the identification of chaotic parameters in traffic flow. The proposed new method of forecasting congestion in traffic flow uses Wigner-Ville frequency distribution. This method enables the display of a chaotic attractor without the use of reconstruction phase space.
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Rodriguez-Vega, Martin, Carlos Canudas-de-Wit, and Hassen Fourati. "Location of turning ratio and flow sensors for flow reconstruction in large traffic networks." Transportation Research Part B: Methodological 121 (March 2019): 21–40. http://dx.doi.org/10.1016/j.trb.2018.12.005.
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Hou, Yue, Da Li, Di Zhang, and Zhiyuan Deng. "An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction." Discrete Dynamics in Nature and Society 2022 (September 23, 2022): 1–22. http://dx.doi.org/10.1155/2022/5604674.
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Traffic flow is chaotic due to nonstationary realistic factors, and revealing the internal nonlinear dynamics of chaotic data and making high-accuracy predictions is the key to traffic control and inducement. Given that high-quality phase space reconstruction is the foundation of predictive modeling. Firstly, an improved C-C method based on the fused norm search domain is proposed to address the issue that the C-C method in the phase space reconstruction algorithm does not meet the Euclidean metric accuracy and reduces the reconstruction quality when the infinite norm metric is used. Secondly, to address the problem of insufficient learning ability of traditional convolutional combinatorial modeling for complex phase space laws of chaotic traffic flow, the high-dimensional phase space features are extracted using the layer-by-layer pretraining mechanism of convolutional deep belief networks (CDBNs), and the temporal features are extracted by combining with long short-term memory (LSTM). Finally, an improved probabilistic dynamic reproduction-based genetic algorithm (PDRGA) is proposed to address the problem of the hybrid model falling into a local optimum when learning the phase space law. Experiments are conducted in three aspects: phase space reconstruction quality analysis, comparison of optimization algorithm convergence, and prediction model performance comparison. The experimentation with two data sets demonstrates that the improved C-C method combines the advantages of the high accuracy metric of the L2 norm with the low operational complexity of the infinite norm, achieving a balance between reconstruction quality and algorithm efficiency. The proposed PDRGA optimization algorithm is a lightweight improvement of the traditional genetic algorithm (GA) and solves the problem that the model tends to fall into a local optimum by optimizing the initial weights of CDBN. Meanwhile, the five error evaluation indexes of the proposed PDRGA-CDBN-LSTM hybrid model are lower than those of the baseline model, providing a new modeling idea for chaotic traffic flow prediction.
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Wu, Ke Man, and Lian De Zhong. "Established Methods of Space between Expressway Work Zones Based on Delay of Expressway." Applied Mechanics and Materials 409-410 (September 2013): 1374–78. http://dx.doi.org/10.4028/www.scientific.net/amm.409-410.1374.
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Via microscopic traffic flow simulation, based on the theory of traffic capacity and queue delay of vehicles on work zones and relying on the Reconstruction and Widening of Fekai Expressway, the paper gives suggested values of minimum space between adjacent work zones in the same direction under different traffic volumes and traffic compositions for references, during the construction of a two-way-8-lane highway with half road way closed. The result can promote work zone traffic safety.
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Wang, Xiaoquan, Wenjun Li, Chaoying Yin, Shaoyu Zeng, and Peng Liu. "A multiple-parameter approach for short-term traffic flow prediction." Modern Physics Letters B 35, no. 14 (May 10, 2021): 2150245. http://dx.doi.org/10.1142/s0217984921502456.
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This study proposes a short-term traffic flow prediction approach based on multiple traffic flow basic parameters, in which the chaos theory and support vector regression are utilized. First, a high-dimensional variable space can be obtained according to the traffic flow fundamental function. Then, a maximum conditional entropy method is proposed to determine the embedding dimension. And multiple time series are reconstructed based on the phase space reconstruction theory using the time delay obtained by mutual information method and the embedding dimension captured by the maximum conditional entropy method. Finally, the reconstructed phase space is used as the input and the support vector regression optimized by the genetic algorithm is utilized to predict the traffic flow. Numerical experiments are performed and the results show that the approach proposed has strong fitting capability and better prediction accuracy.
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Boyarshinov, M. G., A. S. Vavilin, and A. G. Shumkov. "FOURIER ANALYSIS OF THE TRAFFIC FLOW INTENSITY." Intelligence. Innovations. Investment, no. 4 (2021): 46–59. http://dx.doi.org/10.25198/2077-7175-2021-4-46.
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The relevance of this work is determined by the need to find modern ways to process the information about traffic flows for regulating and controlling the movement of transport and pedestrians, to reduce congestion, road accidents, etc. The object of study is a part of road with heavy two-way traffic, equipped with a software and hardware complex that allows to measure the characteristics of the transport flow. The subject of the study is the daily intensity of the cars flow during the week, from Monday to Sunday. The purpose of this study is to analyze the amplitudes, frequencies, and periods of harmonic functions obtained by decomposing the time series of road traffic intensities to identify the main patterns of traffic flow formation. As a theoretical and methodological approach, the decomposition of the function of the traffic flow intensity in the Fourier series with respect to harmonic functions is used. The approach developed by the authors using the fast Fourier transform procedure made it possible to determine the amplitude-frequency characteristics of the time series under consideration, which is a scientific novelty of the analysis. It is proposed to use the «period-amplitude» characteristics as physically more meaningful instead of the «frequency-amplitude» dependencies traditionally used for the analysis. The processing of data obtained from software and hardware complexes allowed us to determine dependences of the car flow intensity on the road of the Perm city at different averaging intervals, to describe the features of the motor transport movement on the road under consideration. As a result of the study, the amplitude-frequency characteristics of time series are obtained. It is shown that the individual harmonics of the Fourier series expansion of the traffic flow intensity, which exhibits the properties of a random function, duplicate the periodicity of the global, local, and intermediate extremes of the original function and have similar periods. The practical significance consists in the use of the decomposition of the function of the traffic flow intensity in the Fourier series of harmonic functions for predicting traffic flows, controlling the operation of traffic lights, monitoring the operation of equipment, as well as in the reconstruction, design and construction of roads and road objects. The study will continue in the direction of obtaining, processing and determining the «period-amplitude» characteristics for time series of traffic flow intensity for other road networks.
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Boyarshinov, M. G., A. S. Vavilin, and A. G. Shumkov. "USING THE COMPLEX OF PHOTO AND VIDEO RECORDING OF TRAFFIC VIOLATIONS TO IDENTIFY DETERMINISTIC AND STOCHASTIC COMPONENTS OF THE TRAFFIC FLOW INTENSITY." Intelligence. Innovations. Investment, no. 3 (2021): 61–71. http://dx.doi.org/10.25198/2077-7175-2021-3-61.
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The relevance of the manuscript is due to the need to process and analyze the information accumulated by the complexes of photo-video recording of traffic violations, which will further develop mathematical, computational and simulation models of road transport, solve problems of optimization and management of traffic flows, make management decisions to reduce the number of congestion and reduce the anthropogenic load on the environment. The object of the study is a part of a three-lane road with heavy one-way traffic, equipped with a software and technical complex that allows measuring the main characteristics of the traffic flow (vehicle speeds, including the average values on the controlled road part, driving time, etc.). The subject of the study is the traffic flow intensity during a 7-day time (from Monday to Sunday). The analysis of the obtained dependences allowed us to formulate a hypothesis about the presence of determin- istic and stochastic components in the traffic flow intensity, which is a random function of time, and the verification of which is the purpose of this study. Statistical processing of the obtained data is used as a theoretical and methodological approach, as well as the assumption that the traffic flow intensity can be represented by the sum of deterministic and stochastic components. The developed approach using the smoothing procedure allowed us to select both components, and this is a scientific novelty of the analysis performed. As a result of the study, it is shown that the deterministic component of the traffic flow intensity for working days is qualitatively different from the deterministic component for weekends. Statistical indicators of probabilistic distributions of traffic flow intensities and random components selected from them are determined. Estimates of the correspondence of the selected curves to the normal law of probability distribution are obtained using the Kolmogorov and Pearson criteria, which contradict each other. Practical significance consists in the use of a deterministic component for predicting traffic flows, controlling the operation of traffic lights, monitoring the operation of equipment, as well as in the reconstruction, design and construction of roads and road objects. The direction of further research is to obtain, statistically process and generalize data on the traffic flows intensity in other parts of the road network.
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Xie, Tao, and Qinshou Huang. "Demonstration and analysis of urban bridge reconstruction under the concept of sustainable development." Advances in Engineering Technology Research 3, no. 1 (November 7, 2022): 15. http://dx.doi.org/10.56028/aetr.3.1.15.
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Urban bridge across the river as an important urban traffic arteries, the completion of the bridge changes the traffic conditions, industrial layout and urban planning, and provides opportunities and development power for economic development. With the rapid growth of urban expansion and traffic volume, the contradiction between the demand of urban road traffic flow and the carrying capacity of road network is becoming increasingly prominent, and the capacity of some Bridges across the river can not meet the growing travel needs of the people, becoming the traffic bottleneck of urban traffic. This paper takes the reconstruction project of Longgang Bridge in Nanning city as an example, insists on the concept of sustainable development, demonstrates and analyzes the necessity of construction combined with the characteristics of the project, discusses the methods and technical points of sustainable demonstration analysis, and provides technical guidance for the reconstruction of urban bridge across the river.
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Li, Na, Ze Wu, and Zhongbiao Zhao. "Application of Internet of Things Compressed Sensing and Information Interaction Technology in Intelligent Transportation Layout." Scientific Programming 2021 (April 23, 2021): 1–11. http://dx.doi.org/10.1155/2021/9979198.
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With the continuous development of economy, the number of motor vehicles has increased sharply, and urban traffic congestion has become more and more frequent. Urban traffic congestion has become one of the important reasons that hinder the continuous development of major cities. In order to better solve the problem of urban traffic congestion, it is more urgent to build urban intelligent transportation system. The technology related to Internet of things is becoming more and more mature, which has become a new idea of collecting traffic information, providing convenience for traffic, and injecting vitality into the study of intelligent transportation layout. The traffic system in the city or the main traffic intersection, through the traffic guidance layout road, provides the traffic instruction for the passenger on the highway network, enables the driver to choose the suitable driving road, can provide the travel guidance service for the driver, adjusts the flow distribution, and improves the traffic condition. This paper describes the research status of intelligent transportation layout and uses the basic theory of compressed sensing and information interaction technology to carry out data fusion and reconstruction algorithm in intelligent transportation. Finally, the application of compressed sensing in intelligent transportation layout is summarized to realize data collection in intelligent transportation network. In this paper, compressed sensing theory is applied to the data acquisition of intelligent transportation network to reduce the amount of data and improve the effective data acquisition. The reconstruction algorithm is used to realize the data reconstruction to ensure the accuracy and stability of signal reconstruction and the accuracy of network transmission information.
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Wang, Zhanzhong, Ruijuan Chu, Minghang Zhang, Xiaochao Wang, and Siliang Luan. "An Improved Hybrid Highway Traffic Flow Prediction Model Based on Machine Learning." Sustainability 12, no. 20 (October 9, 2020): 8298. http://dx.doi.org/10.3390/su12208298.
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For intelligent transportation systems (ITSs), reliable and accurate real-time traffic flow prediction is an important step and a necessary prerequisite for alleviating traffic congestion and improving highway operation efficiency. In this paper, we propose an improved hybrid predicting model including two steps: decomposition and prediction to predict highway traffic flow. First, we adopted the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to adaptively decompose the original nonlinear, nonstationary, and complex highway traffic flow data. Then, we used the improved weighted permutation entropy (IWPE) to obtain new reconstructed components. In the prediction step, we used the gray wolf optimizer (GWO) algorithm to optimize the least-squares support vector machine (LSSVM) prediction model established for each reconstruction component and integrate the prediction results of each subsequence to obtain the final prediction result. We experimentally validated the effectiveness of the proposed approach. The research results reveal that the proposed model is useful for predicting traffic flow and its changing trends and also allowing transportation officials to make more effective traffic decisions.
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Zhou, Jingmei, Hui Chang, Xin Cheng, and Xiangmo Zhao. "A Multiscale and High-Precision LSTM-GASVR Short-Term Traffic Flow Prediction Model." Complexity 2020 (June 17, 2020): 1–17. http://dx.doi.org/10.1155/2020/1434080.
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Short-term traffic flow has the characteristics of complex, changeable, strong timeliness, and so on. So the traditional prediction algorithm is difficult to meet its high real-time and accuracy requirements. In this paper, a multiscale and high-precision LSTM-GASVR short-term traffic flow prediction algorithm is proposed. This method uses 15 min traffic flow data of the first 16 sections as input and completes the data preprocessing operation through reconstruction, normalization, and rising dimension by working day factor; establishing the prediction model based on the long- and short-term memory network (LSTM) and inverse normalization; and proposing the GA-SVR model to optimize the prediction results, so as to realize the real-time high-precision prediction of traffic flow. The prediction experiment is carried out according to the charge data of a toll station in Xi’an, Shaanxi Province, from May 2018 to May 2019. The comparison and analysis of various algorithms show that the prediction algorithm proposed in this paper is 20% higher than the LSTM, GRU, CNN, SAE, ARIMA, and SVR, and the R2 can reach 0.982, the explanatory variance is 0.982, and the MAPE is 0.118. The proposed traffic flow prediction algorithm provides strong support for traffic managers to judge the state of the road network to control traffic and guide traffic flow.
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Sivilevičius, Henrikas, Gražvydas Paliulis, Antanas Klibavičius, and Vytautas Palevičius. "Research on the operation of roundabouts based on the sustainable development principles." Baltic Journal of Road and Bridge Engineering 10, no. 2 (June 25, 2015): 166–73. http://dx.doi.org/10.3846/bjrbe.2015.21.
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The main elements of urban transport infrastructure include: the street network with intersections, bridges, viaducts, flyovers, vehicular traffic tunnels. Unsignalized four-leg intersections and roundabouts is the largest “saturated transport flow” of the street network. Roundabouts which were designed as far back as 1970–1980 were well-functioning when the car ownership level was 180–200 veh/1000 inhabitants. Currently, when the level of car ownership comes to 520–560 veh/1000 inhabitants, unsignalized roundabouts operate in the “oversaturated flows” regime. The research results of the Vilnius Gediminas Technical University and other universities showed that when designing new or reconstructing existing intersections the indicators of territorial planning, transport planning, environmental protection, traffic safety should be considered. As a common indicator for assessing all other indicators a monetization (estimation in monetary terms) should be used. When preparing projects for intersection reconstruction it is recommended to apply a new method of intersection analysis and evaluation based on the principles of sustainable development of urban transport infrastructure.
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Liu, X., Y. Zhang, and Q. Li. "AUTOMATIC PEDESTRIAN CROSSING DETECTION AND IMPAIRMENT ANALYSIS BASED ON MOBILE MAPPING SYSTEM." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-2/W4 (September 13, 2017): 251–58. http://dx.doi.org/10.5194/isprs-annals-iv-2-w4-251-2017.
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Pedestrian crossing, as an important part of transportation infrastructures, serves to secure pedestrians’ lives and possessions and keep traffic flow in order. As a prominent feature in the street scene, detection of pedestrian crossing contributes to 3D road marking reconstruction and diminishing the adverse impact of outliers in 3D street scene reconstruction. Since pedestrian crossing is subject to wearing and tearing from heavy traffic flow, it is of great imperative to monitor its status quo. On this account, an approach of automatic pedestrian crossing detection using images from vehicle-based Mobile Mapping System is put forward and its defilement and impairment are analyzed in this paper. Firstly, pedestrian crossing classifier is trained with low recall rate. Then initial detections are refined by utilizing projection filtering, contour information analysis, and monocular vision. Finally, a pedestrian crossing detection and analysis system with high recall rate, precision and robustness will be achieved. This system works for pedestrian crossing detection under different situations and light conditions. It can recognize defiled and impaired crossings automatically in the meanwhile, which facilitates monitoring and maintenance of traffic facilities, so as to reduce potential traffic safety problems and secure lives and property.
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Qin, Zhong, Guang Ting Su, Dong Hui Li, Yu Mei, Yi Wei He, and Qing Sen Chen. "Vehicle Segmentation Approach Based on the Space-Time and Self-Similarity of Background." Advanced Materials Research 179-180 (January 2011): 115–21. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.115.
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Vehicle segmentation is the key of the traffic video image analysis, the movement state of vehicles can be understudied to get traffic flow parameters. Based on the traffic scene characterized by gradual change, the paper present the background model reconstruction with time-space and self-similarity of background. Video frame image is differenced from background, and binary to get the vehicle template. According to the template, movement vehicle will be segmented from it, the results of the experiment confirmed the effectiveness of algorithm.
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Lu, Liang Fu, Zheng-Hai Huang, Mohammed A. Ambusaidi, and Kui-Xiang Gou. "A Large-Scale Network Data Analysis via Sparse and Low Rank Reconstruction." Discrete Dynamics in Nature and Society 2014 (2014): 1–10. http://dx.doi.org/10.1155/2014/323764.
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With the rapid growth of data communications in size and complexity, the threat of malicious activities and computer crimes has increased accordingly as well. Thus, investigating efficient data processing techniques for network operation and management over large-scale network traffic is highly required. Some mathematical approaches on flow-level traffic data have been proposed due to the importance of analyzing the structure and situation of the network. Different from the state-of-the-art studies, we first propose a new decomposition model based on accelerated proximal gradient method for packet-level traffic data. In addition, we present the iterative scheme of the algorithm for network anomaly detection problem, which is termed as NAD-APG. Based on the approach, we carry out the intrusion detection for packet-level network traffic data no matter whether it is polluted by noise or not. Finally, we design a prototype system for network anomalies detection such as Probe and R2L attacks. The experiments have shown that our approach is effective in revealing the patterns of network traffic data and detecting attacks from large-scale network traffic. Moreover, the experiments have demonstrated the robustness of the algorithm as well even when the network traffic is polluted by the large volume anomalies and noise.
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Růžička, J., J. Kruntorád, and R. Rek. "Microsimulation of pedestrian conflict with other transport modes." IOP Conference Series: Earth and Environmental Science 900, no. 1 (November 1, 2021): 012038. http://dx.doi.org/10.1088/1755-1315/900/1/012038.
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Abstract An effective solution of the conflict points of different transport modes is a significant issue at the urban level today. With the permanent increase of traffic in cities, it is necessary to look for suitable and sustainable transport solutions to these situations, so that the traffic flow is smooth and the transport remains safe, ecological and economical. The paper deals with the design of a simple decision-making tool for selecting the solution of pedestrian conflict with other traffic modes (classical pedestrian crossing, controlled pedestrian crossing for defined pedestrian and vehicle flows, based on experimental microsimulation. High pedestrian flows their dependence on the delay time of road users are not properly implemented in Czech legislation. The results are verified in a case study of conflict solution within the reconstruction of a public transport terminal in Prague.
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Li, Dazhou, Chuan Lin, Wei Gao, Zeying Chen, Zeshen Wang, and Guangqi Liu. "Capsules TCN Network for Urban Computing and Intelligence in Urban Traffic Prediction." Wireless Communications and Mobile Computing 2020 (June 4, 2020): 1–15. http://dx.doi.org/10.1155/2020/6896579.
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Predicting urban traffic is of great importance to smart city systems and public security; however, it is a very challenging task because of several dynamic and complex factors, such as patterns of urban geographical location, weather, seasons, and holidays. To tackle these challenges, we are stimulated by the deep-learning method proposed to unlock the power of knowledge from urban computing and proposed a deep-learning model based on neural network, entitled Capsules TCN Network, to predict the traffic flow in local areas of the city at once. Capsules TCN Network employs a Capsules Network and Temporal Convolutional Network as the basic unit to learn the spatial dependence, time dependence, and external factors of traffic flow prediction. In specific, we consider some particular scenarios that require accurate traffic flow prediction (e.g., smart transportation, business circle analysis, and traffic flow assessment) and propose a GAN-based superresolution reconstruction model. Extensive experiments were conducted based on real-world datasets to demonstrate the superiority of Capsules TCN Network beyond several state-of-the-art methods. Compared with HA, ARIMA, RNN, and LSTM classic methods, respectively, the method proposed in the paper achieved better results in the experimental verification.
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Han, Fengqing. "Short-term Traffic Flow Prediction Based on Multivariable Phase Space Reconstruction and LSSVM." Journal of Information and Computational Science 11, no. 9 (June 10, 2014): 3209–17. http://dx.doi.org/10.12733/jics20103884.
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Bąkowski, Andrzej, and Leszek Radziszewski. "Measurements of urban traffic parameters before and after road reconstruction." Open Engineering 11, no. 1 (January 1, 2021): 365–76. http://dx.doi.org/10.1515/eng-2021-0035.
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Abstract The study analyzed the parameters of vehicle traffic and noise on the national road in the section in the city from 2011 to 2016. In 2013–2014 this road was reconstructed. It was found that in most cases, the distribution of the tested variable was not normal. The median and selected percentiles of vehicle traffic parameters and noise were examined. The variability and type A uncertainty of the results were described and evaluated. The results obtained for the data recorded on working and non-working days were compared. The vehicle cumulative speed distributions, for two-way four-lane road segments in both directions were analyzed. A mathematical model of normalized traffic flow has been proposed. Fit factor R2 of the proposed equations to the experimental data for passenger vehicles ranges from 0.93 to 0.99. It has been shown that two years after the road reconstruction, the median noise level did not increase even though traffic volumes and vehicle speeds increased. The Cnossos noise model was validated for data recorded over a period of 6 years. A very good agreement of the medians determined according to the Cnossos-EU model and the measured ones was obtained. It should be noted, however, that for the other analyzed percentiles, e.g. 95%, the discrepancies are larger.
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Cheng, Shuang, Shun Ying Zhu, Hong Wang, and Bing Liu. "The Rational Construction Length Alternately by Subsections of the Reconstruction Project of Freeway." Applied Mechanics and Materials 178-181 (May 2012): 1433–37. http://dx.doi.org/10.4028/www.scientific.net/amm.178-181.1433.
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In order to ensure the quality of the reconstruction project of freeway as well as the traffic efficiency without breaking down the traffic, it is crucially important to arrange the work zone length alternately by sub-ways and subsections. The simulation method of transportation is adopted for Microscopic of the transportation situation under various length of work zone. Based on the results of the simulation, the stability, efficiency and safety of traffic flow are evaluated and analyzed comprehensively. The balance point among these three factors is determinated by the method of fuzzy comprehensive evaluation. Combined with the project cost, construction cost saving and other factors. to get the reasonable construction segment length reference value. Finally, G4 freeway (Beijing to Hong Kong and Macao) is selected as a case study.
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Zhang, Zhaoyue, An Zhang, Cong Sun, Shuaida Xiang, and Shanmei Li. "Data-Driven Analysis of the Chaotic Characteristics of Air Traffic Flow." Journal of Advanced Transportation 2020 (September 18, 2020): 1–17. http://dx.doi.org/10.1155/2020/8830731.
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Understanding the chaos of air traffic flow is significant to the achievement of advanced air traffic management, and trajectory data are the basic material for studying the chaotic characteristics. However, at present, there are two main obstacles to this task, namely, large amounts of noise in the measured data and the tedium of existing data processing methods. This paper improves the incorrect trajectory processing method based on ADS-B trajectory data and proposes a method by which to quickly extract the traffic flow through a certain waypoint. Currently, the commonly used theoretical analysis tools for nonlinear complex systems include the classical nonlinear dynamics analysis method and the newly developed complex network-based analysis method. The latter is currently in an exploratory stage because it has just been introduced into the study of air traffic flow. From these two perspectives, the chaotic characteristics of air traffic flow are studied in the present work. From the perspective of nonlinear dynamics, the improved C-C method is used to calculate the reliability parameters, namely, the time delay τ and embedding dimension m, of phase-space reconstruction, and the maximum Lyapunov index is calculated by using the small data volume method to prove the existence of chaos in the system. From the perspective of complex networks, the construction of a visibility graph and horizontal visibility graph is used to prove the existence of chaos in the system, and the goodness-of-fit parameters of the degree distributions of two fitting methods under different time scales are evaluated, which provides support for the air traffic flow theory.
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Yuan, Sheng, Yuting Chen, Huihui Huo, and Li Zhu. "Analysis and Synthesis of Traffic Scenes from Road Image Sequences." Sensors 20, no. 23 (December 4, 2020): 6939. http://dx.doi.org/10.3390/s20236939.
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Traffic scene construction and simulation has been a hot topic in the community of intelligent transportation systems. In this paper, we propose a novel framework for the analysis and synthesis of traffic elements from road image sequences. The proposed framework is composed of three stages: traffic elements detection, road scene inpainting, and road scene reconstruction. First, a new bidirectional single shot multi-box detector (BiSSD) method is designed with a global context attention mechanism for traffic elements detection. After the detection of traffic elements, an unsupervised CycleGAN is applied to inpaint the occlusion regions with optical flow. The high-quality inpainting images are then obtained by the proposed image inpainting algorithm. Finally, a traffic scene simulation method is developed by integrating the foreground and background elements of traffic scenes. The extensive experiments and comparisons demonstrate the effectiveness of the proposed framework.
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Kumar, Ashutosh, Takehiro Kashiyama, Hiroya Maeda, Hiroshi Omata, and Yoshihide Sekimoto. "Real-time citywide reconstruction of traffic flow from moving cameras on lightweight edge devices." ISPRS Journal of Photogrammetry and Remote Sensing 192 (October 2022): 115–29. http://dx.doi.org/10.1016/j.isprsjprs.2022.07.022.
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Wang, Shuying, Yinong Zhang, En Fu, and Shaohu Tang. "Multiscale Backcast Convolution Neural Network for Traffic Flow Prediction in The Frequency Domain." Applied Sciences 12, no. 23 (November 22, 2022): 11912. http://dx.doi.org/10.3390/app122311912.
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With the construction of intelligent transportation systems in recent years, intelligent methods for the prediction of traffic flow are becoming more and more important, and accurate prediction plays a key role in enabling downstream scheduling algorithms. However, the accuracy of most current forecasting algorithms remains unsatisfactory. Because traffic depends on the time of the day and varies throughout the week, such as during peak commuting periods as opposed to other times, traffic flow data show evident cyclical patterns. We capitalize on this notion and propose a multiscale convolutional feedback network for frequency prediction based on frequency angle. We combine multiscale convolution (MSC) with dilated convolution, and increase the convolutional receptive field by expanding cavity size while retaining similar parameterization costs, and achieve multiscale convolution with kernels referring to different receptive fields. At the same time, we incorporate an autoencoding module by assigning the same set of hidden features to input reconstruction and output prediction, which results in enhanced stability of features within the hidden layers. When we tested our approach on the Traffic dataset, our model achieved the best performance as assessed via the three indicators measured using mean squared error (MSE), mean absolute error (MAE), and correlation coefficient (CORR), with improvements of 3.818%, 2.472% and, 0.1515%, respectively.
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Jandacka, Dusan, Martin Decky, Katarina Hodasova, Peter Pisca, and Dusan Briliak. "Influence of the Urban Intersection Reconstruction on the Reduction of Road Traffic Noise Pollution." Applied Sciences 12, no. 17 (September 4, 2022): 8878. http://dx.doi.org/10.3390/app12178878.
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The authors present the unsolved issue of the contextual design of urban intersections (UI) from the point of view of traffic noise emissions around residential buildings in the Slovak context. Noise barriers are very rarely used in urban areas, due to such reasons as lack of space for their installation, traffic safety (view at intersections), architectural factors, as well as the fact that they represent a barrier for pedestrians and cyclists. The use of low-noise asphalt pavements is limited in urban areas primarily due to the high cost of production and maintenance of these covers, their limited durability in a colder climate, and lower efficiency compared to the roads outside urban areas. From this aspect of urban intersection design, the authors focused on the identification of individual factors associated with the significant reduction (2–8 dB) of traffic noise induced by the reconstruction of non-signalized urban intersections into roundabouts. The authors draw attention to the fact that both new surfaces of intersection branches and the change in traffic processes contribute to the aforementioned reduction. This finding was obtained by comparing direct measurements of noise levels and the results of their predicted values using validated 3D models in the CadnaA program. Noise emissions were measured by a noise analyzer (NOR-1210), and traffic noise emissions were predicted by the “Nouvelle Méthode de Prévision du Bruit” methodology (NMPB-1996). Based on the analysis of the measured and predicted traffic noise levels, the authors objectivized the share of reduction in traffic processes in the 2–3 dB range. The authors objectified the share of traffic noise reduction due to the change in traffic processes as being in the 2.2–3.3 dB range. The presented research results could contribute to a quantifiable reduction in the noise load in the external environment of residential buildings using the contextual design of intersections.
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Sekimoto, Yoshihide, Atsuto Watanabe, Toshikazu Nakamura, Hiroshi Kanasugi, and Tomotaka Usui. "Combination of spatio-temporal correction methods using traffic survey data for reconstruction of people flow." Pervasive and Mobile Computing 9, no. 5 (October 2013): 629–42. http://dx.doi.org/10.1016/j.pmcj.2012.10.005.
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Bhavani, Yerram, Vinjamuri Janaki, and Rangu Sridevi. "IP Traceback using Flow Based Classification." Recent Advances in Computer Science and Communications 13, no. 3 (August 12, 2020): 482–90. http://dx.doi.org/10.2174/2213275912666190328200635.
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Background:Distributed Denial of Service (DDoS) attack is a major threat over the internet. The IP traceback mechanism defends against DDoS attacks by tracing the path traversed by attack packets. The existing traceback techniques proposed till now are found with few short comings. The victim required many number of packets to trace the attack path. The requirement of a large number of packets resulted in more number of combinations and more false positives.Methods:To generate a unique value for the IP address of the routers in the attack path Chinese Remainder theorem is applied. This helped in combining the exact parts of the IP address at the victim. We also applied K-Nearest Neighbor (KNN) algorithm to classify the packets depending on their traffic flow, this reduced the number of packets to reconstruct the attack path.Results:The proposed approach is compared with the existing approaches and the results demonstrated that the attack graph is effectively constructed with higher precision and lower combination overhead under large scale DDoS attacks. In this approach, packets from diverse flows are separated as per flow information by applying KNN algorithm. Hence, the reconstruction procedure could be applied on each group separately to construct the multiple attack paths. This results in reconstruction of the complete attack graph with fewer combinations and false positive rate.Conclusion:In case of DDoS attacks the reconstruction of the attack path plays a major role in revealing IP addresses of the participated routers without false positives and false negatives. Our algorithm FRS enhances the feasibility of information pertaining to even the farthest routers by incorporating a flag condition while marking the packets. The rate of false positives and false negatives are drastically reduced by the application of Chinese Remainder Theorem on the IP addresses of the router. At the victim, the application of KNN algorithm reduced the combination overhead and the computation cost enormously.
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Al-Mansour, Abdullah, Kang-Won Wayne Lee, and Abdulraaof H. Al-Qaili. "Prediction of Pavement Maintenance Performance Using an Expert System." Applied Sciences 12, no. 10 (May 10, 2022): 4802. http://dx.doi.org/10.3390/app12104802.
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The pavement experiences deterioration due to traffic and environment, i.e., unsatisfactory riding quality and structural inadequacy, over time. Thus, predicting pavement performance over time is one of the key elements of any pavement maintenance management system (PMMS). It can be used as an efficient tool to program/schedule the maintenance applications and expenditures, and thus the necessary funds can be allocated. Using a combination of independent variables for any selected pavement section can generate section-wise condition assessment and prediction models. Moreover, these models can be used to select the most cost-effective maintenance alternative to be applied to that pavement section. The present study developed an expert system based on pavement performance models which combines the available maintenance data with the knowledge acquired from the experts of the General Administration of Operation and Maintenance in Riyadh, Saudi Arabia. Eight regression models were first developed for four maintenance and rehabilitation (M&R) strategies, i.e., no maintenance, routine maintenance, overlay, and reconstruction for low and high traffic. Then, a practical expert system was developed to aid pavement maintenance engineers in finding the most effective and efficient M&R strategies and suitable time for the application. The regression models revealed that the effect of routine maintenance and reconstruction is greater in low traffic than in high traffic, while the effect of overlay is greater in high traffic than in low traffic. Based on this initial system, another improved one can be developed using the machine learning technique.
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Wang, Xiaoyuan, Jianqiang Wang, Zhenxue Liu, Yaqi Liu, and Jingheng Wang. "Study on Reverse Reconstruction Method of Vehicle Group Situation in Urban Road Network Based on Driver-Vehicle Feature Evolution." Mathematical Problems in Engineering 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/1615691.
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Vehicle group situation is the status and situation of dynamic permutation which is composed of target vehicle and neighboring traffic entities. It is a concept which is frequently involved in the research of traffic flow theory, especially the active vehicle security. Studying vehicle group situation in depth is of great significance for traffic safety. Three-lane condition was taken as an example; the characteristics of target vehicle and its neighboring vehicles were synthetically considered to restructure the vehicle group situation in this paper. The Gamma distribution theory was used to identify the vehicle group situation when target vehicle arrived at the end of the study area. From the perspective of driver-vehicle feature evolution, the reverse reconstruction method of vehicle group situation in the urban road network was proposed. Results of actual driving, virtual driving, and simulation experiments showed that the model established in this paper was reasonable and feasible.
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Shao, Yang, Zhongbin Luo, Huan Wu, Xueyan Han, Binghong Pan, Shangru Liu, and Christian G. Claudel. "Evaluation of Two Improved Schemes at Non-Aligned Intersections Affected by a Work Zone with an Entropy Method." Sustainability 12, no. 14 (July 8, 2020): 5494. http://dx.doi.org/10.3390/su12145494.
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The impact of work zones on traffic is a common problem encountered in traffic management. The reconstruction of roads is inevitable, and it is necessary and urgent to reduce the impact of the work zone on the operation of traffic. There are many existing research results on the influence of highway work zones, including management strategies, traffic flow control strategies, and various corresponding model theories. There are also many research results on the impacts of urban road and subway construction on traffic operation, including construction efficiency, economic impact, and travel matrix. However, there are few studies concerning the choice of work zone location, and most previous studies have assumed that the work zone choice was scientific and reasonable. Therefore, it is reasonable to choose the location of the work zone and to assess whether there is room for improvement in the road form of the work zone, but this remains a research gap. Therefore, we studied a seven-lane main road T-intersection in Xi’an, China, and investigated a work zone located at this intersection that caused a road offset, leading to the non-aligned flow of main traffic. We designed two road improvement schemes and multiple transition schemes, used VISSIM software to evaluate the traffic operation of the two schemes, and used the entropy method to choose the suitability of the two schemes under different conditions. According to the results, in the best case, the driving time, delay, and number of stops are reduced by 44%, 66%, and 92%.
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Niu, Wei-Na, Jiao Xie, Xiao-Song Zhang, Chong Wang, Xin-Qiang Li, Rui-Dong Chen, and Xiao-Lei Liu. "HTTP-Based APT Malware Infection Detection Using URL Correlation Analysis." Security and Communication Networks 2021 (April 7, 2021): 1–12. http://dx.doi.org/10.1155/2021/6653386.
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APT malware exploits HTTP to establish communication with a C & C server to hide their malicious activities. Thus, HTTP-based APT malware infection can be discovered by analyzing HTTP traffic. Recent methods have been dependent on the extraction of statistical features from HTTP traffic, which is suitable for machine learning. However, the features they extract from the limited HTTP-based APT malware traffic dataset are too simple to detect APT malware with strong randomness insufficiently. In this paper, we propose an innovative approach which could uncover APT malware traffic related to data exfiltration and other suspect APT activities by analyzing the header fields of HTTP traffic. We use the Referer field in the HTTP header to construct a web request graph. Then, we optimize the web request graph by combining URL similarity and redirect reconstruction. We also use a normal uncorrelated request filter to filter the remaining unrelated legitimate requests. We have evaluated the proposed method using 1.48 GB normal HTTP flow from clickminer and 280 MB APT malware HTTP flow from Stratosphere Lab, Contagiodump, and pcapanalysis. The experimental results have shown that the URL-correlation-based APT malware traffic detection method can correctly detect 96.08% APT malware traffic, and its recall rate is 98.87%. We have also conducted experiments to compare our approach against Jiang’s method, MalHunter, and BotDet, and the experimental results have confirmed that our detection approach has a better performance, the accuracy of which reached 96.08% and the F1 value increased by more than 5%.
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Ge, Yong Gang, Qiang Zou, Jian Qiang Zhang, and Xiao Jun Guo. "Hazards on Dujiangyan-Wenchuan Highways Induced by Catastrophic Debris Flows on July 10 2013 and Prevention." Applied Mechanics and Materials 501-504 (January 2014): 2463–72. http://dx.doi.org/10.4028/www.scientific.net/amm.501-504.2463.
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After the Wenchuan Earthquake on May 12 2008, the highways from Dujiangyan to Wenchuan, a crucial passage from Chengdu to Sichuan Western Plateau and Gansu province, are always seriously endangered by landslides, debris flows and their following hazards. Hundreds of debris flows from watersheds, gullies and slopes on July 10 2013 produced fatal hazards and destruction on the Highway G213 and the Express Highway from Yingxiu to Wenchuan. The debris flows are characterized by numerous-occurrence, large flux (645~2238m3/s) and large magnitude (5~126×104m3) as well as the hazard chain process which is composed of debris flow, dammed lake and outburst flood. The highways were seriously destructed and blocked in 16 sites, which were induced by 6 collapsed bridges, 3 submerged bridges, 3 buried tunnel entrances, 1 site collapsed highway base and 7 sites buried highway base or bridges, and the traffic was completely interrupted. Based on analyzing the destruction modes of highways, it was found that the large-scale and potential debris flows and the irrational location of some sections, vulnerable protection measures and low resistant capability of highways against debris flows were responsible for huge highway destructions. Considering the active debris flows in the future at least 5~10 years, it was strongly suggested that potential debris flow identification, integrated management of disastrous watershed, dangerous road line altering, increasing and strengthening protection constructions at dangerous section and improving highway reconstruction standard should be carried out for highway protection and traffic security.
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Liu, Jianguo, Ze Chen, and Huiming Zhang. "Study on Safety Guarantee Measures for the Same-direction Separated Subgrade Section." Highlights in Science, Engineering and Technology 18 (November 13, 2022): 48–51. http://dx.doi.org/10.54097/hset.v18i.2566.
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In the reconstruction and expansion of the expressway, due to factors such as resource utilization of original roads, economic applicability, adaptation to local conditions, and construction organization, the method of reconstruction and expansion and widening is restricted. The widening method of half-width utilization of the original road and half-width newly built in the same direction is adopted, forming the separated subgrade section in the same direction. Based on the implementation plans of different expressway reconstruction and expansion projects, this paper analyzes and studies the characteristics of the same-direction separated subgrade section, from the aspects of the safety guarantee principle, the design of safety facilities, and the same-direction separated subgrade section with interchanges. The reasonable measures safety protection was proposed to promote the smooth flow of expressway traffic and improve driving safety. It is of certain guiding significance for the design of safety facilities in the same-direction separated subgrade section of expressway reconstruction and expansion projects.
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Pan, Binghong, Shangru Liu, Zhenjiang Xie, Yang Shao, Xiang Li, and Ruicheng Ge. "Evaluating Operational Features of Three Unconventional Intersections under Heavy Traffic Based on CRITIC Method." Sustainability 13, no. 8 (April 7, 2021): 4098. http://dx.doi.org/10.3390/su13084098.
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Conventional four-legged intersections are inefficient under heavy traffic requirements and are prone to congestion problems. Unconventional intersections with innovative designs allow for more efficient traffic operations and can increase the capacity of the intersection, in some cases. Common unconventional designs for four-legged intersections include the upstream signalized crossover intersection (USC), continuous flow intersection (CFI), and parallel flow intersection (PFI). At present, an increasing number of cities are using such unconventional designs to improve the performance of their intersections. In the reconstruction of original intersections or the design of new intersections, the question of how to more reasonably select the form of unconventional intersection becomes particularly critical. Therefore, we selected a typical intersection in Xi’an for optimization and investigated traffic data for this intersection. The traffic operations, with respect to the four solutions of a conventional intersection, USC, CFI, and PFI, were evaluated using the VISSIM software. Then, we evaluated the suitability of each solution under different situations using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method, which is a multi-criteria decision-making (MCDM) method that enables a more comprehensive and integrated evaluation of the four solutions by taking into account the comparative intensities and conflicting character among the indices. The results show that the conventional intersection is only applicable to the case of very low traffic volume; PFI has the advantage in the case of moderate and high traffic volume; CFI performs better in the case of high traffic volume; and USC is generally inferior to CFI and PFI, although it has greater improvement, compared with the conventional solution, in a few cases.
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Lee, Eul-Bum, Hojung Lee, and Massod Akbarian. "Accelerated Pavement Rehabilitation and Reconstruction with Long-Life Asphalt Concrete on High-Traffic Urban Highways." Transportation Research Record: Journal of the Transportation Research Board 1905, no. 1 (January 2005): 56–64. http://dx.doi.org/10.1177/0361198105190500106.
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Rehabilitation of urban highways is a critical issue confronting the California Department of Transportation because the state has a significant inventory of overaged, heavily trafficked urban highways showing extensive signs of distress. This paper presents the innovative pavement rehabilitation technologies and techniques that the agency applied in the first asphalt concrete (AC) project for its Long-Life Pavement Rehabilitation Strategies (LLPRS) program. A 4.4-km stretch of deteriorated concrete pavement on I-710 in Long Beach was rehabilitated successfully with 230 mm of AC overlay or 325 mm of full-depth AC replacement during eight 55-h weekend closures. The pilot project proved that the accelerated (fast-track) rehabilitation with 55-h weekend closures is a viable option that can drastically shorten the overall construction time and lessen the negative effects of construction in an urban area. The project also proved that AC pavement designed to provide a design life of 30-plus years can be constructed in a series of weekend closures even on the most heavily loaded truck route in the state. The construction-monitoring study indicated that contractor productivities were noticeably improved (through the learning effect) as weekend closures were repeated. In addition, the pay factor clause in the contract effectively encouraged the contractor's awareness of quality. The traffic measurements study showed that traffic operated at free-flow speeds throughout the surrounding highways and arterial roads during the construction weekends. It is expected that the construction and traffic management techniques adopted in this project will be used in LLPRS projects on California urban highways with high-traffic volumes.
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Lakušić, Stjepan, Davor Brčić, and Višnja Tkalčević Lakušić. "Analysis of Vehicle Vibrations – New Approach to Rating Pavement Condition of Urban Roads." PROMET - Traffic&Transportation 23, no. 6 (February 21, 2012): 485–94. http://dx.doi.org/10.7307/ptt.v23i6.183.
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Urban road infrastructure is daily burdened by heavy traffic volume. Pavement structure roughness observations are significantly more difficult in urban agglomerations than on roads in unpopulated areas. Roughness, expressed by IRI (International Roughness Index), directly affects the quality and safety of road traffic. Within the framework of the pavement management in relation to safety and the achievement of the best possible ride comfort, it is very important to foresee when a road should be reconstructed. The method for quality evaluations of safety and ride comfort on urban roads presented in this paper is based on vehicle vibrations measurements. In the article, measuring of vehicle vibrations was performed on the main urban roads in Zagreb (Croatia). Measurements covered roads with different pavement surface roughness. This method can be simply and very easily used in pavement management aimed at achieving road safety and better ride comfort. The results of measurements according to this method could be used by traffic and civil engineering experts as an indication for the roads that require reconstruction or maintenance. KEY WORDS: urban roads, traffic flow, safety, vehicle vibrations, road surface roughness (IRI)
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Xu, Su Ning, Dian Zhang, and Cong Cao. "City Transportation Hub Region Planning Based on the Urban Design Integrating Strategy: A Case Study of Conceptual Reform Design of the Changchun Railway Station North Area." Applied Mechanics and Materials 209-211 (October 2012): 763–66. http://dx.doi.org/10.4028/www.scientific.net/amm.209-211.763.
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Abstract. With the rapid urbanization and development in China, reconstruction and construction for the comprehensive transportation hub of the railway station area is underway in most city. The paper used the conceptual reform design of Changchun railway station north area as example, to describe that how to improve integral image and environmental quality by urban design integrating strategy. The purpose is to maximize the function of the transport hub area and to reach reasonable evacuation traffic flow, thus create a transit-oriented composite development project with dynamic and sustainable by function and space integration.
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Zu, Fajin, Chao Du, Chunpeng Han, Lanyu Xu, and Qiuying Peng. "Applicable Conditions of Room-and-Pillar Mining Goaf Treatment Methods under a Traffic Load." Applied Sciences 13, no. 3 (February 3, 2023): 2024. http://dx.doi.org/10.3390/app13032024.
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The disturbance depth of traffic load has a direct impact on the stability of a room-and-pillar mining goaf. To quantitatively calculate the relationship between the traffic load disturbance depth and influencing factors, 49 groups of horizontal combinations of different influencing parameters are designed in this study, based on the orthogonal experimental design method. Midas GTS is used to simulate and obtain the corresponding traffic load disturbance depth data. A multivariate linear regression analysis of the traffic load disturbance depth is conducted, and a regression formula for calculating the traffic load disturbance depth is established. According to the traffic load disturbance depth, goaf depth, and the stability of the roof, coal pillar, and base plate under traffic load conditions, a judgment flow of the room-and-pillar mining goaf treatment method under traffic load conditions is established, and it is applied to the reconstruction and expansion project of the Jixi section of the Dan-A national highway. The results show that a geogrid can be used for treatment purposes when the traffic load disturbance depth is 1.5 times lower than the depth of the room-and-pillar mining goaf, or when the traffic load disturbance depth is 1.5 times greater than the depth of the room-and-pillar mining goaf but the roof, coal pillar, and base plate are stable. Additionally, grouting is needed for treatment in other cases. The results of this study can provide a scientific basis for the selection of treatment methods for room-and-pillar mining goafs underlying highways in the future. The results are of great significance in the field of engineering for the safety measures concerning highway room-and-pillar mining goafs.
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Leontiev, D. N., A. V. Ihnatenko, O. V. Synkovska, L. A. Ryzhikh, N. V. Smirnova, Yu V. Aleksandrov, and N. V. Rudenko. "Fuel Consumption of Wheeled Vehicle and Transportation Costs during Highway Construction/Reconstruction." Science & Technique 20, no. 6 (December 3, 2021): 522–27. http://dx.doi.org/10.21122/2227-1031-2021-20-6-522-527.
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A method is proposed for determining the fuel consumption of a wheeled vehicle depending on its speed, road surface flatness and road slope in the longitudinal direction. The purpose of the research is to derive mathematical relationships for calculating the fuel consumption of vehicles, which is one of the transport cost factors during the construction/reconstruction or overhaul of a highway. The proposed polynomial dependencies for calculating fuel in addition to vehicle speed, road surface flatness and its longitudinal slope take into account the mass-dimensional parameters of vehicles involved in road traffic. New mathematical relationships between the speed of wheeled vehicles, road surface flatness and longitudinal road slope allow to simulate the change in the value of fuel consumption of a wheeled vehicle when the speed of traffic flow or the slope of the road surface changes in the forward or reverse direction of the vehicle. In a graphic way, the influence of the pavement slope on the value of fuel consumption, both loaded and unloaded wheeled vehicle is presented. When determining transport costs associated with the highway construction, reconstruction or overhaul it is proposed to use empirical mathematical relationships, which make it possible to obtain fuel consumption with an accuracy of 5 % and save up to 15 % of budget (private) investments. The analysis of scientific publications of the existing approach determine the transport costs associated with highway construction, reconstruction or overhaul. The presented method for determining the fuel consumption of wheeled vehicles with small and large loading capacity increases the accuracy of determining transportation costs and reduces the level of financial costs for highway construction, reconstruction or overhaul.
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Kim, Young-Mok, Young-Gil Kim, Seung-Yong Son, Soo-Yeon Lim, Bong-Yeol Choi, and Doo-Hyun Choi. "Review of Recent Automated Pothole-Detection Methods." Applied Sciences 12, no. 11 (May 24, 2022): 5320. http://dx.doi.org/10.3390/app12115320.
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Potholes, a kind of road defect, can damage vehicles and negatively affect drivers’ safe driving, and in severe cases can lead to traffic accidents. Efficient and preventive management of potholes in a complex road environment plays an important role in securing driver safety. It is also expected to contribute to the prevention of traffic accidents and the smooth flow of traffic. In the past, pothole detection was mainly performed via visual inspection by human experts. Recently, automated pothole-detection methods apply various technologies that converge basic technologies such as sensors and signal processing. The automated pothole-detection methods can be classified into three types according to the technology used in the pothole-recognition process: a vision-based method, a vibration-based method, and a 3D reconstruction-based method. In this paper, three methods are compared, and the strengths and weaknesses of each method are summarized. The detection process and technology proposed in the latest research related to automated pothole detection are described for each method. The development plans of future technology that is connected with those studies are also presented in this paper.