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Готові списки джерел за темами / Traffic flow reconstruction

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Добірка наукової літератури з теми "Traffic flow reconstruction"

Автор: Grafiati

Опубліковано: 10 березня 2023

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Статті в журналах з теми "Traffic flow reconstruction"

1

Wilkie, David, Jason Sewall, and Ming Lin. "Flow reconstruction for data-driven traffic animation." ACM Transactions on Graphics 32, no. 4 (2013): 1–10. http://dx.doi.org/10.1145/2461912.2462021.

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2

Kriso, S., J. Peinke, R. Friedrich, and P. Wagner. "Reconstruction of dynamical equations for traffic flow." Physics Letters A 299, no. 2-3 (2002): 287–91. http://dx.doi.org/10.1016/s0375-9601(02)00288-8.

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3

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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Анотація:

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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4

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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5

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, intellig

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6

Bilotta, Stefano, and Paolo Nesi. "Estimating CO2 Emissions from IoT Traffic Flow Sensors and Reconstruction." Sensors 22, no. 9 (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 effi

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7

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 (2022): 307. http://dx.doi.org/10.3390/a15090307.

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Анотація:

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 wit

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8

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 (2002): 155–65. http://dx.doi.org/10.3141/1802-18.

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Анотація:

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 firs

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9

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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Анотація:

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 traff

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10

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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Анотація:

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 informatio

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