Relevant bibliographies by topics / Traffic flow reconstruction

Academic literature on the topic 'Traffic flow reconstruction'

Author: Grafiati
Published: 10 March 2023

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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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Dissertations / Theses on the topic "Traffic flow reconstruction"

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Soderi, Mirco. "Semantic models for the modeling and management of big data in a smart city environment." Doctoral thesis, 2021. http://hdl.handle.net/2158/1232245.

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The overall purpose of this research has been the building or the improve- ment of semantic models for the representation of data related to smart cities and smart industries, in such a way that it could also be possible to build context-rich, user-oriented, ecient and eective applications based on such data. In some more detail, one of the key purposes has been the modelling of structural and the functioning aspects of the urban mobility and the produc- tion of instances exploiting the Open Street Map, that once integrated with trac sensors data, it has lead to the building and displaying of real-time trac reconstructions at a city level. One second key purpose has been the modelling of the Internet of Things, that allows today to seamlessy and e- ciently identify sensing devices that are deployed in a given area or along a given path and that are of a given type, and also inspect real-time data that they produce, through a user-oriented Web application, namely the Service Map. A pragmatic approach to the modelling has been followed, always tak- ing into consideration the best practices of semantic modelling on one side for that a clean, comprehensive and understandable model could result, and the reality of the data at our hands and of the applicative requirements on the other side. As said, the identication of architectures and methods that could grant eciency and scalability in data access has also been a primary purpose of this research that has led to the denition and implementation of a federation of Service Maps, namely the Super Service Map. The archi- tecture is fully distributed: each Super Service Map has a local list of the actual Service Maps with relevant metadata, it exposes the same interface as actual Service Maps, it forwards requests and builds merged responses, also implementing security and caching mechanisms. As said, the identica- tion of technologies, tools, methods, for presenting the data in a user-friendly manner is also has been a relevant part of this research, and it has led among the other to the denition and implementation of a client-server architecture and a Web interface in the Snap4City platform for the building, manage- ment, and displaying of synoptic templates and instances thanks to which users can securely display and iteract with dierent types of data. In end, some eort has been made for the automatic classication of RDF datasets as for their structures and purposes, based on the computation of metrics through SPARQL queries and on the application of dimensionality reduc- tion and clustering techniques. A Web portal is available where directories, datasets, metrics, and computations can be inspected even at real-time.
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Books on the topic "Traffic flow reconstruction"

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Steffens, William T. Corridor transportation management for highway reconstruction: Southeast Expressway, Massachusetts, 1984-1985. [Washington, D.C.?]: U.S. Dept. of Transportation, Office of the Secretary of Transportation, 1986.

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United States. Federal Highway Administration., ed. Intelligent transportation systems in work zones: A case study : work zone travel time system : reducing congestion with the use of a traffic management contract incentive during the reconstruction of Arizona State Route 68. [Washington, DC: U.S. Dept. of Transportation, Federal Highway Administration, 2004.

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United States. Federal Highway Administration., ed. Intelligent transportation systems in work zones: A case study : real-time work zone traffic control system : using an automated traffic information system to reduce congestion and improve safety during reconstruction of the I-55 Lake Springfield Bridge in Illinois. [Washington, DC: U.S. Dept. of Transportation, Federal Highway Administration, 2004.

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Travel impacts of urban freeway reconstruction projects in Texas. College Station, TX: Texas Transportation Institute, Texas A&M University System, 1990.

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Inc, Frederic R. Harris. A report on the proposed reconstruction of Washington street, Jamaica Plain (forest hills to egleston square). 1988.

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Book chapters on the topic "Traffic flow reconstruction"

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Treiber, Martin, and Arne Kesting. "Spatiotemporal Reconstruction of the Traffic State." In Traffic Flow Dynamics, 37–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32460-4_5.

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Lubashevsky, Ihor, Namik Gusein-zade, Dmitry Klochkov, and Sergey Zuev. "Three-Frame Algorithm of Car Path Reconstruction from Airborne Traffic Data." In Traffic and Granular Flow ’07, 373–79. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-77074-9_39.

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Herty, Michael, Andrea Tosin, Giuseppe Visconti, and Mattia Zanella. "Reconstruction of Traffic Speed Distributions from Kinetic Models with Uncertainties." In Mathematical Descriptions of Traffic Flow: Micro, Macro and Kinetic Models, 1–16. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66560-9_1.

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del Arco, Eduardo, Mihaela I. Chidean, Inmaculada Mora-Jiménez, Samer H. Hamdar, and Antonio J. Caamaño. "A Geostatistical Approach to Traffic Flow Reconstruction from Sparse Floating-Car Data." In Springer Proceedings in Physics, 441–47. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-55973-1_54.

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Zhang, Jiaming, Baotian Dong, and Pengcheng Li. "Vehicle Track Reconstruction and Section Speed Evaluation Based on Low-Frequency Data." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220059.

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Considering the cost considerations and data maintenance ability in the traffic field, traffic managers only collect only the traffic data of a few important road network nodes and road network sections, and the relevant data of the missing secondary sections will affect the fine research of the road network. So this paper proposes a method based on multiple source data, which firstly performs the vehicle trajectory reconstruction and location positioning with the idea of minimum cost and maximum flow, and then calculates the average speed based on this, and all the results are tested by actual data. The experimental results show that the trajectory reconstruction algorithm is about 90% efficient, and the road average speed estimation result meets the actual situation.
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Zhao, Haibin. "Comparison and Selection of Pedestrian Simulation Schemes for Reconstruction of Transfer Channel at Zhichun Road Station of Beijing Metro." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220058.

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As the most critical facility of rail transit transfer station, the carrying capacity and efficiency of transfer channel greatly affect the operation efficiency and safety of transfer station. Different from the entrance and exit channels of the station, on the one hand, the transfer channel is impacted by the pulse large passenger flow with the departure interval as the cycle, and the load is large; on the other hand, due to the relatively narrow passenger circulation space, it is impossible to take operation and management measures such as flow restriction. Once serious congestion occurs, it is very easy to cause safety accidents. Due to the uncoordinated line network planning and inaccurate passenger flow prediction, the capacity of transfer channels in some transfer stations at peak hours is seriously insufficient, so they have to be rebuilt at a great cost. In the past, the comparison and selection of channel reconstruction schemes often used static calculation and qualitative analysis, which was lack of comprehensiveness. Based on the reconstruction project of transfer channel of Zhichun Road Station of Beijing Metro, this paper uses computer pedestrian simulation tools to establish three-dimensional simulation environments of different schemes, evaluates the traffic efficiency of different schemes, and recommends the better scheme in this paper in combination with the difficulty of project implementation.
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Conference papers on the topic "Traffic flow reconstruction"

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Bellini, Pierfrancesco, Stefano Bilotta, Paolo Nesi, Michela Paolucci, and Mirco Soderi. "WiP: Traffic Flow Reconstruction from Scattered Data." In 2018 IEEE International Conference on Smart Computing (SMARTCOMP). IEEE, 2018. http://dx.doi.org/10.1109/smartcomp.2018.00052.

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Custers, Bram, Wouter Meulemans, Bettina Speckmann, and Kevin Verbeek. "Route Reconstruction from Traffic Flow via Representative Trajectories." In SIGSPATIAL '21: 29th International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3474717.3483650.

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Feng, Xiangwen, Song Xu, and Xuefeng Yan. "Traffic Flow Signal based Traffic Event Reconstruction using Sequential Monte Carlo Methods." In 2015 International Industrial Informatics and Computer Engineering Conference. Paris, France: Atlantis Press, 2015. http://dx.doi.org/10.2991/iiicec-15.2015.385.

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Tian, Guo, and Takuya Akashi. "Sensing of Traffic Flow in Real-World for Reconstruction in VR." In 2013 International Conference on Cyberworlds (CW). IEEE, 2013. http://dx.doi.org/10.1109/cw.2013.32.

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Kumar, Ashutosh, Takehiro Kashiyama, Hiroya Maeda, and Yoshihide Sekimoto. "Citywide reconstruction of cross-sectional traffic flow from moving camera videos." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671751.

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Guo, Wei, Qi Wang, Zhiheng Li, Jiyuan Tan, Yi Zhang, Li Li, and Zuo Zhang. "Compare linear interpolation and adaptive smoothing methods on traffic flow information reconstruction." In 2012 15th International IEEE Conference on Intelligent Transportation Systems - (ITSC 2012). IEEE, 2012. http://dx.doi.org/10.1109/itsc.2012.6338618.

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Ladino, Andres, Carlos Canudas-de-Wit, Alain Kibangou, Hassen Fourati, and Martin Rodriguez. "Density and flow reconstruction in urban traffic networks using heterogeneous data sources." In 2018 17th European Control Conference (ECC). IEEE, 2018. http://dx.doi.org/10.23919/ecc.2018.8550267.

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Jing, Xiao, Yun Yue, Zi Yang, Shan Tian, Peng Weng, and Xin Pei. "Trajectory Reconstruction and Inter-City Traffic Flow Analysis Based on Mobile Phone Data." In 21st COTA International Conference of Transportation Professionals. Reston, VA: American Society of Civil Engineers, 2021. http://dx.doi.org/10.1061/9780784483565.144.

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Zhuo, Hui, Limin Jia, Guoqiang Cai, and Dongmei Liu. "The Improved Forecasting Model for Short-Term Traffic Flow Based on Phase Space Reconstruction." In Second International Conference on Transportation Engineering. Reston, VA: American Society of Civil Engineers, 2009. http://dx.doi.org/10.1061/41039(345)534.

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Rodriguez-Vega, Martin, Carlos Canudas-de-Wit, and Hassen Fourati. "Joint Flow and Density Reconstruction in Large Traffic Networks Using Partial Turning Ratio Information." In 2018 IEEE Conference on Decision and Control (CDC). IEEE, 2018. http://dx.doi.org/10.1109/cdc.2018.8619321.

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