Relevant bibliographies by topics / Traffic flow Traffic engineering

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Traffic flow Traffic engineering'

Author: Grafiati
Published: 4 June 2021
Last updated: 12 February 2022

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Journal articles on the topic "Traffic flow Traffic engineering"

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Milius, Susan. "Ant Traffic Flow." Science News 162, no. 25/26 (2002): 388. http://dx.doi.org/10.2307/4013963.

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Zhang, H. M., and W. L. Jin. "Kinematic Wave Traffic Flow Model for Mixed Traffic." Transportation Research Record: Journal of the Transportation Research Board 1802, no. 1 (2002): 197–204. http://dx.doi.org/10.3141/1802-22.

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The Lighthill-Whitham-Richards kinematic wave traffic flow model was extended to describe traffic with different types of vehicles, in which all types of vehicles are completely mixed and travel at the same group velocity. A study of such a model with two vehicle classes (e.g., passenger cars and trucks) showed that when both classes of traffic have identical freeflow speeds, the model (a) satisfies the first-in-first-out rule, (b) is anisotropic, and (c) has the usual shock and expansion waves and a family of contact waves. Different compositions of vehicle classes in this model propagate along contact waves. Such models can be used to study traffic evolution on long crowded highways where low-performance vehicles entrap high-performance ones.
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KAMIYAMA, Noriaki, Yousuke TAKAHASHI, Keisuke ISHIBASHI, et al. "Effective Flow Aggregation for Traffic Engineering." IEICE Transactions on Communications E98.B, no. 10 (2015): 2049–59. http://dx.doi.org/10.1587/transcom.e98.b.2049.

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Junevičius, Raimundas, and Marijonas Bogdevičius. "DETERMINATION OF TRAFFIC FLOW PARAMETERS IN DIFFERENT TRAFFIC FLOW INTERACTION CASES." TRANSPORT 22, no. 3 (2007): 236–39. http://dx.doi.org/10.3846/16484142.2007.9638131.

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Modelling of straight road section consisting of one traffic line gives the opportunity to simulate “follow the car” system. In general it looks like a line of vehicles, going one after another. Kinetic theory, used in this paper describes traffic flow system as a straight unbroken line with limited flow speed and concentration. Such model also gives the opportunity to derive traffic lines intersections. For example, intersection could be derived like a point with traffic lines coming and outgoing from this point by only changing boundary conditions. Mathematical model is built using characteristic method.
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Maze, Thomas H., Manish Agarwal, and Garrett Burchett. "Whether Weather Matters to Traffic Demand, Traffic Safety, and Traffic Operations and Flow." Transportation Research Record: Journal of the Transportation Research Board 1948, no. 1 (2006): 170–76. http://dx.doi.org/10.1177/0361198106194800119.

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Zefreh, Mohammad Maghrour, and Ádám Török. "DISTRIBUTION OF TRAFFIC SPEED IN DIFFERENT TRAFFIC CONDITIONS: AN EMPIRICAL STUDY IN BUDAPEST." Transport 35, no. 1 (2020): 68–86. http://dx.doi.org/10.3846/transport.2019.11725.

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Fundamental diagram, a graphical representation of the relationship among traffic flow, speed, and density, has been the foundation of traffic flow theory and transportation engineering for many years. Underlying a fundamental diagram is the relation between traffic speed and density, which serves as the basis to understand system dynamics. Empirical observations of the traffic speed versus traffic density show a wide-scattering of traffic speeds over a certain level of density, which would form a speed distribution over a certain level of density. The main aim of the current research is to study on the distribution of traffic speed in different traffic conditions in the urban roads since the distribution of traffic speed is necessary for many traffic engineering applications including generating traffic in micro-simulation systems. To do so, the traffic stream is videotaped at various locations in the city of Budapest (Hungary). The recorded videos were analysed by traffic engineering experts and different traffic conditions were extracted from these recorded videos based on the predefined scenarios. Then their relevant speeds in that time interval were estimated with the so-called “g-estimator method” using the outputs of the available loop detectors among the videotaped locations. Then different parametric candidate distributions have been fitted to the speeds by Maximum Likelihood Estimation (MLE) method. Having fitted different parametric distributions to speed data, they were compared by three goodness-of-fit tests along with two penalized criteria (Akaike Information Criterion – AIC and Bayesian Information Criterion – BIC) in order to overcome the over-fitting problems. The results showed that the speed of traffic flow follows exponential, normal, lognormal, gamma, beta and chisquare distribution in the condition that traffic flow followed over-saturated congestion, under saturated flow, free flow, congestion, accelerated flow and decelerated flow respectively.
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Junevičius, Raimundas, and Marijonas Bogdevičius. "MATHEMATICAL MODELLING OF NETWORK TRAFFIC FLOW." TRANSPORT 24, no. 4 (2009): 333–38. http://dx.doi.org/10.3846/1648-4142.2009.24.333-338.

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The article describes mathematical models of traffic flows to initiate different traffic flow processes. Separate elements of traffic flow models are made in a way to be connected together to get a single complex model. A model of straight road with different boundary conditions is presented as a separate part of the network traffic flow model. First testing is conducted in case the final point of the whole modelled traffic line is closed and no output from that point is possible. The second test is performed when a constant value of traffic flow speed and traffic flow rate is entered. Mathematical simulation is carried out and the obtained results are listed.
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Abdi, Ali, Hamid Bigdeli Rad, and Ehsan Azimi. "Simulation and analysis of traffic flow for traffic calming." Proceedings of the Institution of Civil Engineers - Municipal Engineer 170, no. 1 (2017): 16–28. http://dx.doi.org/10.1680/jmuen.16.00005.

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Chen, Yuanyuan, Yisheng Lv, Peijun Ye, and Fenghua Zhu. "Traffic-Condition-Awareness Ensemble Learning for Traffic Flow Prediction." IFAC-PapersOnLine 53, no. 5 (2020): 582–87. http://dx.doi.org/10.1016/j.ifacol.2021.04.146.

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Abramova, Liudmyla, Valerii Shyrin, Hennadii Ptytsia, and Serhii Kapinus. "Dynamic control over traffic flow under urban traffic conditions." Eastern-European Journal of Enterprise Technologies 4, no. 3 (106) (2020): 34–43. http://dx.doi.org/10.15587/1729-4061.2020.210170.

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Dissertations / Theses on the topic "Traffic flow Traffic engineering"

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Cappiello, Alessandra 1972. "Modeling traffic flow emissions." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/84328.

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Wall, Zach R. "Traffic management and control utilizing a microscopic model of traffic dynamics /." Thesis, Connect to this title online; UW restricted, 2007. http://hdl.handle.net/1773/5922.

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Gebresilassie, Mesele Atsbeha. "Spatio-temporal Traffic Flow Prediction." Thesis, KTH, Geoinformatik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-212323.

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The advancement in computational intelligence and computational power and the explosionof traffic data continues to drive the development and use of Intelligent TransportSystem and smart mobility applications. As one of the fundamental components of IntelligentTransport Systems, traffic flow prediction research has been advancing from theclassical statistical and time-series based techniques to data–driven methods mainly employingdata mining and machine learning algorithms. However, significant number oftraffic flow prediction studies have overlooked the impact of road network topology ontraffic flow. Thus, the main objective of this research is to show that traffic flow predictionproblems are not only affected by temporal trends of flow history, but also by roadnetwork topology by developing prediction methods in the spatio-temporal.In this study, time–series operators and data mining techniques are used by definingfive partially overlapping relative temporal offsets to capture temporal trends in sequencesof non-overlapping history windows defined on stream of historical record of traffic flowdata. To develop prediction models, two sets of modeling approaches based on LinearRegression and Support Vector Machine for Regression are proposed. In the modelingprocess, an orthogonal linear transformation of input data using Principal ComponentAnalysis is employed to avoid any potential problem of multicollinearity and dimensionalitycurse. Moreover, to incorporate the impact of road network topology in thetraffic flow of individual road segments, shortest path network–distance based distancedecay function is used to compute weights of neighboring road segment based on theprinciple of First Law of Geography. Accordingly, (a) Linear Regression on IndividualSensors (LR-IS), (b) Joint Linear Regression on Set of Sensors (JLR), (c) Joint LinearRegression on Set of Sensors with PCA (JLR-PCA) and (d) Spatially Weighted Regressionon Set of Sensors (SWR) models are proposed. To achieve robust non-linear learning,Support Vector Machine for Regression (SVMR) based models are also proposed.Thus, (a) SVMR for Individual Sensors (SVMR-IS), (b) Joint SVMR for Set of Sensors(JSVMR), (c) Joint SVMR for Set of Sensors with PCA (JSVMR-PCA) and (d) SpatiallyWeighted SVMR (SWSVMR) models are proposed. All the models are evaluatedusing the data sets from 2010 IEEE ICDM international contest acquired from TrafficSimulation Framework (TSF) developed based on the NagelSchreckenberg model.Taking the competition’s best solutions as a benchmark, even though different setsof validation data might have been used, based on k–fold cross validation method, withthe exception of SVMR-IS, all the proposed models in this study provide higher predictionaccuracy in terms of RMSE. The models that incorporated all neighboring sensorsdata into the learning process indicate the existence of potential interdependence amonginterconnected roads segments. The spatially weighted model in SVMR (SWSVMR) revealedthat road network topology has clear impact on traffic flow shown by the varyingand improved prediction accuracy of road segments that have more neighbors in a closeproximity. However, the linear regression based models have shown slightly low coefficientof determination indicating to the use of non-linear learning methods. The resultsof this study also imply that the approaches adopted for feature construction in this studyare effective, and the spatial weighting scheme designed is realistic. Hence, road networktopology is an intrinsic characteristic of traffic flow so that prediction models should takeit into consideration.
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Selman, Wassim A. "An investigation of the impact of additional traffic volumes on existing arterials." Diss., Georgia Institute of Technology, 1986. http://hdl.handle.net/1853/19055.

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Benekohal, Rahim Farahnak. "Development and validation of a car following model for simulation of traffic flow and traffic wave studies at bottlenecks /." The Ohio State University, 1986. http://rave.ohiolink.edu/etdc/view?acc_num=osu148726669109498.

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Alothaim, Abdulelah. "Improved Traffic Flow in Riyadh City by 2023." Digital Commons at Loyola Marymount University and Loyola Law School, 2013. https://digitalcommons.lmu.edu/etd/360.

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Cities around the globe try to alleviate congested traffic, especially in economically fast growing cities. In order to help maintain this economic growth, cities must make alleviating congested traffic a priority. The purpose of this paper is to study the traffic problem in Riyadh city in Saudi Arabia and propose a solution. This paper will begin with a brief background about the city and then explain the problem. Afterward, the requirements of the new system will be discussed. Next, using analysis of alternatives, different solutions will be explored and through trade study, some of those alternatives will be chosen to be a part of the new system. Systems Architecture will be used to help readers visualize the current and future systems and in the modeling section, the results of a survey regarding the traffic problem in Riyadh are taken into consideration. This will be combined with a discussion on queuing and forecasting methods. The project is looked at from a Lean point of view to try and minimize waste within traffic administrations by suggesting a new organizational chart. Finally, ethics and risk managements are discussed. The goal of the document is to explore the traffic problem in Riyadh in hopes of finding a solution.
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Yu, Tungsheng. "Traffic flow modeling in highway networks." Master's thesis, This resource online, 1992. http://scholar.lib.vt.edu/theses/available/etd-12232009-020154/.

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Misra, Rajul. "Toward a comprehensive representation and analysis framework for non-worker activity-travel pattern modeling /." Digital version accessible at:, 1999. http://wwwlib.umi.com/cr/utexas/main.

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Sheu, Hsin-Teng. "A coordinated decentralized flow and routing control algorithm for an automated highway system /." The Ohio State University, 1987. http://rave.ohiolink.edu/etdc/view?acc_num=osu148758564557836.

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Nevers, Brandon L. "A Model of Saturation Flow Using Traffic Subgroups." NCSU, 2001. http://www.lib.ncsu.edu/theses/available/etd-20010205-180834.

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<p>This thesis presents a methodology for estimating saturation flow rates at signalized intersections by traffic subgroups. A subgroup is defined as a group of vehicles of a specific vehicle classification that make a single directional movement from one lane. The subgroup method is founded on the procedures described in the 1997 Highway Capacity Manual (HCM) (Transportation Research Board, 1997) but extends beyond the HCM's lane group model to provide results that can be aggregated at multiple levels. Rather than assuming homogeneous conditions within each lane or lane group as is the case with many capacity guides, the subgroup method decomposes a traffic stream into individual components, each of which have unique saturation headways. Comparisons with the HCM show that under similar assumptions, the subgroup method produces similar saturation flow rates when aggregated at the lane group level. This gives confidence for applying the subgroup approach to estimate individual lane performance.The most critical element of the subgroup model is the estimation of lane volumes. Lane volume field data were gathered at four sites. Results of an evaluation of lane distribution strategies for estimating lane volumes when a choice is present indicate that the equal back of queue strategy best reflects driver behavior. Based on the observed field data, the equal back of queue strategy outperforms the equal delay strategy and the equal flow ratio strategy which are widely used in various international capacity guides. <P>
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Books on the topic "Traffic flow Traffic engineering"

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Fukui, Minoru. Traffic and Granular Flow'01. Springer Berlin Heidelberg, 2003.

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Salter, Richard J. Traffic engineering: Worked examples. 2nd ed. Macmillan, 1989.

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Helbing, Dirk. Traffic and Granular Flow '99: Social, Traffic, and Granular Dynamics. Springer Berlin Heidelberg, 2000.

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Smulders, S. A. Control of freeway traffic flow. Centrum voor Wiskunde en Informatica, 1996.

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Leutzbach, Wilhelm. Introduction to the Theory of Traffic Flow. Springer Berlin Heidelberg, 1988.

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Gattis, J. L. Designing horizontal curves for low-speed environments. University of Arkansas, Mack-Blackwell National Rural Transportation Study Center, 2003.

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Rosinak, Werner. Landesverkehrskonzept Salzburg. Amt der Salzburger Landesregierung, Landespressebüro, 1992.

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Virginia. Dept. of Transportation. Report of the Virginia Department of Transportation on residential cut-through traffic: To the governor and the General Assembly of Virginia. Commonwealth of Virginia, 1991.

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Prophète, Fritz. Guide de relevé de circulation: Méthodologie générale des relevés automatiques, principe des relevés manuels. Gouvernement du Québec, Ministère des transports, 1995.

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Agent, Kenneth R. Evaluation of the ADAPTIR system for work zone traffic control. Kentucky Transportation Center, College of Engineering, University of Kentucky, 1999.

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Book chapters on the topic "Traffic flow Traffic engineering"

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Salter, R. J. "Flow, Speed and Density Relationships for Highway Flow." In Traffic Engineering. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-10800-8_8.

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Salter, R. J. "Costs and Benefits of Highway Traffic Flow." In Traffic Engineering. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-10800-8_29.

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Gene Hawkins Jr., H. "Traffic Flow Characteristics for Uninterrupted-Flow Facilities." In Traffic Engineering Handbook. John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781119174738.ch7.

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Salter, R. J. "Relationship between Entry and Circulating Flow at Roundabouts." In Traffic Engineering. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-10800-8_15.

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Salter, R. J. "Flow, Speed and Density Relationships Applied to a Highway Bottleneck." In Traffic Engineering. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-10800-8_9.

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Pande, Anurag, and Brian Wolshon. "Design and Control for Interrupted Traffic Flow through Intersections." In Traffic Engineering Handbook. John Wiley & Sons, Inc, 2016. http://dx.doi.org/10.1002/9781119174738.ch10.

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Guerrieri, Marco, and Raffaele Mauro. "Macroscopic Traffic Flow Models." In Springer Tracts in Civil Engineering. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60723-4_2.

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Salter, R. J. "Right Turning Flows at Traffic Signals Illustrated by an Example." In Traffic Engineering. Macmillan Education UK, 1989. http://dx.doi.org/10.1007/978-1-349-10800-8_22.

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Brockfeld, Elmar, and Peter Wagner. "Testing Traffic Flow Models." In Lecture Notes in Computational Science and Engineering. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-07969-0_37.

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Ozaki, H. "Modeling of Vehicular Behavior from Road Traffic Engineering Perspectives." In Traffic and Granular Flow’01. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-10583-2_27.

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Conference papers on the topic "Traffic flow Traffic engineering"

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Wang, Feng, and Lixin Gao. "Observations on traffic flow patterns and traffic engineering practice." In ITCom 2002: The Convergence of Information Technologies and Communications, edited by Victor Firoiu and Zhi-Li Zhang. SPIE, 2002. http://dx.doi.org/10.1117/12.475286.

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Kamiyama, Noriaki, Yousuke Takahashi, Keisuke Ishibashi, et al. "Flow aggregation for traffic engineering." In GLOBECOM 2014 - 2014 IEEE Global Communications Conference. IEEE, 2014. http://dx.doi.org/10.1109/glocom.2014.7037091.

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Montigny-leboeuf, Annie, and Tim Symchych. "Network Traffic Flow Analysis." In 2006 Canadian Conference on Electrical and Computer Engineering. IEEE, 2006. http://dx.doi.org/10.1109/ccece.2006.277589.

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Zhang, Jian-hua, Tao Jiang, Sheng-an Wang, and Jia-wei Ma. "Research of Cellular Automata Traffic Flow Model for Variable Traffic Flow Density." In International Conference on Chemical,Material and Food Engineering. Atlantis Press, 2015. http://dx.doi.org/10.2991/cmfe-15.2015.172.

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Lou, Zheng, and Liming Dai. "A Study of Traffic Noise Reduction Performance of Arc Pavement Roads and Traffic Flow." In ASME 2006 International Mechanical Engineering Congress and Exposition. ASMEDC, 2006. http://dx.doi.org/10.1115/imece2006-13325.

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Asphalt Rubber Concrete (ARC) pavement has shown an excellent performance of noise reduction in terns of reducing the power of air pumping, absorbing sound power, depressing carcass vibration and changing sound reflection geometry. This research is to investigate the traffic noise reduction performance of a segment of test highway with ARC pavement in Saskatchewan, Canada. Before and after the highway section was repaved, a series of traffic noise level measurements combining with traffic flow monitoring are conducted in order to compare the sound performance of ARC and conventional pavements. A relationship between the noise level and corresponding traffic flow conditions of ARC pavement is established. The energetic averaging method is employed to study the relationship between traffic noise level and traffic flow condition. The two noise levels of 24-hour's time averaged and Statistical Pass-By noise levels indicated that the ARC pavement has a better sound performance over that of conventional pavement in terms of traffic noise reduction. The traffic noise reduction applicability of ARC pavement under various traffic flow conditions is also performed in this research.
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Makki, Ahmed Adnan, Trung Thanh Nguyen, June Ren, William Hurst, and Dhiya Al-jumeily. "Utilizing Automatic Traffic Counters to Predict Traffic Flow Speed." In 2019 12th International Conference on Developments in eSystems Engineering (DeSE). IEEE, 2019. http://dx.doi.org/10.1109/dese.2019.00153.

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Kou, Fei, Weixiang Xu, and Huiting Yang. "Short-Term Traffic Flow Forecasting Considering Upstream Traffic Information." In 2018 International Conference on Mechanical, Electronic, Control and Automation Engineering (MECAE 2018). Atlantis Press, 2018. http://dx.doi.org/10.2991/mecae-18.2018.86.

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Yuanhua Jia and Enhui Xing. "The application Of traffic flow detection technology on characteristics of freeway traffic flow." In 2011 International Conference on Remote Sensing, Environment and Transportation Engineering (RSETE). IEEE, 2011. http://dx.doi.org/10.1109/rsete.2011.5964408.

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Chu, Kang-Ching, Romesh Saigal, and Kazuhiro Saitou. "Stochastic Lagrangian Traffic flow modeling and real-time traffic prediction." In 2016 IEEE International Conference on Automation Science and Engineering (CASE). IEEE, 2016. http://dx.doi.org/10.1109/coase.2016.7743383.

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Chu, Kang-Ching, Li Yang, Romesh Saigal, and Kazuhiro Saitou. "Validation of stochastic traffic flow model with microscopic traffic simulation." In 2011 IEEE International Conference on Automation Science and Engineering (CASE 2011). IEEE, 2011. http://dx.doi.org/10.1109/case.2011.6042479.

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Reports on the topic "Traffic flow Traffic engineering"

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Ould-Brahim, H., D. Fedyk, and Y. Rekhter. BGP Traffic Engineering Attribute. RFC Editor, 2009. http://dx.doi.org/10.17487/rfc5543.

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Brownlee, N., C. Mills, and G. Ruth. Traffic Flow Measurement: Architecture. RFC Editor, 1999. http://dx.doi.org/10.17487/rfc2722.

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Brownlee, N., C. Mills, and G. Ruth. Traffic Flow Measurement: Architecture. RFC Editor, 1997. http://dx.doi.org/10.17487/rfc2063.

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Carlson, Jake. Traffic Flow - Purdue University. Purdue University Libraries, 2009. http://dx.doi.org/10.5703/1288284315016.

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Meyer, M., ed. MPLS Traffic Engineering Soft Preemption. RFC Editor, 2010. http://dx.doi.org/10.17487/rfc5712.

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Kompella, K. A Traffic Engineering (TE) MIB. RFC Editor, 2005. http://dx.doi.org/10.17487/rfc3970.

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Tsirtsis, G., G. Giarreta, H. Soliman, and N. Montavont. Traffic Selectors for Flow Bindings. RFC Editor, 2011. http://dx.doi.org/10.17487/rfc6088.

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Brownlee, N. Traffic Flow Measurement: Meter MIB. RFC Editor, 1997. http://dx.doi.org/10.17487/rfc2064.

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Brownlee, N. Traffic Flow Measurement: Meter MIB. RFC Editor, 1999. http://dx.doi.org/10.17487/rfc2720.

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Ayyangar, A. Inter-Domain MPLS and GMPLS Traffic Engineering -- Resource Reservation Protocol-Traffic Engineering (RSVP-TE) Extensions. Edited by A. Farrel. RFC Editor, 2008. http://dx.doi.org/10.17487/rfc5151.

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