Journal articles on the topic 'Fluid traffic modeling'
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Nicol, David M., and Guanhua Yan. "Discrete event fluid modeling of background TCP traffic." ACM Transactions on Modeling and Computer Simulation 14, no. 3 (July 2004): 211–50. http://dx.doi.org/10.1145/1010621.1010622.
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Metar, Manas. "Computational Fluid Dynamic Analysis of Conceptual 3D Car Model." International Journal for Research in Applied Science and Engineering Technology 9, no. 12 (December 31, 2021): 1704–11. http://dx.doi.org/10.22214/ijraset.2021.39608.
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Abstract: From past decades, people are giving more attention to conservation of the fuels. The increasing number of passenger cars have increased the amount of traffic which directly impacts pollution and traffic congestion. Manufacturers are indulged into making lightweight and performance efficient automobiles. Implementation of different designs and materials has been in practice since ages. We need smaller vehicle designs for personal transport and electric vehicles to tackle the raising problems. In future designs, vehicles will be efficient enough to save more fuel and also the traffic problems may be solved. But for the design optimizations and experiments we need different analyses to be performed, one of which is aerodynamic analysis. In this paper a CFD analysis is done to check the aerodynamic performance of a proposed car design. The car has been designed using Onshape modeling software and analyzed in Simscale software. The car is subjected to different vehicle speeds and the results of drag coefficients and pressure plots are shown. Keywords: Design and analysis of a vehicle, CFD analysis, Aerodynamic analysis, 3D modelling, Drag coefficient, Pressure plot, Concept car, Performance Optimization.
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Itoh, Eri, Mihaela Mitici, and Michael Schultz. "Modeling Aircraft Departure at a Runway Using a Time-Varying Fluid Queue." Aerospace 9, no. 3 (February 25, 2022): 119. http://dx.doi.org/10.3390/aerospace9030119.
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Reducing the length of departure queues at runway entry points is one of the most important requirements for reducing aircraft traffic congestion and fuel consumption at airports. This study designs an aircraft departure model at a runway using a time-varying fluid queue. The proposed model enables us to determine the aircraft waiting time in the departure queue and to evaluate effective control approaches for assigning suitable holds at gates rather than runway entry points. As a case study, this study modeled the departure queue at runway 05 of Tokyo International Airport for an entire day of operations. Using actual traffic data of departures at the airport, the model estimates that aircraft spend a total of 2.5 h departure waiting time in a day at runway 05. Considering the stochastic nature of actual departure traffic, the relevance of the proposed model is discussed using validation criteria. The model estimation shows a reasonable, expected order of magnitude compared with the departure queue recorded in the actual traffic data. Furthermore, ecological and economic benefits are quantitatively evaluated assuming a reduction in the departure queue length. Our results show that about one kiloton of fuel oil per year is wasted due to aircraft waiting to depart from a single departure runway.
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Tang, Jie. "Research and empirical Analysis of Traffic flow Modeling based on fluid Mechanics." IOP Conference Series: Earth and Environmental Science 692, no. 4 (March 1, 2021): 042102. http://dx.doi.org/10.1088/1755-1315/692/4/042102.
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Kuznetsov, A. V., and K. Hooman. "Modeling traffic jams in intracellular transport in axons." International Journal of Heat and Mass Transfer 51, no. 23-24 (November 2008): 5695–99. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2008.04.022.
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DARBHA, SWAROOP, and K. R. RAJAGOPAL. "LIMIT OF A COLLECTION OF DYNAMICAL SYSTEMS: AN APPLICATION TO MODELING THE FLOW OF TRAFFIC." Mathematical Models and Methods in Applied Sciences 12, no. 10 (October 2002): 1381–99. http://dx.doi.org/10.1142/s0218202502002161.
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The flow of traffic is usually described using a continuum approach as that of a compressible fluid, a statistical approach via the kinetic theory of gases or cellular automata models. These approaches are not suitable for modeling dynamical systems such as traffic. While such systems are large collections, they are not large enough to be treated as a continuum. We provide a rationale for why they cannot be appropriately described using a continuum model, the kinetic theory of gases, or by appealing to cellular automata models. As an alternative, we develop a discrete dynamical systems approach that is particularly well suited to describe the dynamics of large systems such as traffic.
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Sun, Dazhi, Jinpeng Lv, and S. Travis Waller. "In-depth analysis of traffic congestion using computational fluid dynamics (CFD) modeling method." Journal of Modern Transportation 19, no. 1 (March 2011): 58–67. http://dx.doi.org/10.1007/bf03325741.
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Otegen, Diana Assankhankyzy. "MODELS OF TRAFFIC FLOW DYNAMICS ON HIGHWAYS." Вестник КазАТК 116, no. 1 (March 15, 2021): 236–41. http://dx.doi.org/10.52167/1609-1817-2021-116-1-236-241.
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The paper is an analytical review of the currently existing methods of traffic flows modeling. The movement of vehicles on the road can be modeled in different ways. Mathematical models as tools that allow us to study complex processes in the real world, including transport infrastructure, without capital expenditures, are a popular tool for solving many problems in various spheres of the national economy. There are several approaches to mathematical modeling of traffic flows. In microscopic models, the law of motion of each car is set, depending on its current position, speed, characteristics of the movement of neighboring cars, and other factors. Microscopic models, in turn, can be divided into models that are continuous in space and time, and into models that are discrete in space and time, the so-called cellular automata. In macroscopic models, the transport flow is considered as a fluid flow with special properties. The equations of the macroscopic model establish the relationship between the flow, density, speed of movement, possibly acceleration, and so on. Macroscopic models can also be continuous or discrete. In continuous models, the change in the state of a road section without branches and intersections is usually described by partial differential equations. Modeling traffic flows is necessary because active experiments in the existing transport network are fraught with unpredictable consequences, and in many cases are not feasible at all. The work presents a description and analysis of the models, and of their advantages and disadvantages.
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Rocha, F. G. C., C. B. Santos, and F. H. T. Vieira. "Traffic Modeling in PLC Networks using a Markov Fluid Model with Autocorrelation Function Fitting." TEMA - Tendências em Matemática Aplicada e Computacional 12, no. 3 (December 2011): 233–43. http://dx.doi.org/10.5540/tema.2011.012.03.0233.
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Kontovasilis, Kimon P., and Nikolas M. Mitrou. "Bursty traffic modeling and efficient analysis algorithms via fluid-flow models for ATM IBCN." Annals of Operations Research 49, no. 1 (December 1994): 279–323. http://dx.doi.org/10.1007/bf02031601.
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Lavassani, Mehrzad, Johan Åkerberg, and Mats Björkman. "Modeling and Profiling of Aggregated Industrial Network Traffic." Applied Sciences 12, no. 2 (January 11, 2022): 667. http://dx.doi.org/10.3390/app12020667.
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The industrial network infrastructures are transforming to a horizontal architecture to enable data availability for advanced applications and enhance flexibility for integrating new technologies. The uninterrupted operation of the legacy systems needs to be ensured by safeguarding their requirements in network configuration and resource management. Network traffic modeling is essential in understanding the ongoing communication for resource estimation and configuration management. The presented work proposes a two-step approach for modeling aggregated traffic classes of brownfield installation. It first detects the repeated work-cycles and then aims to identify the operational states to profile their characteristics. The performance and influence of the approach are evaluated and validated in two experimental setups with data collected from an industrial plant in operation. The comparative results show that the proposed method successfully captures the temporal and spatial dynamics of the network traffic for characterization of various communication states in the operational work-cycles.
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CHOPARD, BASTIEN, ALEXANDRE DUPUIS, ALEXANDRE MASSELOT, and PASCAL LUTHI. "CELLULAR AUTOMATA AND LATTICE BOLTZMANN TECHNIQUES: AN APPROACH TO MODEL AND SIMULATE COMPLEX SYSTEMS." Advances in Complex Systems 05, no. 02n03 (June 2002): 103–246. http://dx.doi.org/10.1142/s0219525902000602.
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We discuss the cellular automata approach and its extensions, the lattice Boltzmann and multiparticle methods. The potential of these techniques is demonstrated in the case of modeling complex systems. In particular, we consider applications taken from various fields of physics, such as reaction-diffusion systems, pattern formation phenomena, fluid flows, fracture processes and road traffic models.
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Li, Dongshuang, Xu Hu, Xinxin Zhou, Wen Luo, A. Xing Zhu, and Zhaoyuan Yu. "Modeling Random Exit Selection in Intercity Expressway Traffic with Quantum Walk." Applied Sciences 12, no. 4 (February 18, 2022): 2139. http://dx.doi.org/10.3390/app12042139.
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In intercity expressway traffic, the multiplicity of available routes leads to randomness in exit selection. Random exit selection by drivers is hard to observe, and thus it is a challenge to model intercity expressway traffic sufficiently. In this paper, we developed a Random Quantum Traffic Model (RQTM), which modeled the stochastic traffic fluctuation caused by random exit selection and the residual regularity fluctuation with the quantum walk and autoregressive moving average model (ARMA), respectively. The RQTM considered the random exit selection of a driver as a quantum stochastic process with a dynamic probability function. A quantum walk was applied to update the probability function, which simulated when and where a driver will leave the expressway. We validated our model with hourly traffic data from seven exits from the Nanjing–Changzhou expressway in eastern China. For the seven exits, the coefficients of determination of the RQTM ranged from 0.5 to 0.85. Compared with the classical random walk and the ARMA model, the coefficients of determination were increased by 21.28% to 104.98%, and the relative mean square error decreased by 11.61% to 32.92%. We conclude that the RQTM provides new potential for modeling traffic dynamics with consideration of unobservable random driver decision making.
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Nowy, Agnieszka, Kinga Łazuga, Lucjan Gucma, Andrej Androjna, Marko Perkovič, and Jure Srše. "Modeling of Vessel Traffic Flow for Waterway Design–Port of Świnoujście Case Study." Applied Sciences 11, no. 17 (September 1, 2021): 8126. http://dx.doi.org/10.3390/app11178126.
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The paper presents an analysis of ship traffic using the port of Świnoujście and the problems associated with modelling vessel traffic flows. Navigation patterns were studied using the Automatic Identification System (AIS); an analysis of vessel traffic was performed with statistical methods using historical data; and the paper presents probabilistic models of the spatial distribution of vessel traffic and its parameters. The factors that influence the spatial distribution were considered to be the types of vessels, dimensions, and distances to hazards. The results show a correlation between the standard deviation of the traffic flow, the vessel sizes, and the distance to the hazard. These can be used in practice to determine the safety of navigation and the design of non-existing waterways and to create a general model of vessel traffic flow. The creation of the practical applications is intended to improve navigation efficiency, safety, and risk analysis in any particular area.
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Letaïef, Sarah, Pierre Camps, Thierry Poidras, Patrick Nicol, Delphine Bosch, and Romane Pradeau. "Biomagnetic Monitoring vs. CFD Modeling: A Real Case Study of Near-Source Depositions of Traffic-Related Particulate Matter along a Motorway." Atmosphere 11, no. 12 (November 28, 2020): 1285. http://dx.doi.org/10.3390/atmos11121285.
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A test site located along a 12-lane motorway east of Montpellier, France, is used to evaluate the potential of biomagnetic monitoring on traffic-related particulate matter (PM) to parametrize a computational fluid dynamics (CFD) simulation of the local airflow. Two configurations were established on the site with three vegetated flat-top earth berms of a basic design, and a fourth one was located windward to the traffic roofed with a 4-m-high precast concrete wall. As a first step, PM deposition simultaneously on plant leaves, on low-cost passive artificial filters, and on soils was estimated from proxies supplied by magnetic and X-ray fluorescence measurements on both sides of the motorway. These latter revealed that traffic-related pollutants are present on soils samples highlighted with a clear fingerprint of combustion residues, and wears of breaks, vehicles, and highway equipment. Maximum PM accumulations were detected in the lee of the berm–wall combination, while no significant deposition was observed on both sides of the flat-top earth berms. These results are in line with measurements from PM µ-sensors operated by the regional state-approved air quality agency. Finally, we compared the experimental measurements with the outcomes of a computational fluid dynamics (CFD) modeling based on the Reynolds-Averaged Navier–Stokes (RANS) equations that consider the traffic-induced momentum and turbulence. The CFD modeling matches the experimental results by predicting a recirculated flow in the near wake of the berm–wall combination that enhances the PM concentration, whereas the flat-top berm geometry does not alter the pollutants’ transport and indeed contributes to their atmospheric dispersion.
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Merah, A., and A. Noureddine. "Reactive Pollutants Dispersion Modeling in a Street Canyon." International Journal of Applied Mechanics and Engineering 24, no. 1 (February 1, 2019): 91–103. http://dx.doi.org/10.2478/ijame-2019-0006.
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Abstract Reactive pollutant dispersion in a 3-D urban street canyon is numerically investigated using a computational fluid dynamics (CFD) code (Ansys-CFX), with the k–ε turbulence model and includes transport equations for NO, NO2, and O3 with simple photochemistry. An area emission source of NO and NO2 was considered in the presence of background O3 with an ambient wind perpendicular to the along-canyon direction. The results showed that the magnitude of NOx (NO+NO2) concentrations on the leeward side of the upstream buildings was much larger than the windward side of the downstream building, due to the entrainment and dispersion of traffic emissions by the primary vortex. The reverse is the case for ozone with higher concentrations on the windward side compared to the leeward side. The model has been validated against no-reactive pollutant experimental data of the wind tunnel experiments of Hoydysh and Dabberdt [1].
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Granato, Giuseppe, Alessio Martino, Andrea Baiocchi, and Antonello Rizzi. "Graph-Based Multi-Label Classification for WiFi Network Traffic Analysis." Applied Sciences 12, no. 21 (November 7, 2022): 11303. http://dx.doi.org/10.3390/app122111303.
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Network traffic analysis, and specifically anomaly and attack detection, call for sophisticated tools relying on a large number of features. Mathematical modeling is extremely difficult, given the ample variety of traffic patterns and the subtle and varied ways that malicious activity can be carried out in a network. We address this problem by exploiting data-driven modeling and computational intelligence techniques. Sequences of packets captured on the communication medium are considered, along with multi-label metadata. Graph-based modeling of the data are introduced, thus resorting to the powerful GRALG approach based on feature information granulation, identification of a representative alphabet, embedding and genetic optimization. The obtained classifier is evaluated both under accuracy and complexity for two different supervised problems and compared with state-of-the-art algorithms. We show that the proposed preprocessing strategy is able to describe higher level relations between data instances in the input domain, thus allowing the algorithms to suitably reconstruct the structure of the input domain itself. Furthermore, the considered Granular Computing approach is able to extract knowledge on multiple semantic levels, thus effectively describing anomalies as subgraphs-based symbols of the whole network graph, in a specific time interval. Interesting performances can thus be achieved in identifying network traffic patterns, in spite of the complexity of the considered traffic classes.
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Ros-Roca, Xavier, Lídia Montero, and Jaume Barceló. "Exploring the Direct and Indirect Use of ICT Measurements in DODME (Dynamic OD Matrix Estimation)." Applied Sciences 11, no. 22 (November 18, 2021): 10910. http://dx.doi.org/10.3390/app112210910.
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The estimation of the network traffic state, its likely short-term evolution, the prediction of the expected travel times in a network, and the role that mobility patterns play in transport modeling is usually based on dynamic traffic models, whose main input is a dynamic origin–destination (OD) matrix that describes the time dependencies of travel patterns; this is one of the reasons that have fostered large amounts of research on the topic of estimating OD matrices from the available traffic information. The complexity of the problem, its underdetermination, and the many alternatives that it offers are other reasons that make it an appealing research topic. The availability of new traffic data measurements that were prompted by the pervasive penetration of information and communications technology (ICT) applications offers new research opportunities. This study focused on GPS tracking data and explored two alternative modeling approaches regarding how to account for this new information to solve the dynamic origin–destination matrix estimation (DODME) problem, either including it as an additional term in the formulation model or using it in a data-driven modeling method to propose new model formulations. Complementarily, independently of the approach used, a key aspect is the quality of the estimated OD, which, as recent research has made evident, is not well measured by the conventional indicators. This study also explored this problem for the proposed approaches by conducting synthetic computational experiments to control and understand the process.
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Caliendo, Ciro, Isidoro Russo, and Gianluca Genovese. "Risk Analysis of One-Way Road Tunnel Tube Used for Bi-Directional Traffic under Fire Scenarios." Applied Sciences 11, no. 7 (April 2, 2021): 3198. http://dx.doi.org/10.3390/app11073198.
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We have set up a Computational Fluid Dynamics (CFD) modeling, and performed a user evacuation model, for evaluating the risk level in one-way road tunnel tube when used for bi-directional traffic in particular circumstances. The simulations were carried out by considering both peak-hour traffic volumes during the day and off-peak hours overnight. The investigated one-way tube is ventilated by natural ventilation only, and has a length of less than 1000 m. With reference to the worst environmental conditions, which are downstream of the fire due to the direction of natural ventilation, the consequences on escaping users, caused by different types of burning vehicles located in various longitudinal positions along the tube, are shown. The results prove the positive effects on environmental conditions (in terms of temperature, visibility distance, CO and CO2 concentration) along the user evacuation path when the tube is used for bi-directional traffic at night rather than daytime. Only for the case of 100 MW fire and in the proximity of the exit portal, the last escaping user might be affected by a visibility distance and CO concentration exceeding the threshold values. In this special case, countermeasures for reducing smoke concentration or emergency services at the portals should be provided. However, the quantitative risk analysis, based on a probabilistic approach, showed that the F-N curve of the tube when used for bi-directional traffic with reference to the night always lies below that of the daytime, and the reduction in the risk level is between 80 and 100% for the night traffic compared to daytime one. It is to be focused on the fact that our modeling may represent a reference in investigating the effects of hourly traffic volumes on the risk level in tunnels and may help decisionmakers in understanding when to temporarily close a tube for maintenance, repair, or rehabilitation activities and use the adjacent tube for bi-directional traffic.
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Hong, Seok-Bum, Byung-Woong Lee, Chang-Hoon Kim, and Hong-Sik Yun. "System Dynamics Modeling for Estimating the Locations of Road Icing Using GIS." Applied Sciences 11, no. 18 (September 14, 2021): 8537. http://dx.doi.org/10.3390/app11188537.
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Road icing can cause large traffic accidents on highways because, unlike snowy roads, its location is difficult to identify and it can occur rapidly, even during rainy weather. In this study, the amount and location of road icing were modeled and simulated over time based on the system dynamics theory. The simulation is expressed on the geographic information system (GIS) and facilitates advance detection of the location and amount of road icing that occurs unexpectedly unlike previous studies. Modeling was designed to process spatial and meteorological data after combining them. The spatial data used for modeling were Hillshade, Water System, Bridge, and Road (Highway). Air temperature, cloudiness, vapor pressure, wind speed, and precipitation were used as meteorological data. The amount of road icing was estimated by scientifically designing the parameters related to its occurrence between spatial and meteorological data. Based on this, the amount of road icing by location was simulated per 1m2 using the GIS. The simulation results showed that the amount of road icing that began to increase from AM 08:00 reached its peak (an average of 213.62 g/m2) at noon and then slowly decreased. Additionally, when simulated with GIS, the sum amount of road icing between AM 12:00 and PM 13:00 was a maximum of 1707.292 (g/14 h) and a minimum of 360.082 (g/14 h) for each location. Hypothesis testing was conducted on whether road icing significantly occurs at actual points vulnerable to traffic accidents. Based on the results, the average significance level was calculated to be less than 0.05. Therefore, the alternative hypothesis that the model can estimate road icing in vulnerable areas was adopted. The verified simulation can be useful data to government agencies (e.g., road traffic authority) in their programs to prevent traffic accidents caused by road icing.
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Lukoševičius, Vaidas, Rolandas Makaras, and Andrius Dargužis. "Assessment of Tire Features for Modeling Vehicle Stability in Case of Vertical Road Excitation." Applied Sciences 11, no. 14 (July 18, 2021): 6608. http://dx.doi.org/10.3390/app11146608.
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Two trends could be observed in the evolution of road transport. First, with the traffic becoming increasingly intensive, the motor road infrastructure is developed; more advanced, greater quality, and more durable materials are used; and pavement laying and repair techniques are improved continuously. The continued growth in the number of vehicles on the road is accompanied by the ongoing improvement of the vehicle design with the view towards greater vehicle controllability as the key traffic safety factor. The change has covered a series of vehicle systems. The tire structure and materials used are subject to continuous improvements in order to provide the maximum possible grip with the road pavement. New solutions in the improvement of the suspension and driving systems are explored. Nonetheless, inevitable controversies have been encountered, primarily, in the efforts to combine riding comfort and vehicle controllability. Practice shows that these systems perform to a satisfactory degree only on good quality roads, as they have been designed specifically for the latter. This could be the cause of the more complicated car control and accidents on the lower-quality roads. Road ruts and local unevenness that impair car stability and traffic safety are not avoided even on the trunk roads. In this work, we investigated the conditions for directional stability, the influence of road and vehicle parameters on the directional stability of the vehicle, and developed recommendations for the road and vehicle control systems to combine to ensure traffic safety. We have developed a refined dynamic model of vehicle stability that evaluates the influence of tire tread and suspensions. The obtained results allow a more accurate assessment of the impact of the road roughness and vehicle suspension and body movements on vehicle stability and the development of recommendations for the safe movement down the road of known characteristics.
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Ali, Md Eabad, Md Farhad Hasan, Sadia Siddiqa, Md Mamun Molla, and Most Nasrin Akhter. "FVM-RANS Modeling of Air Pollutants Dispersion and Traffic Emission in Dhaka City on a Suburb Scale." Sustainability 15, no. 1 (December 30, 2022): 673. http://dx.doi.org/10.3390/su15010673.
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The present study aims to investigate the impact of air pollutants dispersion from traffic emission under the influence of wind velocity and direction considering the seasonal cycle in two major areas of Dhaka city: namely, Tejgaon and Gazipur. Carbon monoxide (CO) mass fraction has been considered as a representative element of traffic-exhausted pollutants, and the distribution of pollutants has been investigated in five different street geometries: namely, single regular and irregular, double regular and irregular, and finally, multiple irregular streets. After the grid independence test confirmation as well as numerical validation, a series of case studies has been presented to analyze the air pollutants dispersion, which mostly exists due to the traffic emission. The popular Reynolds-averaged Navier–Stokes (RANS) approach has been considered, and the finite volume method (FVM) has been applied by ANSYS FluentTM. The k−ϵ turbulence model has been integrated from the RANS approach. It was found that the wind velocity as well as wind direction and the fluid flow fields can play a potential role on pollution dispersion in the Dhaka city street canyons and suburbs. Inhabitants residing near the single regular streets are exposed to more traffic emission than those of single irregular streets due to fewer obstacles being created by the buildings. Double regular streets have been found to be a better solution to disperse pollutants, but city dwellers in the east region of double irregular streets are exposed to a greater concentration of pollutants due to the change of wind directions and seasonal cycles. Multiple irregular streets limit the mobility of the pollutants due to the increased number of buildings, yet the inhabitants near the multi-irregular streets are likely to experience approximately 11.25% more pollutants than other dwellers living far from the main street. The key findings of this study will provide insights on improving the urbanization plan where different geometries of streets are present and city dwellers could have less exposure to traffic-exhausted pollutants. The case studies will also provide a template layout to map pollutant exposure to identify the alarming zone and stop incessant building construction within those regions by creating real-time air quality monitoring to safeguard public safety.
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Nlend, Samuel, Theo G. Swart, and Bhekisipho Twala. "Access Convergence for Heavy Load Markov Ethernet Bursty Traffic Using Two-level Statistical Multiplexing." ECTI Transactions on Electrical Engineering, Electronics, and Communications 20, no. 3 (October 15, 2022): 358–70. http://dx.doi.org/10.37936/ecti-eec.2022203.247512.
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A method for modeling aggregated heavy Markov bursty Ethernet traffic from different sources is proposed in this paper, particularly that prevailing between gateway services and internet routing devices, with the aim of achieving rate accommodation. In other words, to accommodate different rates while filtering out delays in the queue, to achieve access network convergence. Although gateway functions can be used to achieve this by adapting service rates, as many gateways as services are required. Instead of considering the distributed gateway services method, statistical multiplexing is chosen for this study for cost efficiency in network resources. Unfortunately, statistical multiplexing exhibits greater packet variation (jitter) and transfer delay. These delays, basically resulting from positive correlations or time dependency in the queue system, are addressed through infinitesimal queue modeling, based on the diffusion process approximated by Ornstein-Uhlenbeck, which deals with infinitesimal changes in the Markov queue. The related analysis has resulted in an exponential queueing model for univariate and/or multivariate servers obtained through Markov Gaussian approximation. An experiment based on two different voice algorithms shows rate accommodation, and a fluid solution, which is dynamically outputted according to the transmission link availability during each transition time, without any significant delay. Hence, better transfer delay and rate control is obtained through the proposed two multiplexing levels within an Ethernet LAN
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Rossigali, Carlos Eduardo, Michèle Schubert Pfeil, Luis Volnei Sudati Sagrilo, and Hugo Medeiros de Oliveira. "Load Models Representative of Brazilian Actual Traffic in Girder-Type Short-Span Highway Bridges." Applied Sciences 13, no. 2 (January 12, 2023): 1032. http://dx.doi.org/10.3390/app13021032.
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The Brazilian code NBR 7188 for highway bridge design prescribes a live load model consisting of a 3-axle vehicle plus a uniform load, which are both affected by an impact factor when considering dynamic effects. It does not play out actual traffic static effects and may be, in some cases, non-conservative. This work presents two load model configurations developed by gathering a real traffic database, traffic simulations, modeling of the dynamic interaction between vehicles and structure, and statistical extrapolations. Proposed load models comprise dynamic effects and were calibrated for two-lane single carriageway bridges with span lengths up to 40 m under free-flowing traffic. The target values of the effects were compared to those generated by the new proposed load models, as well as by the Brazilian design load model. Adequacy of the proposed models is demonstrated; results show that the effects generated by the Brazilian design load model are underestimated in many cases, particularly for negative moments in cantilevers.
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Dimitrijevic, Branislav, Sina Darban Khales, Roksana Asadi, and Joyoung Lee. "Short-Term Segment-Level Crash Risk Prediction Using Advanced Data Modeling with Proactive and Reactive Crash Data." Applied Sciences 12, no. 2 (January 14, 2022): 856. http://dx.doi.org/10.3390/app12020856.
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Highway crashes, along with the property damage, personal injuries, and fatalities that they cause, continue to present one of the most significant and critical transportation problems. At the same time, provision of safe travel is one of the main goals of any transportation system. For this reason, both in transportation research and practice much attention has been given to the analysis and modeling of traffic crashes, including the development of models that can be applied to predict crash occurrence and crash severity. In general, such models assess short-term crash risks at a given highway facility, thus providing intelligence that can be used to identify and implement traffic operations strategies for crash mitigation and prevention. This paper presents several crash risk and injury severity assessment models applied at a highway segment level, considering the input data that is typically collected or readily available to most transportation agencies in real-time and at a regional network scale, which would render them readily applicable in practice. The input data included roadway geometry characteristics, traffic flow characteristics, and weather condition data. The paper develops, tests, and compares the performance of models that employ Random effects Bayesian Logistics Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Random Forest, and Gradient Boosting Machine methods. The paper applies random oversampling examples (ROSE) method to deal with the problem of data imbalance associated with the injury severity analysis. The models were trained and tested using a dataset of 10,155 crashes that occurred on two interstate highways in New Jersey over a two-year period. The paper also analyzes the potential improvement in the prediction abilities of the tested models by adding reactive data to the analysis. To that end, traffic crashes were classified in multiple classes based on the driver age and the vehicle age to assess the impact of these attributes on driver injury severity outcomes. The results of this analysis are promising, showing that the simultaneous use of reactive and proactive data can improve the prediction performance of the presented models.
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Harrou, Fouzi, Abdelkader Dairi, Abdelhafid Zeroual, and Ying Sun. "Forecasting of Bicycle and Pedestrian Traffic Using Flexible and Efficient Hybrid Deep Learning Approach." Applied Sciences 12, no. 9 (April 28, 2022): 4482. http://dx.doi.org/10.3390/app12094482.
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Recently, increasing interest in managing pedestrian and bicycle flows has been demonstrated by cities and transportation professionals aiming to reach community goals related to health, safety, and the environment. Precise forecasting of pedestrian and bicycle traffic flow is crucial for identifying the potential use of bicycle and pedestrian infrastructure and improving bicyclists’ safety and comfort. Advances in sensory technology enable collecting massive traffic flow data, including road traffic, bicycle, and pedestrian traffic flow. This paper introduces a novel deep hybrid learning model with a fully guided-attention mechanism to improve bicycles and pedestrians’ traffic flow forecasting. Notably, the proposed approach extends the modeling capability of the Variational Autoencoder (VAE) by merging a long short-term memory (LSTM) model with the VAE’s decoder and using a self-attention mechanism at multi-stage of the VAE model (i.e., decoder and before data resampling). Specifically, LSTM improves the VAE decoder’s capacity in learning temporal dependencies, and the guided-attention units enable selecting relevant features based on the self-attention mechanism. This proposed deep hybrid learning model with a multi-stage guided-attention mechanism is called GAHD-VAE. Proposed methods were validated with traffic measurements from six publicly available pedestrian and bicycle traffic flow datasets. The proposed method provides promising forecasting results but requires no assumptions that the data are drawn from a given distribution. Results revealed that the GAHD-VAE methodology can efficiently enhance the traffic forecasting accuracy and achieved better performance than the deep learning methods VAE, LSTM, gated recurrent units (GRUs), bidirectional LSTM, bidirectional GRU, convolutional neural network (CNN), and convolutional LSTM (ConvLSTM), and four shallow methods, linear regression, lasso regression, ridge regression, and support vector regression.
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Alkhulaifi, Abdolmaged, Arshad Jamal, and Irfan Ahmad. "Predicting Traffic Sign Retro-Reflectivity Degradation Using Deep Neural Networks." Applied Sciences 11, no. 24 (December 7, 2021): 11595. http://dx.doi.org/10.3390/app112411595.
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Traffic signs are essential for the safe and efficient movement of vehicles through the transportation network. Poor sign visibility can lead to accidents. One of the key properties used to measure the visibility of a traffic sign is retro-reflection, which indicates how much light a traffic sign reflects back to the driver. The retro-reflection of the traffic sign degrades over time until it reaches a point where the traffic sign has to be changed or repaired. Several studies have explored the idea of modeling the sign degradation level to help the authorities in effective scheduling of sign maintenance. However, previous studies utilized simpler models and proposed multiple models for different combinations of the sheeting type and color used for the traffic sign. In this study, we present a neural network based deep learning model for traffic sign retro-reflectivity prediction. Data utilized in this study was collected using a handheld retro-reflectometer GR3 from field surveys of traffic signs. Sign retro-reflective measurements (i.e., the RA values) were taken for different sign sheeting brands, grades, colors, orientation angles, observation angles, and aging periods. Feature-based sensitivity analysis was conducted to identify variables’ relative importance in determining retro-reflectivity. Results show that the sheeting color and observation angle were the most significant variables, whereas sign orientation was the least important. Considering all the features, RA prediction results obtained from one-hot encoding outperformed other models reported in the literature. The findings of this study demonstrate the feasibility and robustness of the proposed neural network based deep learning model in predicting the sign retro-reflectivity.
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Wang, Li, Ke Pan, Qi Zhao, Lili Zhang, and Lingyu Zhang. "Research on Queue Equilibrium Control Algorithm of Urban Traffic Based on Game Theory." Applied Sciences 13, no. 3 (January 30, 2023): 1781. http://dx.doi.org/10.3390/app13031781.
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The intersection traffic signal control is an essential means of urban traffic. To solve the problem of urban congestion, it is necessary to consider the optimal signal control strategy for intersections. Using the store-and-forward method of traffic control modeling, the in-queue vehicle number of the key signal phase as the payoff index, this paper designs an optimal intersection signal-timing strategy based on the game theory method. In this strategy, each key phase is regarded as a game player, and each player competes for the release time to maximize their own payoff and minimize the queue. To optimize the intersection efficiency, a game strategy is designed to achieve the Nash equilibrium state, which is the queueing equilibrium of each key phase. Finally, by VISSIM simulation, the total number of stops can be decreased by 5% to 10% compared with the MA-DD-DACC method.
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Hamiga, Władysław, and Wojciech Ciesielka. "Numerical modeling of airflow over column of vehicles using Ansys® package." E3S Web of Conferences 46 (2018): 00025. http://dx.doi.org/10.1051/e3sconf/20184600025.
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Growing needs in transportation determinate systems development which improve efficiency of travel and reduce harmful influence to environment. Computational Fluid Dynamic (CFD) uses numerical analysis to find optimal solutions in terms of chosen objective function. Time save and reduce costs for experiments on prototypes are one of the advantages of this method. The aim of this research is to analyze airflow around different motor vehicles which are moving together in the same direction. To reduce fuel consumption and, at the same time, decrease negative influence to environment, the primary target was reducing total drag force during a ride. The vehicles were set in a column - one after another. In this work considered three types of vehicles: Car, Van and a Truck. Presented vehicles were organized into appropriate groups, creating different configuration. Additional parameter in simulation was distance between vehicles. Simulations of singular vehicles were also done. It allows to evaluate influence of moving vehicles in a column for generated drag force. Described traffic situation were modeled and numerically calculated using ANSYS® package. The purpose of this work was to assess the impact of the distance between vehicles, in a given configuration, for generated drag force.
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Amorim, Jorge Humberto, Joana Valente, Pedro Cascão, Vera Rodrigues, Cláudia Pimentel, Ana I. Miranda, and Carlos Borrego. "Pedestrian Exposure to Air Pollution in Cities: Modeling the Effect of Roadside Trees." Advances in Meteorology 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/964904.
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The exposure of students to traffic-emitted carbon monoxide (CO) in their daily walk to school is evaluated, with a particular emphasis on the effect of trees and route choice. The study is focused on the city centre of Aveiro, in central Portugal. Time evolution of the georeferenced location of an individual is tracked with a GPS for different alternative walking routes to a school. Spatial distribution of CO concentration is simulated with a computational fluid dynamics (CFD) model. An exposure model is developed that associates the georeferenced location of the student with the computed air quality levels (at an average breathing height) for that specific grid cell. For each individual, the model calculates the instantaneous exposure at each time frame and the mean value for a given period. Results show a general benefit induced by the trees over the mean exposure of the student in each route. However, in the case of instantaneous exposure values, this is not consistent along the entire period. Also, the variability of the estimated exposure values indicates the potential error that can be committed when using a single value of air quality as a surrogate of air pollution exposure.
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Meneguzzer, Claudio. "Modeling the Combined Effect of Travelers’ Contrarian Behavior, Learning and Inertia on the Day-to-Day Dynamics of Route Choice." Applied Sciences 13, no. 5 (March 4, 2023): 3294. http://dx.doi.org/10.3390/app13053294.
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Understanding the many facets of repeated route choice behavior in traffic networks is essential for obtaining accurate flow forecasts and enhancing the effectiveness of traffic management measures. This paper presents a model of the day-to-day evolution of route choices incorporating travelers’ contrarian behavior, learning and inertia. The model is formulated as a discrete-time nonlinear dynamical system, and its properties are investigated analytically and numerically with a focus on the effect of the fraction of individuals adopting a contrarian route choice behavior. The findings of the study indicate that the extent of contrarian behavior may have significant impacts on the attractiveness and stability of network equilibria as well as on global system performance. We show that a properly balanced combination of direct and contrarian subjects can protect the system from instabilities triggered by other behavioral and network features. Our results also suggest that the fixed point stability range may depend to a considerable extent on travelers’ inertia and memory of previous experiences, as well as on the form of the travel cost functions used in the model. The occurrence of contrarian behavior should be explicitly taken into account in the design of traffic management schemes involving the deployment of Advanced Traveler Information Systems (ATISs), as it may act as a mitigating factor against the concentration of choices on the recommended routes. The analytical framework proposed in this paper represents a novel contribution, since contrarian behavior in repeated route choice has been investigated mainly by means of empirical or simulation approaches thus far.
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Zhou, Junjie, Siyue Shuai, Lingyun Wang, Kaifeng Yu, Xiangjie Kong, Zuhua Xu, and Zhijiang Shao. "Lane-Level Traffic Flow Prediction with Heterogeneous Data and Dynamic Graphs." Applied Sciences 12, no. 11 (May 25, 2022): 5340. http://dx.doi.org/10.3390/app12115340.
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With the continuous development of smart cities, intelligent transportation systems (ITSs) have ushered in many breakthroughs and upgrades. As a solid foundation for an ITS, traffic flow prediction effectively helps the city to better manage intricate traffic flow. However, existing traffic flow prediction methods such as temporal graph convolutional networks(T-GCNs) ignore the dissimilarities between lanes. Thus, they cannot provide more specific information regarding predictions such as dynamic changes in traffic flow direction and deeper lane relationships. With the upgrading of intersection sensors, more and more intersection lanes are equipped with intersection sensors to detect vehicle information all day long. These spatio-temporal data help researchers refine the focus of traffic prediction research down to the lane level. More accurate and detailed data mean that it is more difficult to mine the spatio-temporal correlations between data, and modeling heterogeneous data becomes more challenging. In order to deal with these problems, we propose a heterogeneous graph convolution model based on dynamic graph generation. The model consists of three components. The internal graph convolution network captures the real-time spatial dependency between lanes in terms of generated dynamic graphs. The external heterogeneous data fusion network comprehensively considers other parameters such as lane speed, lane occupancy, and weather conditions. The codec neural network utilizes a temporal attention mechanism to capture the deep temporal dependency. We test the performance of this model based on two real-world datasets, and extensive comparative experiments indicate that the proposed heterogeneous graph convolution model can improve the prediction accuracy.
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Lin, Chao, Yunyi Wang, Ryozo Ooka, Cédric Flageul, Youngseob Kim, Hideki Kikumoto, Zhizhao Wang, and Karine Sartelet. "Modeling of street-scale pollutant dispersion by coupled simulation of chemical reaction, aerosol dynamics, and CFD." Atmospheric Chemistry and Physics 23, no. 2 (January 26, 2023): 1421–36. http://dx.doi.org/10.5194/acp-23-1421-2023.
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Abstract. In the urban environment, gas and particles impose adverse impacts on the health of pedestrians. The conventional computational fluid dynamics (CFD) methods that regard pollutants as passive scalars cannot reproduce the formation of secondary pollutants and lead to uncertain prediction. In this study, SSH-aerosol, a modular box model that simulates the evolution of gas, primary and secondary aerosols, is coupled with the CFD software, OpenFOAM and Code_Saturne. The transient dispersion of pollutants emitted from traffic in a street canyon is simulated using the unsteady Reynolds-averaged Navier–Stokes equations (RANS) model. The simulated concentrations of NO2, PM10, and black carbon (BC) are compared with field measurements on a street of Greater Paris. The simulated NO2 and PM10 concentrations based on the coupled model achieved better agreement with measurement data than the conventional CFD simulation. Meanwhile, the black carbon concentration is underestimated, probably partly because of the underestimation of non-exhaust emissions (tire and road wear). Aerosol dynamics lead to a large increase of ammonium nitrate and anthropogenic organic compounds from precursor gas emitted in the street canyon.
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Huang, L., S. L. Gong, M. Gordon, J. Liggio, R. Staebler, C. A. Stroud, G. Lu, C. Mihele, J. R. Brook, and C. Q. Jia. "Aerosol–computational fluid dynamics modeling of ultrafine and black carbon particle emission, dilution, and growth near roadways." Atmospheric Chemistry and Physics 14, no. 23 (December 2, 2014): 12631–48. http://dx.doi.org/10.5194/acp-14-12631-2014.
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Abstract. Many studies have shown that on-road vehicle emissions are the dominant source of ultrafine particles (UFPs; diameter < 100 nm) in urban areas and near-roadway environments. In order to advance our knowledge on the complex interactions and competition among atmospheric dilution, dispersion, and dynamics of UFPs, an aerosol dynamics–computational fluid dynamics (CFD) coupled model is developed and validated against field measurements. A unique approach of applying periodic boundary conditions is proposed to model pollutant dispersion and dynamics in one unified domain from the tailpipe level to the ambient near-road environment. This approach significantly reduces the size of the computational domain, and therefore allows fast simulation of multiple scenarios. The model is validated against measured turbulent kinetic energy (TKE) and horizontal gradient of pollution concentrations perpendicular to a major highway. Through a model sensitivity analysis, the relative importance of individual aerosol dynamical processes on the total particle number concentration (N) and particle number–size distribution (PSD) near a highway is investigated. The results demonstrate that (1) coagulation has a negligible effect on N and particle growth, (2) binary homogeneous nucleation (BHN) of H2SO4–H2O is likely responsible for elevated N closest to the road, and (3) N and particle growth are very sensitive to the condensation of semi-volatile organics (SVOCs), particle dry deposition, and the interaction between these processes. The results also indicate that, without the proper treatment of the atmospheric boundary layer (i.e., its wind profile and turbulence quantities), the nucleation rate would be underestimated by a factor of 5 in the vehicle wake region due to overestimated dilution. Therefore, introducing atmospheric boundary layer (ABL) conditions to activity-based emission models may potentially improve their performance in estimating UFP traffic emissions.
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Vatulia, Glib, Juraj Gerlici, Oleksij Fomin, Alyona Lovska, Yuliia Fomina, and Kateryna Kravchenko. "Analysis of the Strength of the Supporting Structure of a TwoSection Hopper Wagon under Operating Loading Conditions." Applied Sciences 13, no. 2 (January 8, 2023): 859. http://dx.doi.org/10.3390/app13020859.
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It is proposed to improve hopper wagon design to provide the possibility of simultaneous transportation of different types of cargo. This improvement consists of dividing the body into two separate sections, interacting with each other through a vertical wall and horizontal connecting belts. A calculation by using the FEM in the SolidWorks Simulation has been carried out to determine the strength of the construction of the two-section hopper wagon. The results of the research showed that the maximum load of the hopper wagon occurs in the first design mode (impact). However, the obtained stresses were lower than permissible by 30%. To calculate the indicators of the wagon dynamics, modeling was carried out. All received dynamics indicators were within the permissible limits. For transporting a hopper wagon on railway ferries in international traffic, it is suggested to use units for the fastening chain. Research of the dynamic load of the hopper wagon supporting structure was carried out through mathematical modeling. The strength analysis of the hopper-wagon-bearing structure was carried out. It was found out that the stresses in the hopper wagon structure did not exceed the normative values. The results of the research will contribute to the increased profitability of railway transport. Additionally, the results of the work can be useful in the creation of modern designs of wagons, including for international traffic.
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Sanchez, Beatriz, Jose-Luis Santiago, Alberto Martilli, Magdalena Palacios, and Frank Kirchner. "CFD modeling of reactive pollutant dispersion in simplified urban configurations with different chemical mechanisms." Atmospheric Chemistry and Physics 16, no. 18 (September 28, 2016): 12143–57. http://dx.doi.org/10.5194/acp-16-12143-2016.
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Abstract. An accurate understanding of urban air quality requires considering a coupled behavior between the dispersion of reactive pollutants and atmospheric dynamics. Currently, urban air pollution is mostly dominated by traffic emission, where nitrogen oxides (NOx) and volatile organic compounds (VOCs) are the primary emitted pollutants. However, modeling reactive pollutants with a large set of chemical reactions, using a computational fluid dynamic (CFD) model, requires a large amount of computational (CPU) time. In this sense, the selection of the chemical reactions needed in different atmospheric conditions becomes essential in finding the best compromise between CPU time and accuracy. The purpose of this work is to assess the differences in NO and NO2 concentrations by considering three chemical approaches: (a) passive tracers (non-reactive), (b) the NOx–O3 photostationary state and (c) a reduced complex chemical mechanism based on 23 species and 25 reactions. The appraisal of the effects of chemical reactions focuses on studying the NO and NO2 dispersion in comparison with the tracer behavior within the street. In turn, the effect of including VOC reactions is also analyzed taking into account several VOC ∕ NOx ratios of traffic emission. Given that the NO and NO2 dispersion can also be affected by atmospheric conditions, such as wind flow or the background concentration from season-dependent pollutants, in this work the influence of wind speeds and background O3 concentrations are studied. The results show that the presence of ozone in the street plays an important role in NO and NO2 concentrations. Therefore, greater differences linked to the chemical approach used are found with higher O3 concentrations and faster wind speeds. This bears relation to the vertical flux as a function of ambient wind speed since it increases the pollutant exchange between the street and the overlying air. This detailed study allows one to ascertain under which atmospheric conditions the inclusion of chemical reactions are necessary for the study of NO and NO2 dispersion. The conclusions can be applied to future studies in order to establish the chemical reactions needed in terms of an accurate modeling of NO and NO2 dispersion and the CPU time required in a real urban area.
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Beskopylny, Alexey N., Ivan Panfilov, and Besarion Meskhi. "Modeling of Flow Heat Transfer Processes and Aerodynamics in the Cabins of Vehicles." Fluids 7, no. 7 (July 3, 2022): 226. http://dx.doi.org/10.3390/fluids7070226.
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Ensuring comfortable climatic conditions for operators in the cabin of technological machines is an important scientific and technical task affecting operator health. This article implements numerical and analytical modeling of the thermal state of the vehicle cabin, considering external airflow and internal ventilation. A method for calculating the heat transfer coefficients of a multilayer cabin wall for internal and external air under conditions of forced convective heat exchange is proposed. The cabin is located in the external aerodynamic flow to consider the speed and direction of the wind, as well as the speed of traffic. Inside the cabin, the operation of the climate system is modeled as an incoming flow of a given temperature and flow rate. The fields of velocities, pressures, and temperatures are calculated by the method of computer hydrodynamics for the averaged Navier–Stokes equations and the energy equation using the turbulence model. To verify the model, the values of the obtained heat transfer coefficients were compared with three applied theories obtained from experimental data based on dimensionless complexes for averaged velocities and calculated by a numerical method. It is shown that the use of numerical simulation considering the external air domain makes it possible to obtain more accurate results from 5% to 75% compared to applied theories, particularly in areas with large velocity gradients. This method makes it possible to get more accurate values of the heat transfer coefficients than for averaged velocities.
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Carrillo-González, José Gerardo, and Francisco Perez-Martinez. "Explaining the Mean and Variability of Extra Travel Time in Traffic with Interacting Fast and Slow Vehicles." Applied Sciences 12, no. 14 (July 16, 2022): 7176. http://dx.doi.org/10.3390/app12147176.
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In this work, the authors developed procedures to explain mean extra travel time (T) and extra travel time variability (V). This was carried out for situations (through simulations) where the fast vehicles’ travel time, whose speed tendency (sp1) is the permitted speed limit, is negatively affected (i.e., increasing travel time) by the presence of slow vehicles, whose speed tendency (sp2) is half the speed limit. The speed limit was set in the range of 60 km/h to 90 km/h, with seven cases, and every case had eight simulations, each with different p1 (fast vehicles’ percentage) and p2= 100% − p1 (slow vehicles’ percentage) values. p2 ranged from 10% to 80% at intervals of 10%, for a total of 56 simulations. From the simulations’ data, we calculated the fast vehicles’ extra travel time, which is the additional time to traverse an avenue segment owing to the presence of slow vehicles. The fast and slow vehicles recreate heterogenous traffic in terms of speed. We developed procedures for modeling T and V with p2, and V with T. For modeling, ~71.42% of the data from simulations was used. We find that the models’ parameters values can be used for explaining the remaining data. In addition, we discovered that the pattern of p2 vs. V, for p2 ranging from 50% to 80%, is different among sp1 cases and not linear.
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Aldhari, Ibrahim, Meshal Almoshaogeh, Arshad Jamal, Fawaz Alharbi, Majed Alinizzi, and Husnain Haider. "Severity Prediction of Highway Crashes in Saudi Arabia Using Machine Learning Techniques." Applied Sciences 13, no. 1 (December 24, 2022): 233. http://dx.doi.org/10.3390/app13010233.
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Kingdom of Among the G20 countries, Saudi Arabia (KSA) is facing alarming traffic safety issues compared to other G-20 countries. Mitigating the burden of traffic accidents has been identified as a primary focus as part of vision 20230 goals. Driver distraction is the primary cause of increased severity traffic accidents in KSA. In this study, three different machine learning-based severity prediction models were developed and implemented for accident data from the Qassim Province, KSA. Traffic accident data for January 2017 to December 2019 assessment period were obtained from the Ministry of Transport and Logistics Services. Three classifiers, two of which are ensemble machine learning methods, namely random forest, XGBoost, and logistic regression, were used for crash injury severity classification. A resampling technique was used to deal with the problem of bias due to data imbalance issue. SHapley Additive exPlanations (SHAP) analysis interpreted and ranked the factors contributing to crash injury. Two forms of modeling were adopted: multi and binary classification. Among the three models, XGBoost achieved the highest classification accuracy (71%), precision (70%), recall (71%), F1-scores (70%), and area curve (AUC) (0.87) of receiver operating characteristic (ROC) curve when used for multi-category classifications. While adopting the target as a binary classification, XGBoost again outperformed the other classifiers with an accuracy of 94% and an AUC of 0.98. The SHAP results from both global and local interpretations illustrated that the accidents classified under property damage only were primarily categorized by their consequences and the number of vehicles involved. The type of road and lighting conditions were among the other influential factors affecting injury s severity outcome. The death class was classified with respect to temporal parameters, including month and day of the week, as well as road type. Assessing the factors associated with the severe injuries caused by road traffic accidents will assist policymakers in developing safety mitigation strategies in the Qassim Region and other regions of Saudi Arabia.
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Gonzalez Olivardia, Franchesca, Qi Zhang, Tomohito Matsuo, Hikari Shimadera, and Akira Kondo. "Analysis of Pollutant Dispersion in a Realistic Urban Street Canyon Using Coupled CFD and Chemical Reaction Modeling." Atmosphere 10, no. 9 (August 21, 2019): 479. http://dx.doi.org/10.3390/atmos10090479.
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Studies in actual urban settings that integrate chemical reaction modeling, radiation, and particular emissions are mandatory to evaluate the effects of traffic-related air pollution on street canyons. In this paper, airflow patterns and reactive pollutant behavior for over 24 h, in a realistic urban canyon in Osaka City, Japan, was conducted using a computational fluid dynamics (CFD) model coupled with a chemical reaction model (CBM-IV). The boundary conditions for the CFD model were obtained from mesoscale meteorological and air quality models. Inherent street canyon processes, such as ground and wall radiation, were evaluated using a surface energy budget model of the ground and a building envelope model, respectively. The CFD-coupled chemical reaction model surpassed the mesoscale models in describing the NO, NO2, and O3 transport process, representing pollutants concentrations more accurately within the street canyon since the latter cannot capture the local phenomena because of coarse grid resolution. This work showed that the concentration of pollutants in the urban canyon is heavily reliant on roadside emissions and airflow patterns, which, in turn, is strongly affected by the heterogeneity of the urban layout. The CFD-coupled chemical reaction model characterized better the complex three-dimensional site and hour-dependent dispersion of contaminants within an urban canyon.
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Nuterman, Roman, Alexander Mahura, Alexander Baklanov, Bjarne Amstrup, and Ashraf Zakey. "Downscaling system for modeling of atmospheric composition on regional, urban and street scales." Atmospheric Chemistry and Physics 21, no. 14 (July 22, 2021): 11099–112. http://dx.doi.org/10.5194/acp-21-11099-2021.
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Abstract. In this study, the downscaling modeling chain for prediction of weather and atmospheric composition is described and evaluated against observations. The chain consists of interfacing models for forecasting at different spatiotemporal scales that run in a semi-operational mode. The forecasts were performed for European (EU) regional and Danish (DK) subregional-urban scales by the offline coupled numerical weather prediction HIRLAM and atmospheric chemical transport CAMx models, and for Copenhagen city-street scale by the online coupled computational fluid dynamics M2UE model. The results showed elevated NOx and lowered O3 concentrations over major urban, industrial, and transport land and water routes in both the EU and DK domain forecasts. The O3 diurnal cycle predictions in both these domains were equally good, although O3 values were closer to observations for Denmark. At the same time, the DK forecast of NOx and NO2 levels was more biased (with a better prediction score of the diurnal cycle) than the EU forecast, indicating a necessity to adjust emission rates. Further downscaling to the street level (Copenhagen) indicated that the NOx pollution was 2-fold higher on weekends and more than 5 times higher during the working day with high pollution episodes. Despite high uncertainty in road traffic emissions, the street-scale model effectively captured the NOx and NO2 diurnal cycles and the onset of elevated pollution episodes. The demonstrated downscaling system could be used in future online integrated meteorology and air quality research and operational forecasting, as well as for impact assessments on environment, population, and decision making for emergency preparedness and safety measures planning.
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Bychkov, Igor, Alexander Kazakov, Anna Lempert, and Maxim Zharkov. "Modeling of Railway Stations Based on Queuing Networks." Applied Sciences 11, no. 5 (March 9, 2021): 2425. http://dx.doi.org/10.3390/app11052425.
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Among the micro-logistic transport systems, railway stations should be highlighted, such as one of the most important transport infrastructure elements. The efficiency of the transport industry as a whole depends on the quality of their operation. Such systems have a complex multi-level structure, and the incoming traffic flow often has a stochastic character. It is known that the most effective approach to study the operation of such systems is mathematical modeling. Earlier, we proposed an approach to transport hub modeling using multiphase queuing systems with a batch Markovian arrival process (BMAP) as an incoming flow. In this paper, we develop the method by applying more complex models based on queuing networks that allow us to describe in detail the route of requests within an object with a non-linear hierarchical structure. This allows us to increase the adequacy of modeling and explore a new class of objects—freight railway stations and marshalling yards. Here we present mathematical models of two railway stations, one of which is a freight railway station located in Russia, and the other is a marshalling yard in the USA. The models have the form of queuing networks with BMAP flow. They are implemented as simulation software, and a numerical experiment is carried out. Based on the numerical results, some “bottlenecks” in the structure of the studied stations are determined. Moreover, the risk of switching to an irregular mode of operation is assessed. The proposed method is suitable for describing a wide range of cargo and passenger transport systems, including river ports, seaports, airports, and multimodal transport hubs. It allows a primary analysis of the hub operation and does not need large statistical information for parametric identification.
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Gautam, Prashanta, and Abhilash J. Chandy. "A Computational Fluid Dynamics Model for Investigating Air-Pumping Mechanisms in Air-Borne Tire Noise." Tire Science and Technology 44, no. 3 (July 1, 2016): 191–211. http://dx.doi.org/10.2346/tire.16.440304.
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ABSTRACT The reduction in power train noise over the past decade has led to an increased focus in reducing tire/road noise, largely due to the environmental concerns related to road traffic noise in industrial countries. Computational fluid dynamic (CFD) simulations conducted using ANSYS FLUENT are presented here with the objective of understanding air-pumping noise-generation mechanisms due to tire/road interaction. The CFD model employs a large eddy simulation turbulence modeling approach, in which the filtered compressible Navier-Stokes equations are solved to obtain temporally and spatially accurate near-field pressure fluctuations for a two-dimensional (2D) tire geometry with (1) one groove and (2) two grooves. In addition, the Ffowcs-Williams and Hawkings (FW-H) acoustic model is used to predict far-field acoustics. The deformation of the grooves, as the tire rotates, is represented by prescribed sidewall movements. Consequently, the solution to the numerical problem is obtained through a single process, thereby enabling the prediction of small-scale air pumping, horn effect, and far-field acoustics in a single simulation. The acoustic characteristics associated with air pumping are studied through spectral analysis tools, and comparisons show that the additional groove on the horn geometry alters the spectral characteristics of air pumping. Validation of the model is conducted through qualitative and quantitative comparisons with previous studies. These simulations are intended to provide a deeper understanding about the small-scale noise generation as well as the near-field and far-field acoustics, thereby paving the way for the automotive manufacturer to compare a variety of air-related tire noise characteristics without spending time and money for vehicle pass-by tests.
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Han, Young Shin, Bo Seung Kwon, Choon Sung Nam, and Jong Sik Lee. "SLLB-DEVS: An Approach for DEVS Based Modeling of Semiconductor Lithography Load Balance." Applied Sciences 11, no. 9 (May 7, 2021): 4235. http://dx.doi.org/10.3390/app11094235.
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In industrial applications, software related to computational lithography using a DP system method, which refers to how efficiently hardware resources are used, has a significant impact on performance. Because the amount of data to be processed per unit of time is comparatively large in the current semiconductor industry, the efficiency of hardware should be increased through job 12 scheduling by using the most efficient load balancing techniques possible. For efficient scheduling of the load balancer, these are necessary to predict the end time of a given job; this is calculated based on the performance of computing resources and the development of effective traffic distribution algorithms. Due to the high integration of semiconductor chips, the volume of mask exposure data has increased exponentially, the number of slave nodes is increasing, and most EDA tools require one license per DP node to perform a simulation. In this paper, in order to improve efficiency and reduce cost through more efficient load balancing scheduling, a new type of DEVS load balancing method was studied based on the existing industrial E-beam cluster model. The designed DEVS model showed up to four times the throughput of the existing legacy model for medium and large clusters when the BSF policy was applied.
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Kim, Minjun, and Geunhan Kim. "Modeling and Predicting Urban Expansion in South Korea Using Explainable Artificial Intelligence (XAI) Model." Applied Sciences 12, no. 18 (September 13, 2022): 9169. http://dx.doi.org/10.3390/app12189169.
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Over the past few decades, most cities worldwide have experienced a rapid expansion with unprecedented population growth and industrialization. Currently, half of the world’s population is living in urban areas, which only account for less than 1% of the Earth. A rapid and unplanned urban expansion, however, has also resulted in serious challenges to sustainable development of the cities, such as traffic congestion and loss of natural environment and open spaces. This study aims at modeling and predicting the expansion of urban areas in South Korea by utilizing an explainable artificial intelligence (XAI) model. To this end, the study utilized the land-cover maps in 2007 and 2019, as well as several socioeconomic, physical, and environmental attributes. The findings of this study suggest that the urban expansion tends to be promoted when a certain area is close to economically developed area with gentle topography. In addition, the existence of mountainous area and legislative regulations on land use were found to significantly reduce the possibility of urban expansion. Compared to previous studies, this study is novel in that it captures the relative importance of various influencing factors in predicting the urban expansion by integrating the XGBoost model and SHAP values.
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Li, Kenan, and John P. Wilson. "Modeling the Health Benefits of Superblocks across the City of Los Angeles." Applied Sciences 13, no. 4 (February 6, 2023): 2095. http://dx.doi.org/10.3390/app13042095.
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Superblocks can help to transform urban areas into pedestrian-centric neighborhoods. First launched in Barcelona, Spain, they are expected to reduce harmful environmental exposures, increase green space access and thereby provide substantial health benefits. However, few studies have examined the practicality and likely benefits of implementing Superblocks in other metropolitan areas. We developed a methodological framework to build a generalizable City of Los Angeles (LA) Superblocks Model and evaluate the predicted health benefits that would follow such an intervention. We derived and used five rules to guide the choice of arterial streets and candidate blocks and the choice of major bounding streets that could facilitate mobility across the metropolitan area. We next used the BenMap-CE model to perform a quantitative assessment of the health and economic benefits that would accompany five scenarios that would transform 5–50% of the residential areas in the City of LA to Superblocks. We found that the creation of superblocks resulted in significant reductions in hospital admissions and significant economic savings. The benefits were strongest when 5–10% of residential areas were transformed, but rapidly decreased as the threshold reached 30%. These results will help stakeholders determine the optimal balance between reduced car traffic and improved health outcomes. Moreover, we illustrated how to develop a Superblocks model for a highly versatile and populated metropolitan area like the City of LA and how the model can be used to assess the potential health benefits and benchmark the relationship between the scale of the Superblock implementation and the accompanying health benefits moving forward.
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Ma, Siqi, Xin Wang, Xiaochen Wang, Hanyu Liu, and Runtong Zhang. "A Framework for Diagnosing Urban Rail Train Turn-Back Faults Based on Rules and Algorithms." Applied Sciences 11, no. 8 (April 8, 2021): 3347. http://dx.doi.org/10.3390/app11083347.
Full textAbstract:
Although urban rail transit provides significant daily assistance to users, traffic risk remains. Turn-back faults are a common cause of traffic accidents. To address turn-back faults, machines are able to learn the complicated and detailed rules of the train’s internal communication codes, and engineers must understand simple external features for quick judgment. Focusing on turn-back faults in urban rail, in this study we took advantage of related accumulated data to improve algorithmic and human diagnosis of this kind of fault. In detail, we first designed a novel framework combining rules and algorithms to help humans and machines understand the fault characteristics and collaborate in fault diagnosis, including determining the category to which the turn-back fault belongs, and identifying the simple and complicated judgment rules involved. Then, we established a dataset including tabular and text data for real application scenarios and carried out corresponding analysis of fault rule generation, diagnostic classification, and topic modeling. Finally, we present the fault characteristics under the proposed framework. Qualitative and quantitative experiments were performed to evaluate the proposed method, and the experimental results show that (1) the framework is helpful in understanding the faults of trains that occur in three types of turn-back: automatic turn-back (ATB), automatic end change (AEC), and point mode end change (PEC); (2) our proposed framework can assist in diagnosing turn-back faults.
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Buidin, Thomas Imre Cyrille, and Florin Mariasiu. "Modeling Approach of an Air-Based Battery Thermal Management System for an Electric Vehicle." Applied Sciences 11, no. 15 (July 31, 2021): 7089. http://dx.doi.org/10.3390/app11157089.
Full textAbstract:
The battery thermal management system is one of the important systems of an electric vehicle with direct effects on its performance. In this regard, this paper proposes a mathematical model that increases the accuracy of data obtained by numerical analysis of the temperature inside battery packs. The activity of the design and development (as accurate as possible) of a battery pack leads to an increase in the life of the battery cells and of the energetic efficiency of the electric vehicle in the specific operating conditions of road traffic. The research methodology of the thermal phenomenon in the battery pack, presented by the authors, is based on an efficient co-simulation concept consisting of steady-state CFD simulations and transient 1D simulations using a new mathematical model for the thermal behavior of a lithium-ion (Li-ion) cylindrical battery and applied in a battery pack’s forced air cooling thermal management system. Comparing the obtained results, it was found that the use of the model provides more accurate calculations of the local thermal performance of the air cooling system, with a direct influence on optimizing its design and construction. It is also highlighted that using the proposed model for higher heat transfer coefficient values (increase in air flow), offers more accurate data compared to other models, with immediate benefits in the proper design and development of the battery’s thermal management system.
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Zeng, Yangyang, Jiayi Li, Shiyun Lin, Xiaolong He, Bing Li, and Tao Deng. "Comparison of Manual Setting Weight Reduction and Topology Optimization of the Wing Tips of Electric Vertical Take-Off and Landing Aircraft." Applied Sciences 12, no. 11 (May 30, 2022): 5548. http://dx.doi.org/10.3390/app12115548.
Full textAbstract:
Urban air mobility aircraft are considered an effective solution to traffic jams. These particular aircraft come with different designs and are very different from traditional aircraft. An effective weight reduction method is required to enable ongoing design validation and verification. This paper presents the design of an aircraft with a high space utilization rate of the take-off and landing stage, which allows vertical take-off and landing and has long battery life. For the components fixed at the wing tips and used for installing ducted fans and based on the fused deposition modeling (FDM) process, this paper puts forward two methods to carry out the lightweight design according to the stress conditions of the aircraft, namely, the manual setting the internal structure and the topology optimization. The results show that when the force on the component is small, manual setting is very effective for the purpose of weight reduction, which can also improve the manufacturability of the aircraft.
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Masser, Robin, and Karl Heinz Hoffmann. "Optimal Control for a Hydraulic Recuperation System Using Endoreversible Thermodynamics." Applied Sciences 11, no. 11 (May 28, 2021): 5001. http://dx.doi.org/10.3390/app11115001.
Full textAbstract:
Energy savings in the traffic sector are of considerable importance for economic and environmental considerations. Recuperation of mechanical energy in commercial vehicles can contribute to this goal. One promising technology rests on hydraulic systems, in particular for trucks which use such system also for other purposes such as lifting cargo or operating a crane. In this work the potential for energy savings is analyzed for commercial vehicles with tipper bodies, as these already have a hydraulic onboard system. The recuperation system is modeled based on endoreversible thermodynamics, thus providing a framework in which realistic driving data can be incorporated. We further used dissipative engine setups for modeling both the hydraulic and combustion engine of the hybrid drive train in order to include realistic efficiency maps. As a result, reduction in fuel consumption of up to 26% as compared to a simple baseline recuperation strategy can be achieved with an optimized recuperation control.