Traffic Modeling and Simulation

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Background Traffic Modeling for Large-Scale Network Simulation

Background Traffic Modeling for Large-Scale Network Simulation

work focuses on addressing the challenges facing the background traffic modeling for large-scale network simulation. There have been numerous attempts to model the background traffic for large-scale network study. Unfortunately, there are important issues left to be addressed for mod- eling and simulating background traffic for large-scale network simulation. First, it is critical to preserve the important realistic characteristics of the real network traffic in the background traffic models for network experimentation. Floyd and Kohler [FK03] showed through a series of counter examples that realistic traffic characterization (in- cluding, for example, proper traffic load distribution and bidirectional traffic) plays an important role in determining the correct outcome of the simulation studies. The same observation has been confirmed in the performance studies of high-speed TCP protocols. Ha et al. [HLRX07] demonstrated conclusively that the stability, fairness, and conver- gence speed of several TCP variants are clearly affected by the intensity and variability of background traffic. In addition, Vishwanath and Vahdat conducted a more systematic study on the impact of background traffic on distributed systems, including Web applica- tions, multimedia video streaming applications, and bandwidth estimation tools [VV08]. They concluded that even small differences in the burstiness of background traffic can lead to drastic changes in the overall application behavior. Therefore, how to preserve the essential characteristics of the real traffic is one main challenge of modeling the back- ground traffic.
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Modeling of Freeway Traffic

Modeling of Freeway Traffic

Commonly, the simulation of traffic flow bases on microscopic, macroscopic or mesoscopic traffic modeling. A microscopic modeling featuring descriptive rules is developed. A macroscopic mode- ling consisting of Navier–Stokes–like equations is considered. An integration of the microscopic modeling in any macroscopic modeling leads to a novel rule–based mesoscopic modeling. The three different modelings are numerically approximated by different numerical methods. In parti- cular, the finite element method can successfully be applied to the macroscopic modeling. The simulation programs evolved from implementations of numerical approximations of the three modelings are verified for usefulness in perturbation analysis and comparison of simulation results with detector data. Both the microscopic and the macroscopic simulation are able to reproduce typi- cal traffic phenomena like traffic jams or stop–and–go waves. Choosing a suitable velocity–distan- ce–relation the mesoscopic simulation proves a consistent link between microscopic and macros- copic simulations. The velocity–distance–relation respectively the velocity–density–relation are the decisive parameters of the shown simulations. Due to changes of these parameters, the simula- tion reacts very sensitively.
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Traffic Modeling and Importing Traffic

Traffic Modeling and Importing Traffic

NETWARS provides several options for representing, generating, and simulating traffic. Understanding the available options and selecting the appropriate traffic modeling technique is crucial to simulation performance. This user’s guide, Traffic Modeling and Importing Traffic, describes how you can tune the fidelity of traffic being modeled using different traffic representations. How you choose to model traffic depends on the type of study being done.

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Modeling and Simulation of Traffic flow: A Case Study - First Ring Road in Downtown Madinah

Modeling and Simulation of Traffic flow: A Case Study - First Ring Road in Downtown Madinah

Some other work related to traffic modeling include the work of Li and Lu who presented a model for a new short-term freeway traffic flow predication based on combined Neural Network (NN) approach consisting of self-organizing feature map (SOM) and Elman NN, where the SOM was used to classify the traffic condition and the Elman NN identified the relationships between input and output in order to produce the prediction value. As a case study, performance of this model was estimated using real observation data from a freeway in Beijing, China (Li and Lu, 2009). The work in (Xu, Kong, Lin, and Liu, 2012) proposed a spatial prediction approach based on a macroscopic urban traffic network model. This work concentrated on mechanism of vehicles transmission on road segments and the spatial model of the network in addition a speed density model was used to acquire the vehicle travel time in the network. Then, traffic simulation software, CORSIM was used to simulate the real urban traffic. The simulation results of the software produced the effective prediction timings in particular during rush hours and sudden change in traffic states. The work of Jin presented a link queue model of network traffic flow where the changes in the link density describe the different congestion levels on a road link. This model could capture some features of the link traffic flow such as capacity, jam density free-flow speed. When incorporating a junction flux functions it could also describe the propagation, initiation and dissipation of traffic queues within a road network which might be caused by different types of bottlenecks (Jin, 2013).
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Forecasting of Traffic Simulation Model Under Heterogeneous Traffic Condition

Forecasting of Traffic Simulation Model Under Heterogeneous Traffic Condition

The test site for the study is four-lane divided National Highway (Nagpur-Amravati NH-06) in India is selected. A longitudinal stretch of 50 meters is marked on the carriageway of the highway. A video recording of this section is done for morning, evening, and off-peak hour of normal weekdays. Traffic data is then extracted from the video for 5-minute interval and composition. All the vehicles are classified into seven categories namely Car, Bus, Two-Wheeler, Three-Wheeler, LCV, MAV, Truck. Physical dimensions of vehicles and their proportion at highway is shown in table-A-1. The speed of the vehicles in traffic stream is determined by using speed-distance formula at the highway and also, using a stop watch having accuracy 0.01 second. The speed of the different vehicles is shown in table-A-2. Vehicle characteristics at different carriageway shown in table A- 3. The PCU values of a vehicles change from vehicle to vehicle and it affects the capacity of the road. In order to know the effect of lane width or carriageway width on the capacity of the road, the capacity of a two-lane road with different carriageway are collected shown in Table.A-4. and Table.A-5. The capacity of road and carriageway are plotted shown in Fig.1.0. From the graph it was found that the relationship between width
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Introduction to Modeling and Simulation

Introduction to Modeling and Simulation

1 WHAT IS MODELING? Modeling is the process of producing a model; a model is a representation of the construction and working of some system of interest. A model is similar to but simpler than the system it represents. One purpose of a model is to enable the analyst to predict the effect of changes to the system. On the one hand, a model should be a close approximation to the real system and incorporate most of its salient features. On the other hand, it should not be so complex that it is impossible to understand and experiment with it. A good model is a judicious tradeoff between realism and simplicity. Simulation practitioners recommend increasing the complexity of a model iteratively. An important issue in modeling is model validity. Model validation techniques include simulating the model under known input conditions and comparing model output with system output.
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Modeling and Simulation of Varistors

Modeling and Simulation of Varistors

The topics of research group FOR 575 include the research on means to filter traveling waves emanating from insulated-gate bipolar transistor (IGBT) inverters. One approach is the embedding of microvaristor materials into the insulation of wires to achieve a nonlinear resistive stress grading. As microvaristors are not fundamentally different from normal varistors, they can be simulated in the same way as normal varistor materials. However, it became apparent that the knowledge of their material properties was insufficient and that, prior to any simulation, one had to find a way to improve the quality of the material models. For that reason, a novel approach has been developed for the characterization of the nonlinear electrical properties, which is described in Ch. 5 along with the resulting observations for a specific microvaristor material.
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Comparative Study of Traffic Simulation Model For Mix Traffic Condition

Comparative Study of Traffic Simulation Model For Mix Traffic Condition

With the continuous increase in vehicle on urban roads the congestion also increases and it became the major concern that how to maintain free flow speed of traffic. The traffic in India is highly heterogeneous with different classes of vehicle and no lane discipline. To avoid the congestion we must require proper and channelized traffic and it can be achieved by developing a microscopic model by using computer program such as simulation. Simulation is the mirroring of the operation of a real world process or system. To simulate something firstly we require to develop model and this model represent the behavior or function of the selected process. Traffic flow phenomenon consist of a wide range of complex activity, speed of the travel, lane discipline, overtaking and crossing logic, gap acceptance, acceleration and deceleration etc. The characteristics of traffic flow depend upon various factor like road features, vehicle performance characteristic and road user behaviour. To understand the phenomena of traffic flow behaviour, it can be achieved by observing movement of vehicle in traffic stream and collect data and synthesise the flow characteristic through analytical or mathematical model. Simulation model can be developing to understand the phenomena of traffic flow and to reduce the problem commonly met with in road traffic and transportation. Some of these are: development of speed flow relationship, prediction of fuel consumption of vehicle, gap acceptance problem in the design of intersection, queuing problem, bus route schedule, accident occurrence etc. This study can contribute to an improved understanding of traffic flow on Indian expressway.
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Analysis of the traffic behavior of emergency vehicles in a microscopic traffic simulation

Analysis of the traffic behavior of emergency vehicles in a microscopic traffic simulation

Interestingly, all simulation models are over estimating the average speed the vehicle could pass an intersection. One reason for this is that there might be not enough pedestrians simulated to block the vehicles. Additionally, human drivers might drive more carefully to have more time to react to unforeseen events. The traffic simulation has full knowledge of all traffic participants and knows that no pedestrian is close, but in real world drivers have to stop or reduce their speed first to make sure that there is no other traffic participant in blind spot. Another factor is: vehicles in SUMO are not reducing their speed while driving curves in contrast to human drivers would do. This aspect can also be seen in evaluation results in Figure 5. The speed differences between human drivers and the simulation is much higher for right turning vehicles. The passing times the vehicles needed to cross the intersection are stated in Figure 6. As expected, the results are similar to the evaluation of average speeds at the intersection. It can be seen that emergency vehicles are saving around one second while turning right at an intersection. But if the emergency vehicle just follow the main road the spreading of the data is much larger than for private vehicles. In addition, there are many outlines: some emergency vehicle needed over 60 seconds to cross the intersection while normal vehicles normally need less than 10 seconds. There could be different reasons for this:
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Modeling and Simulation of Microgrid

Modeling and Simulation of Microgrid

a Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO 65401 b System Engineering, Missouri University of Science and Technology, Rolla, MO 65401 Abstract Complex computer systems and electric power grids share many properties of how they behave and how they are structured. A microgrid is a smaller electric grid that contains several homes, energy storage units, and distributed generators. The main idea behind microgrids is the ability to work even if the main grid is not supplying power. That is, the energy storage unit and distributed generation will supply power in that case, and if there is excess in power production from renewable energy sources, it will go to the energy storage unit. Therefore, the electric grid becomes decentralized in terms of control and production. To deal with this change, one needs to interpret the electrical grid as a system of systems (SoS) and build new models that capture the dynamic behavior of the microgrid. In this paper, different models of electric components in a microgrid are presented. These models use complex system modeling techniques such as agent-based methods and system dynamics, or a combination of different methods to represent various electric elements. Examples show the simulation of the solar microgrid is presented to show the emergent properties of the interconnected system. Results and waveforms are discussed.
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Analysis and modeling of YouTube traffic

Analysis and modeling of YouTube traffic

The experimental test bed used for this validation was designed to collect data traffic traces of downloads from the original YouTube server as well as from our YouTube emulation server. For the first case, we reused the framework of section 3. For the second, the playback monitor tool (see section 3.1) installed in our PC was enabled to access the YouTube emulation server that was located in another computer within the same Local Area Network (LAN). The emulation server was implemented with the pseudo-code of Figure 11 in Java, and upon request of the web browser of the Playback Monitor Tool, it starts feeding the TCP socket with the FLV contents at the pace indicated by the proposed traffic model. For downloads from the original YouTube server and from the emulated server, the available bandwidth of the PC's network connection was limited to 5 Mbps. Without a common limit, the connection to the emulated server through the LAN was expected to provide a much higher available bandwidth than the connection through the Internet to the original YouTube server. This would cause a bias in the time required to download the initial burst or to recover after a bandwidth limitation period, which would impede the comparison between the download traces from the original YouTube server and from the emulated one.
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Traffic Games: Modeling Freeway Traffic with Game Theory

Traffic Games: Modeling Freeway Traffic with Game Theory

The results of our model for the evolution of cooperation ( Fig 5 to Fig 8 ) show that cooperative behavior may succeed only for certain values of the traffic density. It is worth noting that for many of the behaviors modeled these density values correspond to those in which the synchro- nized flow phase takes place ( Fig 10 ). It is important to remember that unlike other models [ 11 ], the cooperative or politeness degree exhibited by the drivers is internally and individually chosen by the agents themselves. There is no global variable, nor parameter, to guide their behavior. The coincidence between the large increase in the fraction of cooperators for certain density values implies that only during the synchronized phase are conditions for drivers’ self- organization present. Despite the selfish nature of the payoff matrices (drivers are rewarded by increasing their speed and punished by slowing down) the drivers found a circumstance in which not pursuing a velocity increase (cooperative behavior) ended up rewarding them. This can be seen as an example of the slower-is-faster effect [ 35 ].
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Notes on using simulation-optimization techniques in traffic simulation

Notes on using simulation-optimization techniques in traffic simulation

the supply becomes the main concern. Such would be the case for freeways and motorways, where route choice is not an issue because for each entry ramp there is only one path to every exit ramp, with each route being unique. This is the case addressed in this paper. Calibrating the supply is namely a problem of finding the appropriate values for both the car-following and lane-changing parameters. Car-following models aim to reproduce traffic flows by reproducing the dynamics of car pairs (i.e., leaders and followers) and describing how each follower adapts its behavior to changes in its leader’s behavior. In essence a car-following model is composed of a binomial: a mechanical entity, the car (with certain physical characteristics, e.g., maximum acceleration/deceleration capabilities, vehicle length, etc.); and a human driver whose behavioral characteristics are described by parameters such as desired speed, minimum headway, sensitivity to a stimulus, etc. From a formal point of view, car-following models can be formulated as instances of a follower’s acceleration law, (Wilson and Ward, 2010):
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Modeling and Simulation in HYSYS

Modeling and Simulation in HYSYS

Process simulation is used or the design, analysis and optimization of the process such as chemical plant, chemical processes, environmental systems, power stations, complex manufacturing operations, biological processes, and similar technical functions. The software has to solve the mass and energy balance to find a stable operating point. The goal of a process simulation is to find optimal conditions for an examined process . This is essentially an optimization problem which has to be solved in an iterative process. The model used in the process simulation is the approximation and assumption but it gives the wide range of the temperature and pressure data which is not covered in the real data. Models also allow interpolation and extrapolation - within certain limits - and enable the search for conditions outside the range of known properties.
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A Modeling & Simulation Framework for Large-Scale Simulation

A Modeling & Simulation Framework for Large-Scale Simulation

This paper presents the architecture of a modeling and simulation environment, in which simulation components are composed to construct the simulation application. This simulation environment provides common simulation services and offers every chance to reuse exited resources to simplify development of complex simulation systems. By using of the runtime object database and software adapters including HLA-DEVS and DEVS-DIS agents, this environment can be compatible to many distributed interoperation protocols, such as HLA, DIS et cetera. By composing simulation services, this simulation environment can be tailored and specified to meet the needs of certain simulation applications.
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Simulation of CPRI traffic on Optical Ethernet

Simulation of CPRI traffic on Optical Ethernet

The novel concept of CRAN relies on this link to exchange data between the REC and the RE [13]. This is done by exchanging the digitized radio signals by means of high bandwidth constant bitrate traffic flows. As opposed to the Fronthaul, the traditional packet based backhaul is the link that exists between a base station, or a BBU, and the core of the mobile network. Traditionally, it consists of a coax or fiber cable, or in some cases employs proprietary wireless links [7]. Fronthaul, Backhaul, and varied hybrid architectures are required to satisfy cost efficient, dense deployment, backward compatibility, and low latency demands for future networks [14].
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TRAFFIC SIMULATION SOFTWARE COMPARISON STUDY

TRAFFIC SIMULATION SOFTWARE COMPARISON STUDY

alternatives. In order to examine the impacts of system alternatives in greater detail (e.g., highway access, interchange configuration, lane geometry), the Regional Planning Commission of Greater Birmingham (RPCGB) expressed interest in exploring the use of microscopic traffic simulation models. This project compared three commercially available traffic simulation software packages: CORSIM (version 4.32), SimTraffic (version 5.0), and AIMSUN (version 4.2). Each simulation package was evaluated using the following corridor “types:” Interstate, Signalized Principal Arterial, and an Urban Collector. Each package was evaluated according to criteria that included: system requirements, ease of coding, data requirements, relevance/accuracy of performance measures reported in the output, and versatility/expandability (intelligent transportation systems evaluations, incident management, HOV facilities, ramp metering, etc.). The results indicate that all three models can provide reasonable simulations of traffic operations, although they each offer different capabilities and require varying levels of effort to code, debug, and calibrate. SimTraffic, CORSIM, and AIRSUN each have applications to which they are particularly well-suited, and the RPCGB may want to consider a combination of models to address their planning needs.
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Microscopic Simulation of Traffic at a Suburban Interchange

Microscopic Simulation of Traffic at a Suburban Interchange

Accurate microscopic simulation of traffic and traffic control within VISSIM requires that a detailed network be created. VISSIM employs a "link" based network system, where each link is coded with attributes such as number of lanes, lane width, and gradient. All network inputs are via a graphical user interface that allows the user to build the network by inserting a series of links and connectors. Figure 3 shows a typical VISSIM screen shot. Although "freehand" drafting of the network is possible with VISSIM, network modeling is mo re efficient when an air photograph or line drawing is used as a background or underlay. Network links are joined by connectors as shown in thereby eliminating the need for "nodes" that are used in many other traffic network software packages.
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Traffic Data Analysis on Sathorn Road with Synchro Optimization and Traffic Simulation

Traffic Data Analysis on Sathorn Road with Synchro Optimization and Traffic Simulation

The purposes of this paper are to study and optimize traffic signal timing for each intersection on Sathorn Road: Sathorn-Surasak, Narinthorn, and Wittayu intersections. The results can assist the usage of signal control and further educate traffic police during critical periods along with reducing travel time for each vehicle on the road network [10], [11]. Moreover, since Sathorn district can be considered as CBD in Bangkok, there are huge daily incoming flows every working-day morning. As a result, a reversible lane scenario has been proposed. However, in order to implement the reversible lane, traffic lights must be coordinated, especially at Narinthorn intersection, where there are two traffic lights within a short distance. This case is investigated in this work as well, which time offset of two traffic lights is the key result [8]. Furthermore, to strengthen the validity of the analysis, real data from the field are used. In particular, the effect of motorcycles [12]-[14], typical to Bangkok, is considered. Finally, outputs from Synchro in a form of optimal green times are applied into a microscopic traffic simulation to further analyze the outcomes. While there exists several types of microscopic simulators [15], ‘SUMO’ [6] is chosen. SUMO is an integral part of the Sathorn Model project, because it is an open source simulator which allows the development to be done in various ways. SUMO is also calibrated to Sathorn traffic based on data from the field. Subsequently, the results have shown some improvement for travel time.
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Traffic Modeling in Mobile Communication Networks

Traffic Modeling in Mobile Communication Networks

The mobile communication network is faced with serious challenges to ensure good Quality of Service (QOS).The Good QOS in mobile communication network is necessary at this, presence economic situation of global economic meltdown and competitive business environment with low tariff. In order to survive in this present economic situation, the network operators must put into consideration the effective use of their available resources, which leads to effective network design and network planning. The parameters used for effective network design and network planning are call blocking probability, handover blocking probability etc. These parameters operate base on available resource and traffic load in erlang in the mobile communication network. The block probability is used to control the number of block call experience in the mobile network and these block calls arises from lack of network capacity (channels) to accommodate or carry all the call at a particular point in time (Jangir H.et al, 2000; Marco A.et al, 2003).The blocking probability is determined from the number of available channels and traffic load in erlang. In evaluating the performance of handover blocking probability the following parameters must be consider as follows, handover rate, dropping probability, handover probability, call holding time and channel holding time (Yuguang F.,2005;Madhusmita P.et al,2008).The call dropping probability, it the probability that calls that originally (initially) granted access to the network channels (switches), but due to technical error(such are Electromagnetic causes, irregular user behavior etc) the calls are truncated during conversation (also called the forced termination), which is closely related to handover blocking probability. The handover rate is used to determine the handover traffic arrival rate, which is also needed to find the call blocking probability. The channel holding time is determined by the cell residence time (cell dwell time).The cell residence is affected by the subscriber mobility, the geographic situation and the channel allocation schemes used (or other factors such as fading). Channel holding time, this is the time a Mobile Station (MS) remains in the same cell during a call, while call holding time is the total call duration time when MS move from a cell to another new cell, the process is called handover process (cellular traffic-Wikipedia; Stefano et al,2008).
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