Top PDF Collection and Analysis of data for Inter-domain Traffic Engineering

Collection and Analysis of data for Inter-domain Traffic Engineering

Collection and Analysis of data for Inter-domain Traffic Engineering

Network operators must control the distribution of traffic crossing their network, in order to provide highly available and efficient services. Traffic engineering (TE) is referred to as the set of techniques and tools that operators utilize to this end [Awduche et al. 2002]. For the case of Internet Service Providers (ISPs), TE processes can be divided in two types: the management of how traffic flows within their own network infrastructure (intra- domain TE), and the control on where traffic enters and where traffic exits onto neighbor- ing networks (inter-domain TE). These two types are very different from an operational point of view. For the intra-domain case, operators have complete control of the protocols and configuration of the devices of the network. Inter-domain methods, on the other hand, must consider the policies of external, sometimes remote, Autonomous Systems (ASes). In inter-domain TE, operators should not only be concerned by how to route traffic, but also by how to assess the efficiency of their strategies and the impact of the policies of external ASes on them.
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A traffic data collection and analysis method based on wireless sensor network

A traffic data collection and analysis method based on wireless sensor network

In this paper, a WSN-based traffic data collection scheme is proposed, which is low cost and low power. The scheme can collect vehicle speed and position infor- mation accurately and timely and lays a foundation for traffic flow analysis. Then a method based on incremen- tal noise addition is proposed for the chaotic identifica- tion of traffic flow signal. PPS is used to add noise incrementally to the analyzed signal, and the MD mean value is used as a measurement for the complexity of the signal. It is found that, for different types of signals, the complexity trends of surrogate data under each noise in- tensity are different. For the periodic signal, when the noise intensity is small, the MD mean value of surrogate data is stable; when the noise intensity is larger than a threshold, the MD mean value starts to decrease grad- ually. For the chaotic signal, such as traffic flow, the first MD mean value is obviously smaller than the periodic signal, and as the increase of noise intensity, the MD mean value keeps decreasing monotonously. For the ran- dom signal, the MD mean value keeps at a low value. Therefore, as the noise intensity increases, the trend of MD mean values is an effective criterion for distinguish- ing various types of signals. Of course, although param- eter A is used to try to quantify the criteria presented in this paper, the criterion is more inclined to a qualitative criterion than a quantitative one. Further research is needed to make extensive use of the criterion.
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Data Collection and Analysis in the Air Travel Planning Domain

Data Collection and Analysis in the Air Travel Planning Domain

DATA COLLECTION AND ANALYSIS IN THE AIR TRAVEL PLANNING DOMAIN D A T A C O L L E C T I O N A N D A N A L Y S I S IN T H E AIR T R A V E L P L A N N I N G D O M A I N Jacqueline C Kowtko, Patti J Price[.]

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<p>A narrative review of data collection and analysis guidelines for comparative effectiveness research in chronic pain using patient-reported outcomes and electronic health records</p>

<p>A narrative review of data collection and analysis guidelines for comparative effectiveness research in chronic pain using patient-reported outcomes and electronic health records</p>

and multisite project clinical teams utilize comprehensive age- and disease-specific measures that are implemented as widely as possible and checked regularly for reliability. PBE data are merged into a central study database for analysis and hypothesis testing. Study findings are then implemented into clinical practice for validation testing with the ultimate goal of integration into standard care. PBE studies are designed to improve on traditional observational studies by 1) examining large, diverse patient populations; 2) involving clinicians in the research design and data collection; 3) using carefully selected patient characteristics for analysis to avoid bias; and 4) standardizing data collection and treatment documentation at all research sites. PBE methodology is ideal for conducting “pragmatic” trials that are designed to measure the overall benefit produced by a treatment in a naturalistic clinical setting. 9 Reviews in the pain medicine literature indicate
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Wind Data Collection and Analysis in Kumasi

Wind Data Collection and Analysis in Kumasi

The height of the anemometer was 5 m above the rooftop and 20 m above the ground while the wind vane was about 4.90 m above the rooftop and about 19.90 m above the ground. The height of the wind instruments above the rooftop of the building was specifically chosen and was not up to the standard meteorological height of 10 m due to infrastructural constraints. As a result of this the anemometer and the vane used for the measurement were engulfed by turbulent wind flow which affects the sensitivity of the wind instruments. Seven months internally binned wind speeds and directions (Captured on the display pages of a Wind Explorer™) were organized and made available for analysis due to time constraints while only the last three months time-series wind data (stored on a 128 KByte DataPlug) was made available for analysis due to accidental erasing of data from the DataPlug. The raw wind data stored on the DataPlug for the first four months of the measurement period was erased. Stata software was used for the analysis of the internally binned wind data while the Wind climate Analyst component of WAsP and the Microsoft Excel Software were separately used for the analysis of the three month time-series wind data. The full WAsP software could not be used for the analysis of the three month wind data because the wind data which was retrieved from the DataPlug was limited in size (minimum recommended size of wind data for analysis by the full WAsP software is one year). No climate-adjustment was done. However, the wind measurement campaign for this research work was carried out for one year and as a result a one year internally binned monthly average wind speeds and wind directions were captured on the display pages of the Wind Explorer™.
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Trip Generation Characteristics of Special Generator

Trip Generation Characteristics of Special Generator

Transportation forecasting is the attempt of estimating the number of vehicles or people that will use a specific transportation facility in the future. For instance, a forecast may estimate the number of vehicles on a planned road or bridge, the ridership on a railway line, the number of passengers visiting an airport, or the number of ships calling on a seaport. Traffic forecasting begins with the collection of data on current traffic. This traffic data is combined with other known data, such as population, employment, trip rates, travel costs, etc., to develop a traffic demand model for the current situation. Traffic forecasts are used for several key purposes in transportation policy, planning, and engineering: to calculate the capacity of infrastructure, e.g., how many lanes a bridge should have; to estimate the financial and social viability of projects, e.g., using cost–benefit analysis and social impact assessment; and to calculate environmental impacts, e.g., air pollution and noise. The four steps of the classical urban transportation planning system model are:
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Implementing Divide and Conquer Technique for a Big-Data Traffic

Implementing Divide and Conquer Technique for a Big-Data Traffic

Real Time Analytic Processing (RTAP) in this modern world is inducing huge data traffic by everyone knowingly or unknowingly compared to few years back data traffic where only few companies was source of data traffic. This is really a challenge to the technology and needs a solution which can be implemented at the earliest and with an ease. This paper tries to discuss about the solution for handling the Big-Data traffic without downgrading the processing time. However, the analysis of big data can be troublesome because of its heterogeneous nature i.e. Big-data often involves the collection and storage of mixed data based on different patterns or rules. This has made the heterogeneous mixture property of data a very important issue. This paper focuses on applying “Divide and Conquer Technique” to handle the Big-data traffic using parallel processing in Network”
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ROADWAY CAPACITY ESTIMATION BY MICROSIMULATION MODELLING

ROADWAY CAPACITY ESTIMATION BY MICROSIMULATION MODELLING

Transportation network is confronted with many complex problems. Capacity estimation is a dynamic problem. Vehicle density is increasing every year and is leading to congestion on many roads. Vehicle shapes and characteristics are changing rapidly. Therefore study of road traffic essentials like planning, designing of roadway, operations of roadway facilities along with control and regulation become necessary. On field observations and data collection and analysis is tedious and time consuming. Broad spectrum collection of data collection is therefore very difficult. Modelling of traffic flow is a time-tested alternate.
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Performance Analysis of Multimedia Traffic over MPLS Communication Networks with Traffic Engineering

Performance Analysis of Multimedia Traffic over MPLS Communication Networks with Traffic Engineering

Internet Protocol (IP) allows a global network among an endless mixture of systems and transmission media [2][7].The IP was created as a connectionless network layer protocol that makes no attempt to discriminate between various application types [6][9]. The main function of IP is to send the data from the source to destination. Data is constructed as a series of packets. All the packets are routed through a chain of routers and multiple networks to reach the destination. In the Internet, router takes independent decision on each incoming packet. When a packet arrives at a router, the router has to consult its routing table to find the next hop for that packet based on the packets destination address in the packets IP header (longest match prefix lookup), as explained in Fig. 1 [2][10][11]. To build routing tables each router runs IP routing protocols like Border Gateway Protocol (BGP), Open Shortest Path First (OSPF) or Intermediate System-to-Intermediate System (IS-IS). When a packet traverses through the network, each router performs the same steps of finding the next hop for the packet until it reach the destination [7][12]. As a result traffic is concentrate across a smaller
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Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain

Exploring Knowledge Engineering Strategies in Designing and Modelling a Road Traffic Accident Management Domain

With respect to bug identification and removal, in the hand- coded method, all but syntactic bugs were dealt with by dy- namic testing of the model. Most of the development time was spent in dynamic testing: analysing produced plans, iden- tifying bugs and removing them from the model. While hand encoding a domain, usually many issues are noticed only by carefully reading the generated plans. One example of bug identification is when the team of method A noticed broken vehicles were being delivered to hospitals, instead of garages. Removing the bug in this case amounted to adding further constraints to the operators. On the other hand, most of the time spent with itSIMPLE was in designing classes of ob- jects and defining legal interactions between them. After that, only a relatively short time is required for debugging. How- ever, we found that where debugging was required, it was initiated through dynamic testing: while the structure of the model helps in its development and maintenance, it is the failure of a planning engine to solve a goal which alerts the developer to the presence of a bug, in most cases. This is per- haps a failing peculiar to itSIMPLE, as there are systems with stronger static tests (such as GIPO [Simpson et al., 2007]) which are capable of identifying bugs at an earlier stage than dynamic testing. The need for static tests is reduced, however, given the structure imposed by the UML method; additionally this helps determine the completeness of the model, in terms of classes and finite state machines. Also, itSIMPLE’s auto- mated generation of the PDDL model, much like compilation of a high level language into a low level language, has the benefit of eliminating human errors in encoding details. The tool offers the modellers a range of third party planners for generating plans, along with features such as plan analysis, where the plan generated can be viewed graphically.
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Traffic Impact Assessment of Land Use Proposals: Fifty Years of Australian Experience

Traffic Impact Assessment of Land Use Proposals: Fifty Years of Australian Experience

A Traffic Impact Assessment (TIA) provides information on the projected traffic expected from a proposed development. A TIA also evaluates the impact of proposed development on the roadways in the immediate proximity of the proposed development. The TIA should identify potential traffic operational problems or concerns and recommend appropriate actions to reduce impacts. Draft traffic impact assessment (TIA) guidelines for various land uses in general, and for high traffic generating buildings in particular, were proposed in Iran in the middle of 2016. In formulating that report the Traffic Committee of the Iranian Building Engineering Order reviewed international experience, including that of Australia. Traffic impact assessment started in Australia in the 1960s when the traffic police exercised much authority in trying to prevent land-use developments from taking place abutting main roads. Concepts derived from queuing theory where the application of the mean service time to establish maximum traffic generation rates for a proposed development was introduced. Attached to that theory was the absorption capacity of a priority traffic stream. This latter one found its way into traffic engineering practice to provide a rational basis for assessing development applications. As experience accumulated the guidelines became refined and a second edition of Austroads’ traffic impact assessment was released in 2016. This paper presents the documentation which is expected from a developer, traffic models available to the consultants working for the developer, and importance of developer contributions require by the developer for changes to the surrounding transport network. To illustrate the process, a simple case study of a school expansion is provided. The steps taken to show the impact of an increase from 185 to 600 pupils in Sydney is illustrated. The process include traffic data collection on the surrounding streets to forecasting future traffic using appropriate models and community workshops. In the light of a practical case, the process then ends with proposing options to mitigate adverse traffic impacts of the development application. The overall work illustrates the principles of TIA and to point to the onus on all developers to manage travel demands to achieve more sustainable outcomes from urban development.
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Comparison of intra-domain traffic engineering methods

Comparison of intra-domain traffic engineering methods

A series of simulations were carried out to benchmark the performance of default OSPF routing and optimised routing based on LP weight setting and the NN/MIH MPLS method. We compare the performance of the three routing schemes based on their packet loss rate as a function of total demand. For the simulations we have selected a network which consists of 23 nodes, connected by 86 uni-directional links as depicted in Figure 1. The link capacities range from 512 Mbps to 16,384 Mbps. The default OSPF metric for links faster than 100 Mbps is unity (without any changes to the reference bandwidth). The link latency is chosen to be 5 ms. We assume that the traffic represents aggregated flows from every origin to every destination in the network. Thus there are 506 individual flows which need to be accommodated, where each flow represents the offered traffic for one ODpair. Each flow is modelled as a constant bit rate source sending 500 bytes packets from the origin to the destination. The interval rate is determined by the ODpair demand which is randomly generated using a Gaussian distribution with a computed mean and standard deviation. The simulation length is 3 seconds including 0.1 second of a warm-up period. The statistic collection starts after the warm-up period elapsed.
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Modelling and estimation for the genetic analysis of longitudinal data

Modelling and estimation for the genetic analysis of longitudinal data

3.3 Character Process models. In these models rather than thinking of a linear model to generate the covariance functions we directly decompose the variance function G(s,t) into terms such as υ(s)υ(t) ρ(‌s-t‌).The variance function υ(t) υ(t) can be written as a polynomial in t and ρ(‌s-t‌) describes how the correlation changes with s and t. Jaffrezic and Pletcher(2000) suggest using a transition of t,based on a small number of parameters, as a way of introducing non-stationarity. 4 COMPARISONS FOR UNIVARIATE ANALYSIS

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IXP traffic: a macroscopic view

IXP traffic: a macroscopic view

IXP traffic dynamics. Traffic dynamic on various time scales is an important aspect of any network. For exam- ple, monthly or yearly growth is essential for infrastructure dimensioning and traffic engineering. The network’s daily and seasonal trends provide interesting social information regarding its user base. The peak-to-valley ratio in a given network is closely related to the application mix used in the network; i.e. a residential ISP with heavy P2P usage would have a low peak-to-value ratio, while an enterprize network would typically have very high peak-to-valley ratio [11, 27]. Understanding the evolution of such temporal properties is in the focus of our current research. While IXPs often pro- vide data for the past 12-24 months, web-archives can be used for digging into historical data.
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COMPARATIVE ANALYSIS OF TIME DOMAIN AND FREQUENCY DOMAIN BLIND AUDIO SOURCE SEPARATION TECHNIQUES

COMPARATIVE ANALYSIS OF TIME DOMAIN AND FREQUENCY DOMAIN BLIND AUDIO SOURCE SEPARATION TECHNIQUES

(4) Once algorithms stops after fulfilling the stopping criteria. Weight needs to normalise and centre removal performed. And obtained estimated weight matrix applied as per equation (2) for estimation of source signals. Second time domain algorithm is complete ICA decomposition of observation space. In this algorithms first, it is assumed that M samples of simultaneously recorded from array of microphones are yi(n) y2(n). . . .. . ym(n). And a matrix is created by delaying each recorded sample by l. Matrix Y is created
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Traffic Management Study

Traffic Management Study

HELLINGA, B ET AL., (2008) Through this paper, authors stated that the traffic signal timing plans are typically developed on the basis of turning movement traffic and pedestrian volume counts aggregated to 15-minute intervals and obtained over a 4 or 8-hour period on a single day. These data are used to identify the peak hour and to compute the peak hour turning movement traffic volumes. They may also be used to compute the peak hour factor (PHF). Result is to control the traffic congestion in intersection by using signals.
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Estimate mortality rate by using kaplan meier estimator among accidental data at Batu Pahat, Johor, Malaysia

Estimate mortality rate by using kaplan meier estimator among accidental data at Batu Pahat, Johor, Malaysia

The standard nonparametric estimator of the survival function is the Kaplan-Meier (K-M) estimator, which is the product-limit estimator. This estimator comprise information from all the observation available, both censored and uncensored (event times), by consider survival to any point in times as series of step defined at the observed survival and censored times [6].Survival function of road traffic accident is denoted by S(t), gives the probability that people involved in road traffic accident survives longer than some specified time(t). Survival function gives the probability that the random variable(T) exceeds the specified time(t).This estimator is defined as:
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Traffic Signal Controller for Mixed Traffic C...

Traffic Signal Controller for Mixed Traffic C...

This leads to extending the green stage inefficiently, particularly when there are long queues waiting at red signals. In general, VA control performance deteriorates with heavy traffic conditions. Self Optimising or Adaptive Traffic Signal (ATS) control is designed to address those deficiencies. ATS control adjusts the length of green time for a particular stage, based both on traffic demand from the stages having right of way, and having red. The algorithm of the controller is usually developed using a complex conventional mathematical model. A number of approaches have been proposed for the design and implementation of ATS control systems, such as TOL and MOVA. Due to the complexity of finding the optimum signal setting parameters using conventional mathematical models, researchers have applied artificial intelligence techniques to address the problems.
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Title: Minimizing Delay by Controlling Signal in VANET

Title: Minimizing Delay by Controlling Signal in VANET

In this method, initialize the traffic signal controller to the initial phase and initially set the extension time for the phase to 0. Next, we search for a vehicle that is closest to the stop line by examining the location field of all the vehicles. And compute the approximate traveling time to the stop line using the Compute_Traveling_Time () function as follows. The packet broadcast by the closest vehicle contains its position and speed data. These data are extracted, and since the position data consist of a Cartesian coordinates, we can compute the Euclidean distance of the vehicle from the stop line. Given the distance of a vehicle from the stop line, we can use the current speed information to compute the traveling time to the stop line. This traveling time is an approximation of the actual traveling time. If the vehicle closest to the stop line indicates a speed of 0, Compute_Traveling_Time () returns a traveling time of 2 s . We set variable gap to be equal to the traveling time returned by Compute_Traveling_Time ().
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The Application of Cluster Analysis for Division of the Territory of Poland into Homogenous Groups in Terms of Traffic

The Application of Cluster Analysis for Division of the Territory of Poland into Homogenous Groups in Terms of Traffic

In order to make a division of the territory of Poland into homogenous groups in terms of traffic, the procedure was used based on a thorough use of quantitative methods and qualitative methods. In the first place, basing on the shape of the profiles of variation (cluster analysis), groups of indicators were established and, subsequently, based on expert knowledge (geographic and functional classification), the factors using which they can be easily identified were determined. The validity of this approach is confirmed by the current, studies described in [4, 5, 6, 7].

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