Traffic Profiles Research Articles

Enabling Performance-Based Navigation Arrivals: Development and Simulation Testing of the Terminal Sequencing and Spacing System

NASA has developed an advanced arrival management capability for terminal controllers, known as Terminal Sequencing and Spacing (TSAS). TSAS increases use of performance-based navigation (PBN) arrival procedures during periods of high traffic demand. It enhances two Federal Aviation Administration (FAA) operational systems with terminal metering and controller spacing tools. Sixteen high-fidelity human-in-the-loop simulations, involving more than five hundred hours of evaluation time, were conducted to mature TSAS from proof-of-concept design to fully functional prototype. These simulations modeled arrival procedures at several US airports, incorporated a broad range of traffic demand profiles and wind conditions, and used controllers with extensive operational experience. Two metrics are evaluated for these simulations: PBN Success Rate and Inter-Arrival Spacing Error. The PBN Success Rate shows a definitive trend when TSAS is used. TSAS increases from 42% for today’s operations to 68% for terminal metering only and 92% for terminal metering with controller-managed spacing tools. Meanwhile, the Inter-Arrival Spacing Error improves 25% to 35% when TSAS is used compared to when it is not used. The TSAS technology was transferred to the FAA, and it is targeted for deployment to several busy airports in the US starting in 2018.

Identifying Optimal High-Risk Driver Segments for Safety Messaging

Given the public safety risk posed by high-risk drivers, most traffic safety agencies consider this group a key target for strategic planning purposes. The aim of this research was to develop a framework that could be used to efficiently and effectively target high-risk drivers. The specific objectives were to establish whether high-risk drivers were homogeneous and if not, to determine the optimal set of primary and secondary clusters for efficient and effective targeting with minimal resources. The study area was Saskatchewan, Canada. Multiple databases (including traffic collisions, insurance claims, and conviction data) formed the basis for the research. In this study, high-risk drivers were defined as all drivers who were enrolled both in the Driver Improvement Program of Saskatchewan Government Insurance and in the negative or penalty zone of Saskatchewan Government Insurance's safety driver rating scale as a result of accumulated demerit points. Geodemographic modeling with the neighborhood as the unit of analysis, a large number of variables, and a set of probabilistic clustering techniques were used in the analysis. The results indicated that the high-risk driver group was heterogeneous; it fell into subclusters with varying collision and traffic behavior profiles. The study found that Saskatchewan high-risk drivers were mainly in the major cities (56%) but also in rural municipalities (18%) and towns (15%). The optimal primary high-risk segments for efficient targeting were those major cities and towns where both the risk of collision involvement and the concentration of high-risk drivers were higher than the driver population. Drivers in the primary target area for messaging showed higher levels of distracted, impaired, and aggressive driving behaviors; driver inexperience; extreme fatigue; falling asleep behind the wheel; and inattention.

Large-Scale Monitoring for Cyber Attacks by Using Cluster Information on Darknet Traffic Features

This paper presents a machine learning approach to large-scale monitoring for malicious activities on Internet. In the proposed system, network packets sent from a subnet to a darknet (i.e., a set of unused IPs) are collected, and they are transformed into 27-dimensional TAP (Traffic Analysis Profile) feature vectors. Then, a hierarchical clustering is performed to obtain clusters for typical malicious behaviors. In the monitoring phase, the malicious activities in a subnet are estimated from the closest TAP feature cluster. Then, such TAP feature clusters for all subnets are visualized on the proposed monitoring system in real time. In the experiment, we use a big data set of 303,733,994 darknet packs collected from February 1st to February 28th, 2014 (28 days) for monitoring. As a result, we can successfully detect an indication of the pandemic of a new malware, which attacked to the vulnerability of Synology NAS (port 5,000/TCP).

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Incident-Induced Delays on Freeways

Freeway incidents cause nonrecurrent congestion, which contributes significantly to overall travel delays. This paper provides a method to estimate the magnitude of delays resulting from incidents. Incident-induced delays can be estimated by establishing an incident-free reference travel time profile as a basis from which the incident-induced delay is measured. The main objective of this paper is to show how to establish the reference profile from a subset of candidate profiles of similar traffic patterns. To identify similar traffic patterns, a hybrid of travel time and volume profiles was used. To establish the reference profile, three methods for candidate profile identification were investigated: day of the week based, cluster analysis based, and k–nearest neighbor based. Weighted summations of the candidate profiles were also investigated: arithmetic average, Shepard's inverse distance method, and rank exponent method. A ground truth experiment was conducted, and the results showed that the best way to formulate the reference traffic profile was by using the k–nearest neighbor in combination with the weighted summation of rank exponent method. The proposed approach provides practical guidance to estimate incident-induced delays more accurately.

Caracterización de la intensidad media diaria y de los perfiles horarios del tráfico vehicular del Distrito Metropolitano de Quito

El tráfico vehicular es una fuente relevante de contaminantes del aire. La configuración espacial y temporal de las emisiones vehiculares debe caracterizarse con la mejor información disponible, para apuntalar la gestión de calidad del aire, o emprender estudios del comportamiento de los contaminantes en la atmósfera. Los mapas de emisión se estructuran típicamente en base de mapas de la intensidad y de los perfiles horarios de tráfico. Presentamos una caracterización actualizada al año 2013 de la intensidad media diaria del tráfico vehicular del Distrito Metropolitano de Quito (DMQ) y de su variación horaria por tipos de día, en base al conteo en 335 puntos desarrollado por la Secretaria de Movilidad del Municipio de Quito. Las vías con mayor actividad se localizan principalmente al norte de la zona urbana de Quito y en aquellas que conectan con los valles de Los Chillos, Cumbayá y Tumbaco. En la zona norte, el flujo vehicular de días laborables es mayor a los flujos de los sábados (en un 10%) y domingos (en un 60%). En la zona sur, el flujo vehicular de los sábados es similar a los valores de días laborables. Los domingos, la intensidad de tráfico se reduce en un 26%. En la zona urbana de Quito el tráfico se reduce en días feriados. Sin embargo, en las vías de la periferia hay un incremento (entre 11 y 47%) con respecto al flujo de los días laborables. A futuro esta información será utilizada para generar mapas de alta resolución espacial y temporal de las emisiones de contaminantes primarios del aire. Estos mapas permitirán el desarrollo estudios actualizados de dispersión de contaminantes del aire en el DMQ.

An efficient network on-chip architecture based on isolating local and non-local communications

In this paper, a locality aware NoC communication architecture is proposed. The architecture may reduce the energy consumption and latency in MultiProcessor Systems on Chips (MPSoCs). It consists of two network layers which one layer is dedicated to the packets transmitted to near destinations and the other layer is used for the packets transmitted to far destinations. The actual physical channel width connecting the cores is divided between the two layers. The locality is defined based on the number of hops between the nodes. The relative significances of the two types of communications determine the optimum ratio for the channel width division. To assess the efficiency of the proposed method, we compare its communication latency with that of conventional one for different channel widths, communication traffic profiles, and mesh sizes.

Analysis of Sub-Band Allocation in Multi-Service Cognitive Radio Access Networks

This letter addresses the sub-band allocation problem in dynamic spectrum access (DSA) scheme in a multi-service cognitive radio network (CRN) built over a multi-service primary network (PRN). Both access networks support connection-level and packet-level services, where the Poisson process and the Markov modulated Poisson process are used to describe the connection-level and packet-level services, respectively. By means of a multi-dimensional continuous-time Markov chain model, the DSA scheme is assessed under various traffic load profiles. Numerical results show that the connection-level secondary users's quality of service may be significantly deteriorated by the packet-level primary user service.

Full Featured and Lightweight Control for Optical Packet Metro Networks [Invited

Metro networks support increasing traffic volumes and evolving traffic profiles. Revisiting metro network architecture, optical packet rings with packet optical add and drop multiplexing (POADM) features are proposed in order to support both optical transparency and subwavelength granularity. A medium access control (MAC) structure for multiring POADM networks is proposed, which can support multiprotocol encapsulation and provides the support of differentiated quality of service (QoS) and differentiated protection on a per-flow basis. Unicast and multicast flows are efficiently transported between stations, with a lightweight control within each station. An opportunistic insertion process associated with an appropriate scheduling process is shown to ensure transport network QoS levels. Simple models are provided to assess the transfer performance on the POADM ring. Per-flow protection mechanisms are proposed, and their efficiency is assessed. Dimensioning costs are derived, which take into account the number of used wavelengths and the number of fixed receivers required to support a given traffic matrix. Finally, it is shown that POADM rings can be used to directly support metro Ethernet services and thus allow collapsing the stack of transport network layers.

Toward Energy-Efficient 5G Wireless Communications Technologies: Tools for decoupling the scaling of networks from the growth of operating power

The densification and expansion of wireless networks pose new challenges on energy efficiency. With a drastic increase of infrastructure nodes (e.g. ultradense deployment of small cells), the total energy consumption may easily exceed an acceptable level. While most studies focus on the energy radiated by the antennas, the bigger part of the total energy budget is actually consumed by the hardware (e.g., coolers and circuit energy consumption). The ability to shut down infrastructure nodes (or parts of it) or to adapt the transmission strategy according to the traffic will therefore become an important design aspect of energy-efficient wireless ?architectures. Network infrastructure should be ?regarded as a resource that can be occupied or released on demand, and the modeling and optimization of such systems are highly nontrivial problems. In particular, elements of the network infrastructure should be released by taking into account traffic forecasts to avoid losing the required coverage and capacity. However, even if traffic profiles were perfectly known, the determination of the elements to be released is complicated by the potential interference coupling between active elements and the sheer size of the optimization problems in dense networks.

On the Optimal Transmission Policy in Hybrid Energy Supply Wireless Communication Systems

This paper addresses the optimal transmission scheduling problem in hybrid energy supply systems with the save-then-transmit protocol, where the energy supply of the transmitter comes from both the primary battery and the energy harvester. We first consider minimizing the outage probability for a given amount of battery energy by optimizing the saving factor. It is demonstrated that harvesting external energy is unnecessary for a large spectral efficiency requirement. Then, we consider joint packet scheduling and saving factor optimization to the battery energy consumption minimization (BECM) problem in both single packet arrival and burst packet arrival scenarios. Both optimal and suboptimal offline policies with full information on the traffic profile, the harvesting power, and the channel state are developed. We also propose an optimal online policy in the case that only causal information is available. Numerical results are presented that validate the effectiveness of the proposed algorithms.

On Optimal Cell Activation for Coverage Preservation in Green Cellular Networks

Energy-efficient base station (BS) operation is a key design goal in green cellular networks. An effective way for energy conservation of BSs is to switch BSs on/off according to the traffic profile. However, such operations may create coverage holes in the network. In this paper, we aim to minimize the total power consumption of the network by switching BSs on/off adaptively while maintaining the network coverage. We find that the BS activation problem for minimal network power consumption with full network coverage preservation is an NP-hard problem. To address the problem, we first derive the optimal cell size for minimizing BS power consumption per unit coverage area and propose a polynomial-time algorithm for energy-efficient BS activation. The simulation results show that our algorithm can approach the minimum network power consumption and adapt to network traffic load under non-uniform traffic load distributions. More importantly, we demonstrate that network densification with small cells for bursting throughput in hot spot areas can also be beneficial in saving network energy during the low traffic load period.

Analysis of pedestrian short-term exposure on a high traffic city street

This article studies the influence of building street geometry, wind direction, daily car traffic and pedestrian trajectories in short-term personal exposure to PM10 on a street canyon in Barreiro City, Portugal. An automatic system for the analysis of traffic profiles, a Gaussian model for the determination of traffic emissions (ADMS- Urban) and a computational fluid dynamics model (ANSYS Fluent) to simulate the dispersion of pollutants inside a street canyon were used. Buildings height, width, length and geometry, as well as the distance between the buildings and road width were tested. Besides the real actual geometry of the street, with the real disposition and volumetric configuration of buildings, three scenarios were evaluated: (i) gaps of 4 m between the buildings along the street; (ii) gaps of 6 m between the buildings along the street and (iii) same height and width for all buildings along the street without gap between buildings. Wind direction, wind velocity and four pedestrian trajectories were considered. The results show that PM10 concentration, at 1.5-m high plane, is highly dependent on street geometry and wind conditions for along-canyon wind directions (west and east wind). Lower concentration levels are obtained for configuration (iii) because this geometry promotes the dispersion of pollutants under along-canyon wind direction. For cross-canyon wind directions (north and south), configuration (i) results in lower PM10 concentrations. There are no visible improvements in having higher gaps between buildings (configuration ii). Short-term pedestrian personal exposure is dependent on the pedestrian trajectory considered inside the canyon. Pedestrian trajectories that correspond to crossing the road in the centre of the street result in the highest values. Keywords Air quality, PM10, short-term personal exposure, street canyon, urban planning Language: en

A comprehensive simulation analysis of LTE Discontinuous Reception (DRX)

In an LTE cell, Discontinuous Reception (DRX) allows the central base station to configure User Equipments for periodic wake/sleep cycles, so as to save energy. DRX operations depend on several parameters, which can be tuned to achieve optimal performance with different traffic profiles (i.e., CBR vs. bursty, periodic vs. sporadic, etc.). This work investigates how to configure these parameters and explores the trade-off between power saving, on one side, and per-user QoS, on the other. Unlike previous work, chiefly based on analytical models neglecting key aspects of LTE, our evaluation is carried out via simulation. We use a fully-fledged packet simulator, which includes models of all the protocol stack, the applications and the relevant QoS metrics, and employ factorial analysis to assess the impact of the many simulation factors in a statistically rigorous way. This allows us to analyze a wider spectrum of scenarios, assessing the interplay of the LTE mechanisms and DRX, and to derive configuration guidelines.

SDP Policy Iteration-Based Energy Management Strategy Using Traffic Information for Commuter Hybrid Electric Vehicles

This paper demonstrates an energy management method using traffic information for commuter hybrid electric vehicles. A control strategy based on stochastic dynamic programming (SDP) is developed, which minimizes on average the equivalent fuel consumption, while satisfying the battery charge-sustaining constraints and the overall vehicle power demand for drivability. First, according to the sample information of the traffic speed profiles, the regular route is divided into several segments and the statistic characteristics in the different segments are constructed from gathered data on the averaged vehicle speeds. And then, the energy management problem is formulated as a stochastic nonlinear and constrained optimal control problem and a modified policy iteration algorithm is utilized to generate a time-invariant state-dependent power split strategy. Finally, simulation results over some driving cycles are presented to demonstrate the effectiveness of the proposed energy management strategy.

Cloud BI: Future of business intelligence in the Cloud

In self-hosted environments it was feared that business intelligence (BI) will eventually face a resource crunch situation due to the never ending expansion of data warehouses and the online analytical processing (OLAP) demands on the underlying networking. Cloud computing has instigated a new hope for future prospects of BI. However, how will BI be implemented on Cloud and how will the traffic and demand profile look like? This research attempts to answer these key questions in regards to taking BI to the Cloud. The Cloud hosting of BI has been demonstrated with the help of a simulation on OPNET comprising a Cloud model with multiple OLAP application servers applying parallel query loads on an array of servers hosting relational databases. The simulation results reflected that extensible parallel processing of database servers on the Cloud can efficiently process OLAP application demands on Cloud computing.

A Preliminary Evaluation of Bandwidth Allocation Model Dynamic Switching

Bandwidth Allocation Models (BAMs) are used in order to define Bandwidth Constraints (BCs) in a perclass basis for MPLS/DS-TE networks and effectively define how network resources like bandwidth are obtained and shared by applications. The BAMs proposed (MAM – Maximum Allocation Model, RDM – Russian Dolls Model, G-RDM – Generic RDM and AllocTC-Sharing) attempt to optimize the use of bandwidth resources on a per-link basis with different allocation and resource sharing characteristics. As such, the adoption of distinct BAMs and/or changes in network resource demands (network traffic profile) may result in different network traffic allocation and operational behavior for distinct BAMs. This paper evaluates the resulting network characteristics (link utilization, preemption and flows blocking) of using BAMs dynamically with different traffic scenarios. In brief, it is investigated the dynamics of BAM switching with distinct traffic scenarios. The paper presents initially the investigated BAMs in relation to their behavior and resource allocation characteristics. Then, distinct BAMs are compared using different traffic scenarios in order to investigate the impact of a dynamic change of the BAM configured in the network. Finally, the paper shows that the adoption of a dynamic BAM allocation strategy may result in benefits for network operation in terms of link utilization, preemption and flows blocking.

A functional approach to monitor and recognize patterns of daily traffic profiles

Functional Data Analysis (FDA) is a collection of statistical techniques for the analysis of information on curves or functions. This paper presents a new methodology for analyzing the daily traffic flow profiles based on the employment of FDA. A daily traffic profile corresponds to a single datum rather than a large set of traffic counts. This insight provides ideal information for strategic decision-making regarding road expansion, control, and other long-term decisions. Using Functional Principal Component Analysis the data are projected into a low dimensional space: the space of the first functional principal components. Each curve is represented by their vector of scores on this basis. The principal component scores are used for clustering and also to identify outliers (meaning that there was a bad performance in the recording of data or special circumstances affected the traffic) and to monitor the traffic profile by multivariate control charts. This paper introduces this new methodology and illustrates good results by using 1-min traffic data from the I-94 Freeway in the Twin Cities, Minnesota (U.S.) metroplex ranging from 2004 to 2011.

Energy‐aware load adaptive framework for LTE heterogeneous network

ABSTRACTOne of the main approaches for improving the network energy efficiency (EE) is through the introduction of load adaptive techniques, where the network's components/subsystems are switched off when the network is lightly loaded. Optimising such a dynamic operation in a heterogeneous network (HetNet) remains an active topic of research. In this paper, a traffic load‐adaptive model that aims to evaluate the EE of base stations in Long Term Evolution (LTE) HetNet is presented. First, a model that simulates the load‐adaptive power consumption behaviour of LTE HetNet is developed. In this regard, a load adaptation factor is introduced to assess the network's EE performance. The model also adapts and predicts the achievable data rate of each base station with respect to the traffic load. Our study shows that the fully load‐adaptive LTE HetNet can significantly improve network's EE up to 10%, 40%, and 80% for high, medium, and low loads, respectively, as compared to the conventional non load‐adaptive HetNet. In addition, we show that the full adaptive network operation can achieve significant EE gains under a realistic daily traffic profile up to 86%. The proposed evaluation model is essential to assess the network EE and can be used in future studies that focus on improving the adaptation level of the already installed network equipments. Copyright © 2014 John Wiley & Sons, Ltd.

Delay minimization and priority scheduling in wireless mesh networks

Wireless mesh networks (WMNs) have emerged as a significant technology for applications because of its advantage of multi-radio and multi-channel which makes it perform better than wireless LANs. Furthermore, quality-of-service (QoS) support can be achieved by some distinguished ways in WMN. In this paper, QoS requirements are recorded by traffic profile, QoS constraints are formulated as delay time of transmitting all the requested data flows in the network. Multi-commodity flow technologies are applied for handling this issue. After minimizing the delay of the network by the assistance of multi-commodity-flow techniques and resource contention graph, we use effective channel assignment algorithm to schedule the data flows under the QoS constraints. Our evaluation indicates that our technologies successfully route flows under their special QoS requirements with different priority.