Traffic Profiles Research Articles

The urban project of HafenCity. Today’s Urban and Traffic profile of the area. Executive summary of methodology and traffic research conducted in the region

Abstract HafenCity is the largest, ongoing, urban regeneration program in Europe. The old port area of Hamburg is fully reconstructed with a series of urban planning, traffic and building interventions, in the context of sustainable development (middle road back, renewable energy buildings like “Greenpeace”, “Unilever”, etc., proofing infrastructure and new infrastructure from phenomena of natural disasters like floods, promotion policies of “sustainable mobility”. The northern part of the port is relocated (as a result of the gradual relocation trend and technological upgrading which had already begun in the 1960s throughout Europe) and Hamburg is first associated with the water element and the River Elbe along with a program of its fully environmental restoration. Also the Elbe is still used for a number of smart travel people and goods to and from Hamburg. The old “logistic” used buildings are tottaly replaced by new buildings with mixed uses. Hamburg is flipping its face at the liquid element. It also introduces and seeks the full protection of the wider area of HafenCity phenomena of floods and tides which proved fatal in the past through implementation of the project “Warft solution”. All new buildings and traffic infrastructure is now built at a height of 4.5-8.3 meters higher than the initial level and in old level created a regenerated track of historical walks a total length of 10.5 kilometers, creating in this way an additional network of public urban space, in the context of sustainable mobility. Also the new line of traffic means (U-4) gives new chances in the way of Sustainable mobility for the whole city Which is the current planning, traffic and environmental image of the region of HafenCity 15 years after the beginning of the program and on completion of 70% of the planned interventions? How this occurs by utilizing a set of indicators in the direction of Sustainable Development and Sustainable Mobility and what is its contribution to the modulation of total microclimate of the greater Hamburg region by removing a perfectly disturbing use of the center of the city? Which parameters and practices must be taken into account from now on for the Planning, Traffic and environmental shielding of HafenCity, on the basis of original objectives as they possibly today redefined? Pilot presentation of the results of research with use of indicators of “European Local Transport Information Service (ELTIS)”

Sleep-enabled roadside units for motorway vehicular networks

In this paper, we introduce a number of generic sleep mechanisms for energy saving at the vehicular roadside units (RSUs). Since random sleep cycles (sleep cycles type-I) were already introduced before, we term the introduced mechanisms sleep cycles (type-II, III, IV, V, VI). Each sleep cycles type arranges the service and sleep sequences distinctively to yield various levels of energy savings and average packet delay. A generic analytic model for the roadside unit (RSU) with such sleep cycles is proposed using G/G/1/K G-vacation queuing, where real vehicular traffic profiles and packet size measurements are utilised. The performance evaluation reveals that with one of the proposed sleep cycles (type-IV), the RSU achieves 68% energy savings and 7.3 ms average packet delay over the day, resulting in respective improvements of 10% and 28% compared to the existing random sleep cycles. These improvements have been achieved under a very conservative operating delay bound for audio conferencing applications. However, modern compression and codecs, due to their leniency on Quality of Service (QoS), would potentially enable higher energy savings through the proposed sleep cycles.

Energy-Efficient Service Multiplexing on Profile-Based TWDM Access Systems

Both the variety of communication devices and their options in connecting to the Internet are continuously increasing due to the advances in numerous attractive network applications. These trends make designing the network much more complex. Legacy network design only had to consider predefined applications using common end-point devices such as plain old telephone system. On the other hand, the current and future network, especially in the access part, is expected to be universal so as to cover not only residential users, but also mobile users, and the huge number Internet of Things devices for the sake of cost efficiency. The key issue is the difficulty of designing a complex network that is also extremely energy efficient, particularly the future access system. Although many studies have attempted to improve the bandwidth utilization or energy efficiency in multiple-wavelength systems, little is known about how it will be impacted by the divergence among these multiple concrete services. In order to solve this energy issue, we propose a service multiplexing method with a profile-based network architecture, where each device declares its traffic profile. Our concept provides the feasible and practical control of complex network. It differs markedly from the methods that control the traffic at the packet or session level as it is easy to apply in actual networks.

Analyzing the potential of full duplex in 5G ultra-dense small cell networks

Full-duplex technology has become an attractive solution for future 5th generation (5G) systems for accommodating the exponentially growing mobile traffic demand. Full duplex allows a node to transmit and receive simultaneously in the same frequency band, thus, theoretically, doubling the system throughput over conventional half-duplex systems. A key limitation in building a feasible full-duplex node is the self-interference, i.e., the interference generated by the transmitted signal to the desired signal received on the same node. This constraint has been overcome given the recent advances in the self-interference cancellation technology. However, there are other limitations in achieving the theoretical full-duplex gain: residual self-interference, traffic constraints, and inter-cell and intra-cell interference. The contribution of this article is twofold. Firstly, achievable levels of self-interference cancellation are demonstrated using our own developed test bed. Secondly, a detailed evaluation of full-duplex communication in 5G ultra-dense small cell networks via system level simulations is provided. The results are presented in terms of throughput and delay. Two types of full duplex are studied: when both the station and the user equipments are full duplex capable and when only the base station is able to exploit simultaneous transmission and reception. The impact of the traffic profile and the inter-cell and intra-cell interferences is addressed, individually and jointly. Results show that the increased interference that simultaneous transmission and reception causes is one of the main limiting factors in achieving the promised full-duplex throughput gain, while large traffic asymmetries between downlink and uplink further compromise such gain.

A Study on Improving Urban Food Delivery Services Considering Daily Traffic Profile by Time of the Day

A Study on Improving Urban Food Delivery Services Considering Daily Traffic Profile by Time of the Day

A statistical method for estimating predictable differences between daily traffic flow profiles

It is well known that traffic flows in road networks may vary not only within the day but also between days. Existing models including day-to-day variability usually represent all variability as unpredictable fluctuations. In reality, however, some of the differences in flows on a road may be predictable for transport planners with access to historical data. For example, flow profiles may be systematically different on Mondays compared to Fridays due to predictable differences in underlying activity patterns. By identifying days of the week or times of year where flows are predictably different, models can be developed or model inputs can be amended (in the case of day-to-day dynamical models) to test the robustness of proposed policies or to inform the development of policies which vary according to these predictably different day types. Such policies could include time-of-day varying congestion charges that themselves vary by day of the week or season, or targeting public transport provision so that timetables are more responsive to the day of the week and seasonal needs of travellers. A statistical approach is presented for identifying systematic variations in daily traffic flow profiles based on known explanatory factors such as the day of the week and the season. In order to examine day-to-day variability whilst also considering within-day dynamics, the distribution of flows throughout a day are analysed using Functional Linear Models. F-type tests for functional data are then used to compare alternative model specifications for the predictable variability. The output of the method is an average flow profile for each predictably different day type, which could include day of the week or time of year. An application to real-life traffic flow data for a two-year period is provided. The shape of the daily profile was found to be significantly different for each day of the week, including differences in the timing and width of peak flows and also the relationship between peak and inter-peak flows. Seasonal differences in flow profiles were also identified for each day of the week.

Hybrid femto/macro rate-based offloading for high user density networks

Nowadays, the popularity of smart phones creates huge capacity requirements for networks during mass events where thousands of people coexist in specific areas. At such events, large numbers of people use their smartphones to share pictures and download information. This behavior creates traffic profiles that differ from those typically observed in legacy -less populated- networks where lower uplink traffic volumes are generated. Thus, novel network planning and radio resource management mechanisms have to be considered for such dense network conditions, one of which is macrocell offloading. In this context, reducing the cell size has always been the best way to increase the network capacity of LTE. Femtocells are used to enable offloading data-traffic from macrocell network to increase the capacity. In this paper, to achieve efficient user offloading from macro to femtocells, we propose an offloading algorithm based on a perceived rate threshold in combination with uplink power control for hybrid femtocells also considering resource block partitioning. The proposed algorithm enhances the network capacity so that more mobile users can satisfy their minimum Quality of Service (QoS) requirements, thus the overall performance of dense cellular networks increases. The proposed offloading mechanism is assessed in terms of the achieved total throughput and outage probability figures. Simulation results demonstrate the potential increase of the number of supported users per macrocell in joint macro and hybrid femtocell deployments.

Development of metabolomic indicators of exposure to primary traffic for use in air pollution epidemiologic modeling

Introduction. Traffic pollution health studies increasingly focus on identification of sensitive, biologically-relevant indicators of exposure and response. Environmental metabolomics, where metabolites associated with endogenous and exogenous processes can be quantitated, holds promise as a powerful tool to improve internal exposure estimation to complex air pollution mixtures, including primary traffic emissions. Methods. We conducted a panel-based study to measure an extensive suite of pollutants at ambient and indoor sites ranging from 0.01 to 2.3 km away from a major highway artery. In addition, 54 students living in dormitories either near (20 m) or far (1.4 km) from the highway conducted personal sampling and contributed weekly biomonitoring (plasma and saliva). We used targeted and untargeted metabolomics-wide association analyses to examine associations between primary traffic and corresponding metabolomics profiles in the panel. Results. Exposures to traffic pollution were different between students living in the near and far dorms. A total of 20766 metabolites were reliably extracted from plasma and 29013 from saliva samples. Linear random effects models were conducted to examine associations between metabolite intensity (relative concentration) and student dorm location. In total, 221 metabolites were robustly identified and significantly different in the near dorm metabolic profiles compared to those in far dorm (p < 0.05, Benjamini–Hochberg FDR correction). Preliminary functional analyses indicate that these metabolites relate to pathways associated with redox biology, the carnitine shuttle, and oxidative stress. Conclusions. This study is among the first to examine the metabolic response to complex traffic exposures. Comprehensive pathway analysis and validation is currently being conducted to identify specific metabolite patterns and further develop biologically-relevant indicators to primary traffic exposures for panel-based epidemiologic studies.

Traffic classification for managing Applications’ networking profiles

AbstractAlong with the growing number of applications and end‐users, online network attacks and advanced generations of malware have continuously proliferated. Many studies have addressed the issue of intrusion detection by inspecting aggregated network traffic with no knowledge of the responsible applications/services. Such systems fail to detect intrusions in applications whenever their abnormal traffic fits into the network normality profiles. We address the problem of detecting intrusions in (known) applications when their traffic exhibits anomalies. Building traffic profiles for each separate application is the main challenge of this problem. This paper surveys traffic classification methodologies, within a taxonomy framework, to find out the best possible traffic classification methodologies that could help us answer the following question: given a traffic sample, generated by a particular application, does it conforms to the expected application's traffic? The key requirements for a practical solution are discussed. Then, the referred traffic classification methodologies are assessed in terms of their capabilities, limitations and challenges for being used as a part of this solution. The approaches based on “multiple sub‐flows” have shown the potential to be used for building robust and practical per‐application profiles in near real‐time. An overview of a blend of real‐time approaches is also described. Copyright © 2016 John Wiley & Sons, Ltd.

Optimal planning of virtual content delivery networks under uncertain traffic demands

Content Delivery Networks (CDNs) have been identified as one of the relevant use cases where the emerging paradigm of Network Functions Virtualization (NFV) will likely be beneficial. In fact, virtualization fosters flexibility, since on-demand resource allocation of virtual CDN nodes can accommodate sudden traffic demand changes. However, there are cases where physical appliances should still be preferred, therefore we envision a mixed architecture in between these two solutions, capable to exploit the advantages of both of them. Motivated by these reasons, in this paper we formulate a two-stage stochastic planning model that can be used by CDN operators to compute the optimal long-term network planning decision, deploying physical CDN appliances in the network and/or leasing resources for virtual CDN nodes in data centers. Key findings demonstrate that for a large range of pricing options and traffic profiles, NFV can significantly save network costs spent by the operator to provide the content distribution service.

Economic planning of electric vehicle charging stations considering traffic constraints and load profile templates

This paper develops a novel solution to integrate electric vehicles and optimally determine the siting and sizing of charging stations (CSs), considering the interactions between power and transportation industries. Firstly, the origin–destination (OD) traffic flow data is optimally assigned to the transportation network, which is then utilized to determine the capacity of charging stations. Secondly, the charging demand of charging infrastructures is integrated into a cost-based model to evaluate the economics of candidate plans. Furthermore, load capability constraints are proposed to evaluate whether the candidate CSs deployment and tie line plans could be adopted. Different scenarios generated by load profile templates are innovatively integrated into the economic planning model to deal with uncertain operational states. The models and framework are demonstrated and verified by a test case, which offers a perspective for effectively realizing optimal planning of the CSs considering the constraints from both transportation and distribution networks.

Traffic-sensitive Live Migration of Virtual Machines

In this paper we address the problem of network contention between the migration traffic and the Virtual Machine (VM) application traffic for the live migration of co-located Virtual Machines. When VMs are migrated with pre-copy, they run at the source host during the migration. Therefore the VM applications with predominantly outbound traffic contend with the outgoing migration traffic at the source host. Similarly, during post-copy migration, the VMs run at the destination host. Therefore the VM applications with predominantly inbound traffic contend with the incoming migration traffic at the destination host. Such contention increases the total migration time of the VMs and degrades the performance of the VM application. Here, we propose a traffic-sensitive live VM migration technique to reduce the contention of migration traffic with the VM application traffic. It uses a combination of pre-copy and post-copy techniques for the migration of the co-located VMs (those located on the same source host), instead of relying on any single pre-determined technique for the migration of all the VMs. We base the selection of migration techniques on the VMs’ network traffic profiles so that the direction of migration traffic complements the direction of the most VM application traffic. We have implemented a prototype of traffic-sensitive migration on the KVM/QEMU platform. In the evaluation, we compare traffic-sensitive migration against the approaches that use only pre-copy or only post-copy for VM migration. We show that our approach minimizes the network contention for migration, thus reducing the total migration time and the application degradation.

Study of Reconfiguration Cost and Energy Aware VNE Policies in Cycle-Stationary Traffic Scenarios

Network virtualization techniques allow for the coexistence of many virtual networks hosted in the same substrate network. Virtual router migration allows for resource consolidation with the consequence to reduce the power consumption in low traffic periods. Unfortunately, virtual router migration has the effect of degrading the quality of service during the downtime in which the router is not able to carry on its forwarding function. For this reason, despite the advantages in power consumption saving, the migration technique should be applied with parsimony to avoid an excessive QoS degradation. The objective of this paper is to study virtual network embedding (VNE) problems aware of both operation and reconfiguration costs that are characterized by the energy consumption and the revenue loss due to QoS degradation. Both embedding optimization problem formulation and feasible solutions to the problem in cycle-stationary traffic scenario and when the possible embeddings are determined a priori will be given. Two migration policies will be introduced and compared. The first one, referred to as “global,” is based on the knowledge of the entire daily traffic profile and it is determined by solving a Markov decision process. The second one, referred to as “local,” is only based on the knowledge of the current traffic. The results achieved show how the application of the global policy allows better performance than the local one and its application can lead to a cost reduction in the order of 35% with respect to traditional migration policies.

Evaluating C-RAN fronthaul functional splits in terms of network level energy and cost savings

The placement of the complete baseband processing in a centralized pool results in high data rate requirement and inflexibility of the fronthaul network, which challenges the energy and cost effectiveness of the cloud radio access network (C-RAN). Recently, redesign of the C-RAN through functional split in the baseband processing chain has been proposed to overcome these challenges. This paper evaluates, by mathematical and simulation methods, different splits with respect to network level energy and cost efficiency having in the mind the expected quality of service. The proposed mathematical model quantifies the multiplexing gains and the trade-offs between centralization and decentralization concerning the cost of the pool, fronthaul network capacity and resource utilization. The event-based simulation captures the influence of the traffic load dynamics and traffic type variation on designing an efficient fronthaul network. Based on the obtained results, we derive a principle for fronthaul dimensioning based on the traffic profile. This principle allows for efficient radio access network with respect to multiplexing gains while achieving the expected users' quality of service.

Programmable bandwidth management in software-defined EPON architecture

This paper proposes a software-defined EPON architecture which replaces the hardware-implemented DBA module with reprogrammable DBA module. The DBA module allows pluggable bandwidth allocation algorithms among multiple ONUs adaptive to traffic profiles and network states. We also introduce a bandwidth management scheme executed at the controller to manage the customized DBA algorithms for all date queues of ONUs. Our performance investigation verifies the effectiveness of this new EPON architecture, and numerical results show that software-defined EPONs can achieve less traffic delay and provide better support to service differentiation in comparison with traditional EPONs.

Energy Efficiency and Blocking Reduction for Tidal Traffic via Stateful Grooming in IP-Over-Optical Networks

The growing popularity of high-speed mobile communications, cloud computing, and the Internet of Things (IoT) has reinforced the tidal traffic phenomenon, which induces spatio-temporal disequilibrium in the network traffic load. The main reason for tidal traffic is the large-scale population migration between business areas during the day and residential areas during the night. Traffic grooming provides an effective solution to aggregate multiple fine-grained IP traffic flows into the optical transport layer by flexibly provisioning lightpaths over the physical topology. In this paper, we introduce a comprehensive study on energy efficiency and network performance enhancement in the presence of tidal traffic. We propose and leverage the concept of stateful grooming to apply differentiated provisioning policies based on the state of network nodes. We formulate and solve the node-state-decision optimization problem, which can decide the specific state of network nodes when a certain traffic profile is given, considering the trade-off between energy efficiency and blocking performance with a multi-objective integer linear program (MOILP). Then, we propose an online traffic-aware intelligent differentiated allocation of lightpaths (TIDAL) algorithm, based on stateful grooming and the MOILP, to accommodate the dynamic tidal traffic. Our illustrative numerical results show that the proposed method can achieve a significant performance improvement in an energy-efficient way.

Joint Wireless and Optical Power States Scheduling for Green Multi-Radio Fiber-Wireless Access Network

Due to the massive deployment of electrical network devices, Fiber-Wireless (FiWi) access network has to suffer from the challenge of high energy consumption. With the ever-increasing eager for green communication infrastructure, the issue of high energy consumption may become one of the major barriers for the advance of FiWi access network. Previous works proposed for green FiWi access network cover three aspects, including the optimized deployment of network devices such as optical network units (ONUs), the energy-efficient bandwidth allocation of ONUs with QoS guarantee, and the dynamic power states scheduling of ONUs (i.e., active/sleep) according to their traffic profile. However, these works did not take the energy-saving design of wireless subnetwork into account simultaneously. In fact, when some ONUs in the optical subnetwork are switched into sleep states, part of the radio interfaces originally forwarding traffic to the sleep ONUs would be idle or low-loaded. This provides a potential opportunity for energy-saving in wireless subnetwork by switching off the idle or low-loaded radios. This paper focuses on the design of green multi-radio FiWi access network by integrating the energy-savings of optical and wireless subnetworks. To support the energy-efficient design, the new power states are defined for ONUs and radios, respectively. Aiming at dynamic traffic profile, the heuristic algorithms are proposed for the energy saving of integrated wireless front-end and optical back-end of FiWi access network. First, the Energy-saving algorithm with ONU sleep mechanism (EAS) is proposed to dynamically schedule the power states of ONUs by judging their traffic profile with load thresholds. Then, the Energy-saving algorithm based on Radios Off (ERO) is proposed to reconfigure the topology of wireless subnetwork by controlling the power states of radios dynamically. Moreover, wireless rerouting is employed in both EAS and ERO to guarantee the QoS provisioning ability of network. Finally, a comprehensive energy-saving scheme called EE is proposed by combining the EAS and ERO algorithms strategically. Simulation results show that with the reasonable setting of parameters, the proposed EE scheme can save the energy of 33.14% to 64.35% and 8.56% to 36.42% in a wide range of traffic load compared to the No-energy-saving and QoS-aware energy-saving scenarios, respectively.

Meso-scale on-road vehicle emission inventory approach: a study on Dhaka City of Bangladesh supporting the ‘cause-effect’ analysis of the transport system

The study aims to develop an emission inventory (EI) approach and conduct an inventory for vehicular sources in Dhaka City, Bangladesh. A meso-scale modelling approach was adopted for the inventory; the factors that influence the emissions and the magnitude of emission variation were identified and reported on, which was an innovative approach to account emissions unlike the conventional inventory approaches. Two techniques for the emission inventory were applied, viz. (i) a combined top-down and bottom-up approach that considered the total vehicle population and the average diurnal on-road vehicle speed profile in the city and (ii) a bottom-up approach that accounted for road link-specific emissions of the city considering diurnal traffic volume and speed profiles of the respective roads. For the bottom-up approach, road link-specific detailed data were obtained through field survey in 2012, where mid-block traffic count of the day, vehicle speed profile, road network and congestion data were collected principally. The emission variances for the change in transport system characteristics (like change in fuel type, AC usage pattern, increased speed and reduced congestion/stopping) were predicted and analysed in this study; congestion influenced average speed of the vehicles, and fuel types in the vehicles were identified as the major stressors. The study performance was considered reasonable when comparing with the limited number of similar studies conducted earlier. Given the increasing trend of private vehicles each year coupled with increasing traffic congestion, the city is under threat of increased vehicular emissions unless a good management strategy is implemented. Although the inventory is conducted for Dhaka and the result may be important locally, the approach adopted in this research is innovative in nature to be followed for conducting research on other urban transport systems.

Network anomaly detection using IP flows with Principal Component Analysis and Ant Colony Optimization

It is remarkable how proactive network management is in such demand nowadays, since networks are growing in size and complexity and Information Technology services cannot be stopped. In this manner, it is necessary to use an approach which proactively identifies traffic behavior patterns which may harm the network’s normal operations. Aiming an automated management to detect and prevent potential problems, we present and compare two novel anomaly detection mechanisms based on statistical procedure Principal Component Analysis and the Ant Colony Optimization metaheuristic. These methods generate a traffic profile, called Digital Signature of Network Segment using Flow analysis (DSNSF), which is adopted as normal network behavior. Then, this signature is compared with the real network traffic by using a modification of the Dynamic Time Warping metric in order to recognize anomalous events. Thus, a seven-dimensional analysis of IP flows is performed, allowing the characterization of bits, packets and flows traffic transmitted per second, and the extraction of descriptive flow attributes, like source IP address, destination IP address, source TCP/UDP port and destination TCP/UDP port. The systems were evaluated using a real network environment and showed promising results. Moreover, the correspondence between true-positive and false-positive rates demonstrates that the systems are able to enhance the detection of anomalous behavior by maintaining a satisfactory false-alarm rate.

Energy-Aware Traffic Offloading for Green Heterogeneous Networks

With small cell base stations (SBSs) densely deployed in addition to conventional macro base stations (MBSs), the heterogeneous cellular network (HCN) architecture can effectively boost network capacity. To support the huge power demand of HCNs, renewable energy harvesting technologies can be leveraged. In this paper, we aim to make efficient use of the harvested energy for on-grid power saving while satisfying the quality of service (QoS) requirement. To this end, energy-aware traffic offloading schemes are proposed, whereby user associations, ON-OFF states of SBSs, and power control are jointly optimized according to the statistical information of energy arrival and traffic load. Specifically, for the single SBS case, the power saving gain achieved by activating the SBS is derived in closed form, based on which the SBS activation condition and optimal traffic offloading amount are obtained. Furthermore, a two-stage energy-aware traffic offloading (TEATO) scheme is proposed for the multiple-SBS case, considering various operating characteristics of SBSs with different power sources. Simulation results demonstrate that the proposed scheme can achieve more than 50% power saving gain for typical daily traffic and solar energy profiles, compared with the conventional traffic offloading schemes.