Traffic Generation Model Research Articles

The Potential of VGI and Traffic Flow Generation Model for Solving the Traffic Sensor Location Problem

The Potential of VGI and Traffic Flow Generation Model for Solving the Traffic Sensor Location Problem

A multilane traffic and collision generator for IoV

Cooperative driving is an emerging area in the field of vehicular networks and Intelligent Transportation Systems (ITS). It contributes to enhancing safety by reducing accidents as a consequence of rogue driving and overtaking. Thus, to accomplish the requirements of safety, real-time traffic traces are required for comprehensive analysis. However, the real traces are limited in availability and constraint to the environment they are collected from. On the other hand, a common practice is to use a single-lane traffic generation model to address collision modeling, which is limited by collision modeling of rear-end collisions. Therefore, to address these limitations in the literature, this paper proposes a multilane traffic generation (MLTG) model using microscopic modeling. Furthermore, to generate different multilane synthetic traffic traces, traffic flow behaviors and collision scenarios are replicated as use cases by incorporating the perception & human errors. The efficacy of the MLTG model is tested and analyzed by performing extensive simulations in MATLAB by considering varied scenarios such as straight road, merging, and diversion for single-lane and cross-lane collision modeling, respectively. The results show that the performance of the proposed MLTG model outperforms in terms of stability and scalability, compared to the existing state-of-the-art techniques.

A Maritime Traffic Network Mining Method Based on Massive Trajectory Data

Intelligent ships are the future direction of maritime transportation. Route design and route planning of intelligent ships require high-precision, real-time maritime traffic network information, which changes dynamically as the traffic environment changes. At present, there is a lack of high-precision and accurate information extraction methods for maritime traffic networks. Based on the massive trajectory data of vessels, the adaptive waypoint extraction model (ANPG) is proposed to extract the critical waypoints on the traffic network, and the improved kernel density estimation method (KDE-T) is constructed to mine the spatial–temporal characteristics of marine lanes. Then, an automatic traffic network generation model (NNCM), based on the pix2pix network, is put forward to reconstruct the maritime traffic network. NNCM has been tested on the historical trajectory data of Humen waters and Dongping waters in China, the experimental results show that the NNCM model improves the extraction accuracy by 13% and 33% compared to the geometric analysis method and density clustering method. It is of great significance to improve the navigation accuracy of intelligent ships. This method can also provide important technical support for waterway design and monitoring and maritime traffic supervision.

Subcycle Waveform Modeling of Traffic Intersections Using Recurrent Attention Networks

Traffic flow dynamics in the vicinity of urban arterial intersections is a complex and nonlinear phenomenon, influenced by factors such as signal timing plan, road geometry, driver behaviors, etc. Predicting such flow dynamics is an important task for urban traffic signal control and planning. Current methods use microscopic simulation for studying the impact of a large number of signal timing plans at each of the intersections. A major drawback of microscopic simulation is that they are they are based on source destination traffic generation models and cannot incorporate the high resolution loop detector data such as that are provided by automated traffic signal performance measures (ATSPM) based systems. The arrival (or departure) information of each vehicle on a detector can be thought of as a time series waveform. Given the high granularity of ATSPM data, this waveform can be used to several interesting analyses. The waveforms can be used to derive information on platoon dispersion as vehicles progress across the corridor. Also, these waveforms can be modelled to understand how the vehicles progress across the corridor for a variety of signal timing plans. In this paper, we show that deep neural networks based machine learning systems can be used to effectively leverage the waveforms collected at multiple sensors (stopbar and advanced) on the intersection to model the traffic dynamics both at an intersection and across intersections. We show that modelling of these waveforms can be useful to understand traffic flow dynamics under different signal timing plans and can be potentially integrated into signal timing optimization software. Further, these methods are three to four orders of magnitudes faster than using microscopic simulations.

Design and Implementation of Traffic Generation Model and Spectrum Requirement Calculator for Private 5G Network

This paper proposes a neural 5G traffic generation model and a methodology for calculating the spectrum requirements of private 5G networks to provide various industrial communication services. To accurately calculate the spectral requirements, it is necessary to analyze the actual data volume and traffic type of industrial cases. However, because there is currently no suitable traffic model to test loads in private 5G networks, we have developed a generative adversarial network (GAN)-based traffic generator that can generate realistic traffic by learning actual traffic traces collected by mobile network operators. In addition, in the case of industrial applications, probability-based traffic models were used in parallel as there were not enough real data to be learned. The proposed 5G traffic generation model is combined with the proposed 5G spectrum calculation methodology, enabling more accurate spectrum requirements calculation through traffic simulation similar to a real-life environment. In this paper, the spectrum requirements are calculated differently according to two types of duplexing, namely frequency division duplexing (FDD) and time division duplexing (TDD). As a guide for companies aiming to provide advanced wireless connectivity for a wide variety of vertical industries using 5G networks, eight use cases defined in the 5G Alliance for Connected Industries and Automation (ACIA) white paper were simulated. The spectrum requirements were calculated under various simulation conditions considering varying traffic loads, deployment scenarios, and duplexing types. Various simulation results confirmed that a bandwidth of at least 22.0 MHz to a maximum of 397.8 MHz is required depending on the deployment scenario.

Cauchy Density-Based Algorithm for VANETs Clustering in 3D Road Environments

Vehicular ad hoc networks (VANETs) are emerging to serve various types of applications for serving smart cities and intelligent transportation systems. There are several challenging factors for ensuring reliable and stable VANETs communications. VANETs clustering is essential functionality to serve routing protocols and in enable reliable VANETs. Clustering algorithms for VANETs operate in decentralized mode, which requires incorporating additional stages before deciding the clustering decisions and might create suboptimality due to the local nature of the decentralized approach. In addition, the challenging architecture of the road environment can cause confusing clustering decisions. This problem becomes more challenging due to the evolving nature of clusters in VANETs in general and in 3D VANETs in particular. This paper tackles the problem of 3D VANETs using a centralized clustering model based on the developed Cauchy density model. The development has included a traffic generation model, mobility model for generating driving behavior, and an algorithm for enabling modeling of road curvature based on an adjacency list that defines the road’s points that define a straight-line segment. In addition, a clustering algorithm that defines mobility vector and uses Cauchy-based density for enabling adding vehicles to their respective clusters. For evaluation, conducted scenarios in three locations with 3D architecture are considered, and a comparison with benchmarks shows the superiority of our developed over the benchmarks with an improvement percentage of 1%, 10%, and 3% for average cluster head duration, average cluster member duration, and clustering efficiency respectively.

Traffic Accident Data Generation Based on Improved Generative Adversarial Networks.

For urban traffic, traffic accidents are the most direct and serious risk to people’s lives, and rapid recognition and warning of traffic accidents is an important remedy to reduce their harmful effects. However, research scholars are often confronted with the problem of scarce and difficult-to-collect accident data resources for traffic accident scenarios. Therefore, in this paper, a traffic data generation model based on Generative Adversarial Networks (GAN) is developed. To make GAN applicable to non-graphical data, we improve the generator network structure of the model and used the generated model to resample the original data to obtain new traffic accident data. By constructing an adversarial neural network model, we generate a large number of data samples that are similar to the original traffic accident data. Results of the statistical test indicate that the generated samples are not significantly different from the original data. Furthermore, the experiments of traffic accident recognition with several representative classifiers demonstrate that the augmented data can effectively enhance the performance of accident recognition, with a maximum increase in accuracy of 3.05% and a maximum decrease in the false positive rate of 2.95%. Experimental results verify that the proposed method can provide reliable mass data support for the recognition of traffic accidents and road traffic safety.

On the Complexity of Traffic Traces and Implications

This paper presents a systematic approach to identify and quantify the types of structures featured by packet traces in communication networks. Our approach leverages an information-theoretic methodology, based on iterative randomization and compression of the packet trace, which allows us to systematically remove and measure dimensions of structure in the trace. In particular, we introduce the notion of trace complexity which approximates the entropy rate of a packet trace. Considering several real-world traces, we show that trace complexity can provide unique insights into the characteristics of various applications. Based on our approach, we also propose a traffic generator model able to produce a synthetic trace that matches the complexity levels of its corresponding realworld trace. Using a case study in the context of datacenters, we show that insights into the structure of packet traces can lead to improved demand-aware network designs: datacenter topologies that are optimized for specific traffic patterns.

On the Complexity of Traffic Traces and Implications

This paper presents a systematic approach to identify and quantify the types of structures featured by packet traces in communication networks. Our approach leverages an information-theoretic methodology, based on iterative randomization and compression of the packet trace, which allows us to systematically remove and measure dimensions of structure in the trace. In particular, we introduce the notion of \emphtrace complexity which approximates the entropy rate of a packet trace. Considering several real-world traces, we show that trace complexity can provide unique insights into the characteristics of various applications. Based on our approach, we also propose a traffic generator model able to produce a synthetic trace that matches the complexity levels of its corresponding real-world trace. Using a case study in the context of datacenters, we show that insights into the structure of packet traces can lead to improved demand-aware network designs: datacenter topologies that are optimized for specific traffic patterns.

A New Energy-Efficient and Reliable Protocol for Stochastic WSNs Based on Back-Pressure and Entropy of Residual Energy

In this paper, a new energy-efficient and reliable routing protocol is introduced for WSNs including a stochastic traffic generation model and a wakeup/sleep mechanism. Our objective is to improve the longevity of the WSNs by energy balancing but providing reliable packet transfer to the Base Station at the same time. The proposed protocol is based on the principle of the back-pressure method and besides the difference of backlogs, in order to optimize energy consumption, we use a cost function related to an entropy like function defined over the residual energies of the nodes. In the case of two-hop routing the optimal relay node is selected as the one which has maximum backlog difference and keeps the distribution of residual energy as close to uniform as possible where the uniformity is measured by the change of the entropy of the residual energy of the nodes. The protocol assumes Rayleigh fading model. Simulation results show that the proposed algorithm significantly improves the performance of traditional back-pressure protocol with respect to energy efficiency, E2E delay and throughput, respectively.

A network application model with operational process feature

A network application serves as the response process to the user request. The network application, taking the traffic as operation carrier, is closely related with process features. The existing investigations mainly focus on the traffic generation by capturing request feature, which is insufficient to characterize the application operation with specific processes. Thus, this paper presents a network application model with operational process feature, where the operation process is introduced in network applications in addition to characterizing traffic element from request feature. And the process feature is represented by the random, customized and routine processes, while request feature is described by the heavy-tailed ON/OFF source. Our analysis and simulation show that the traffic of our model admits the ubiquitous statistical laws: the self-similarity and the mean-variance relationship, which further validate our model. Moreover, compared with the traffic generation model without considering complex process features, where traffic distribution is found being positively correlated with node betweenness centrality (BC), the traffic of our model is both positively related with node BC, and much higher on nodes in the specific processes. The proposed model is thus beneficial for traffic control and network enhancement with complex process features.

Hybrid Electro-Optical Intra-Data Center Networks Tailored for Different Traffic Classes

This work presents a novel approach in intradata center network design and performance evaluation, based on a tailored, long-range dependence data traffic generation model for different application classes. We examine how an intra-data center network can be efficiently evaluated and optimized by applying accurate models for machine-generated data and using assumptions on current and future commercially available data center hardware. Moreover, we show that by migrating such a Fat-Tree network to its hybrid, electro-optical counterpart by employing optical circuit switching for selected, intense traffic connections, significant capital and operational expenditure cost savings can be obtained.

Synthesis of dependency-aware traffic generators from NoC simulation traces

Networks-on-chip (NoCs) are currently the most appropriate communication infrastructure for many-core embedded systems. As NoCs become a de facto standard for on-chip systems, traffic generation models become critical for system-on-chip (SoC) design. Traditional trace-based traffic distorts the injection rate and the effects of congestion due to the lack of packets dependency information. They also have large data storage requirements. In this paper, we propose a new framework to process traces generated by message passing applications modeled as acyclic task graphs. This framework builds dependency-aware traffic generators (DATGs) by retrieving the packet dependencies from traces in a single simulation. The DATGs accurately replace the application nodes in emulations or simulations to explore the NoC design space. Our experimental analysis showed that our framework is more accurate than trace-based simulation for a broad range of NoC configurations. Moreover, our proposed framework uses only 3% of the data storage required by the traces.

Modeling Internet Traffic Generations Based on Individual Users and Activities for Telecommunication Applications

A traffic generation model is a stochastic model of the data flow in a communication network. These models are useful during the development of telecommunication technologies and for analyzing the performance and capacity of various protocols,algorithms, and network topologies. We present here two modeling approaches for simulating internet traffic. In our models, we simulate the length and interarrival times of individual packets, the discrete unit of data transfer over the internet. Our first modeling approach is based on fitting data to known theoretical distributions. The second method utilizes empirical copulae and is completely data driven. Our models were based on internet traffic data generated by different individuals performing specific tasks (e.g., web browsing, video streaming, and online gaming). When combined, these models can be used to simulate internet traffic from multiple individuals performing typical tasks. KEYWORDS: Internet Traffic Simulation; Stochastic Models; Empirical Copula; Cumulative distribution function; Wireshark

Urban compactness and the trade-off between air pollution emission and exposure: Lessons from a spatially explicit theoretical model

Air pollution is a major concern in urban areas worldwide. The interplay between urban structure and air pollution from an environmental, health and social perspective is the focus of our work: we model how urban structure impacts traffic-induced pollutant emissions and the exposure of residents to those pollutants.We present a chain of models applied to theoretical monocentric space: a residential choice model with endogenous open-space and road network, a commuting traffic generation and road assignment model and a pollutant emissions, dispersion and exposure model. The theoretical study approach decouples results from location specific characteristics and enables us to analyse how the preference of households for green amenities, a transport tax, the provision of public transport alternatives and local neighbourhood design impact the environment (total emissions) as well as residents’ health (population exposure) and utility.We emphasise that environmental strategies in the form of urban compaction have a strong impact on the exposure of households to pollutants, especially close to the centre, in addition to their reduction of welfare. Our results suggest that more beneficial policy outcomes can be obtained from strategies which preserve green spaces close to the centre or which intend a greater shift from car to public transport. Further, we find indication that different local designs of neighbourhoods have much stronger impacts on the exposure–emission tension than city-wide land use or transport options.

On Network Lifetime Expectancy With Realistic Sensing and Traffic Generation Model in Wireless Sensor Networks

Wireless sensor networks (WSNs), which are used in a wide variety of mission-critical applications, are comprised of sensor nodes with limited battery power. The feasibility of such applications is highly influenced by the longevity or sustainability of these networks. Improvement of energy efficiency and estimation of network lifetime are important issues in these energy constrained networks. In this paper, we present a probabilistic model for estimating the network lifetime of a sensor network. The traffic generation model is designed for irregular surfaces while combining the Elfes sensing model and event generation model. A discrete radio model is considered in this paper for better energy efficiency. Two different scenarios, such as, single-hop and multi-hop networks are analyzed with the proposed model. The likelihood of achieving the desired network lifetime with the proposed model is examined. Extensive simulations are done taking the topology from real-sensor network testbeds. We also estimate the packet loss rates and the results also confirm the accuracy and energy efficiency of the proposed model.

Modeling and stability analysis of hybrid multiple access in the IEEE 802.15.4 protocol

To offer flexible quality of service to several classes of applications, the medium access control (MAC) protocol of IEEE 802.15.4 wireless sensor networks (WSNs) combines the advantages of a random access with contention with a time division multiple access (TDMA) without contention. Understanding reliability, delay, and throughput is essential to characterizing the fundamental limitations of the MAC and optimizing its parameters. Nevertheless, there is not yet a clear investigation of the achievable performance of hybrid MAC. In this article, an analytical framework for modeling the behavior of the hybrid MAC protocol of the IEEE 802.15.4 standard is proposed. The main challenge for an accurate analysis is the coexistence of the stochastic behavior of the random access and the deterministic behavior of the TDMA scheme. The analysis is done in three steps. First, the contention access scheme of the IEEE 802.15.4 exponential back-off process is modeled through an extended Markov chain that takes into account channel, retry limits, acknowledgements, unsaturated traffic, and superframe period. Second, the behavior of the TDMA access scheme is modeled by another Markov chain. Finally, the two chains are coupled to obtain a complete model of the hybrid MAC. By using this model, the network performance in terms of reliability, average packet delay, average queuing delay, and throughput is evaluated through both theoretical analysis and experiments. The protocol has been implemented and evaluated on a testbed with off-the-shelf wireless sensor devices to demonstrate the utility of the analysis in a practical setup. It is established that the probability density function of the number of received packets per superframe follows a Poisson distribution. It is determined under which conditions the guaranteed time slot allocation mechanism of IEEE 802.15.4 is stable. It is shown that the mutual effect between throughput of the random access and the TDMA scheme for a fixed superframe length is critical to maximizing the overall throughput of the hybrid MAC. In high traffic load, the throughput of the random access mechanism dominates over TDMA due to the constrained use of TDMA in the standard. Furthermore, it is shown that the effect of imperfect channels and carrier sensing on system performance heavily depends on the traffic load and limited range of the protocol parameters. Finally, it is argued that the traffic generation model established in this article may be used to design an activation timer mechanism in a modified version of the CSMA/CA algorithm that guarantees a stable network performance.

Analysis and modelling of YouTube traffic

ABSTRACTYouTube currently accounts for a significant percentage of the Internet's global traffic. Hence, understanding the characteristics of the YouTube traffic generation pattern can provide a significant advantage in predicting user video quality and in enhancing network design. In this paper, we present a characterisation of the traffic generated by YouTube when accessed from a regular PC. On the basis of this characterisation, a YouTube server traffic generation model is proposed, which, for example, can be easily implemented in simulation tools. The derived characterisation and model are based on experimental evaluations of traffic generated by the application layer of YouTube servers. A YouTube server commences the download with an initial burst and later throttles down the generation rate. If the available bandwidth is reduced (e.g. in the presence of network congestion), the server behaves as if the data excess that cannot be transmitted because of the reduced bandwidth were accumulated at a server's buffer, which is later drained if the bandwidth availability is recovered. As we will show, the video clip encoding rate plays a relevant role in determining the traffic generation rate, and therefore, a cumulative density function for the most viewed video clips will be presented. The proposed traffic generation model was implemented in a YouTube emulation server, and the generated synthetic traffic traces were compared with downloads from the original YouTube server. The results show that the relative error between downloads from the emulation server and the original server does not exceed 6% for the 90% of the considered videos. Copyright © 2012 John Wiley & Sons, Ltd.

Service characterization for cost/benefit optimization of enhanced UMTS

In this paper an all-IP Enhanced-Universal Mobile Telecommunications System (E-UMTS) is considered, where enhancements include link level behavior, high-speed downlink packet access (HSDPA) channel, resource management, Diffserv architecture, and Radio Resource Management schemes. An overview of E-UMTS deployment scenarios and service needs is presented based on the views of relevant players. Deployment and mobility scenarios are considered, including expected population density and usage of service mix for three environments, namely offices, urban/vehicular, and business city center. In addition, based on population and service penetration values, E-UMTS traffic generation and activity models are described and characterized. Based on these scenarios and characterizations, system level simulations are carried out and the enhanced service quality performance is demonstrated, including blocking probability, handover failure probability and end-to-end delay in each deployment scenario. By using system level simulations, services and environmental conditions can be mapped into deployment strategies (and supported system capacity) whose evaluation is essential prior to field trials and real implementation. On the one hand, costs depend on the prices of the spectrum, equipment, operation and maintenance, as well as on the number of cells which, in turn, depends on the cell radius. On the other, revenues depend on the price per MB and on the supported throughput. As the goal of operators and service providers is to maximize the profit, the profit in percentage was obtained for the three considered scenarios. Its optimum values are found for cell radii around 31, 257, and 310 m for offices, vehicular and business city center scenarios, respectively.

Analysis of a type II hybrid ARQ strategy in a ds-cdma packet transmission environment

A type II hybrid automatic repeat request scheme is considered as a retransmission strategy in a direct-sequence code-division multiple-access packet mobile radio network. An analysis based on equilibrium point analysis is presented to model the behavior of the system in a message-based traffic generation model. A simulation approach is introduced to validate the proposed analytical model, obtaining results that closely match those derived theoretically.