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Researchers and practitioners have widely studied road network traffic data in different areas such as urban planning, traffic prediction and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install global positioning system(GPS) receivers and administrators must continuously monitor these devices. There have been some urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) Scalability: most of them only offer single-machine solution which is not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) Granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, car following. This paper proposed GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize large-scale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. The experimental analysis shows that GeoSparkSim can simulate the movements of 300 thousand vehicles over a very large road network (250 thousand road junctions and 300 thousand road segments) and outperform the existing competitors.

As a distributed system, Hadoop heavily relies on the network to complete data-processing jobs. While the traffic generated by Hadoop jobs is critical for job execution performance, the actual behaviour of Hadoop network traffic is still poorly understood. This lack of understanding greatly complicates research relying on Hadoop workloads. In this article, we explore Hadoop traffic through empirical traces. We analyse the generated traffic of multiple types of MapReduce jobs, with varying input sizes, and cluster configuration parameters. We present Keddah, a toolchain for capturing, modelling, and reproducing Hadoop traffic, for use with network simulators to better capture the behaviour of Hadoop. By imitating the Hadoop traffic generation process and considering the YARN resource allocation, Keddah can be used to create Hadoop traffic workloads, enabling reproducible Hadoop research in more realistic scenarios.

Summarization: Traffic congestion in urban networks may lead to strong degradation in the utilization of the network infrastructure, which can be mitigated via suitable control strategies. This paper studies and analyzes the performance of an adaptive traffic-responsive strategy that controls the traffic light parameters in an urban network to reduce traffic congestion. A nearly optimal control formulation is adopted to avoid the curse of dimensionality occurring in the solution of the corresponding Hamilton–Jacobi–Bellman (HJB) optimal control problem. First, an (approximate) solution of the HJB is parametrized via an appropriate Lyapunov function; then, the solution is updated at each iteration in such a way to approach the nearly optimal solution, using a close-to-optimality index and information coming from the simulation model of the network (simulation-based design). Simulation results obtained using a traffic simulation model of the network Chania, Greece, an urban traffic network containing many varieties of junction staging, demonstrate the efficiency of the proposed approach, as compared with alternative traffic strategies based on a simplified linear model of the traffic network. It is shown that the proposed strategy can adapt to different traffic conditions and that low-complexity parametrizations of the optimal solution, a linear and a bimodal piecewise linear strategy, respectively, provide a satisfactory trade-off between computational complexity and network performance. Παρουσιάστηκε στο: Transportation Science

The basic idea of Microscopic approach (A1) (2)is that the behavior of each vehicle is affected by neighboring vehicles, and the entire traffic flow is represented as statistical occurrences. The Cellular Automaton (CA) based model (3) (4) and (11) is widely known idea to represent the behavior of each vehicle. In the CA model, the road is discretized into many small cells. Each cell can be either empty or occupied by only one vehicle. The behavior of each vehicle in each cell is specified by the geometrical relationship with other vehicles together with some stochastic parameters. Although many simulation results based on these microscopic models showed high similarity to the measured real data, these approaches are not suitable for the large-scale traffic network modeling and its traffic light controller design. This is because they require enormous computational efforts to find all vehicles’ behavior. Furthermore, the precise information on initial positions and speeds of all vehicles are usually not available in advance. On the other hand, it has been a common strategy in the macroscopic approach (A2) (9) that the designer uses a fluid approximation model where the behavior of traffic flow is regarded as a continuous fluid with density k(x, t) and volume q(x, t) at location x and time t. In this case, k(x, t) and q(x, t) must satisfy the following law of mass conservation

Driving simulators are a common tool to study human responses to driver assistance algorithms interacting with vehicle behavior. The accuracy of human responses, particularly to other vehicles, depends on the fidelity of representing the traffic surrounding the ego-vehicle. The traffic surrounding the ego- vehicle must also react to the ego-vehicle and be updated at a high enough rate such that the driver can perceive continuity. Microscopic traffic simulation tools are well developed to simulate traffic behavior replicating the real world over large networks. But the speed of microscopic simulations depends on the size of the network, traffic volume, simulation's computational hardware, and the traffic simulation software itself. The frequency of updating the traffic in many traffic simulators is not frequent enough for direct use in a driving simulator, thus causing discontinuous jumps in vehicle motion perceived by drivers. However, these traffic simulation results can be made continuous in perception by performing trajectory-level smoothing and time up-sampling as a real-time process occurring parallel to the driving simulator's rendering software. This ensures a realistic human perception of traffic behavior around the ego-vehicle. However, this integration creates the problem of synchronizing the traffic simulation dynamics with the ego-vehicle's dynamics within a driving simulator. This is challenging because traffic simulations are not typically programmed to be real-time, and thus the time offsets of the traffic simulator in relation to real-time can be time-varying. This paper describes a model-predictive feedback control method to synchronize a traffic simulator with a driving simulator in real-time by projecting the ego-vehicle into the future while adjusting time offsets via a feedback loop taking the traffic simulator's speed and communication delays into account. The update rate of the traffic is enhanced using a uniform acceleration observer that utilizes a localized simulation of the traffic network, and thus three dynamic systems are integrated at once: a large-scale network traffic simulation, a local realtime reduced -order model of the traffic, and the behavior of an ego-vehicle. This method of integrating traffic simulation with a driving simulator is demonstrated for a high-speed vehicle motion in a highway driving simulator, and the error analysis shows the success of this approach.

Network traffic simulation is of great significance in testbed for evaluating network security and performance of a system and so on. How to deeply simulate the traffic under the regular load of a business system is a key issue. We designed and developed a distributed container-deployed generator for Web business background traffic. The generator conducts in-depth customized simulation according to the characteristics of the spatio-temporal distribution of traffic, implements the distributions of user behaviors, and fine-tune the system by the feedback and adjustment function. The generator also supports distributed deployment in the form of containers. Experiments show that the traffic generator, which is developed on the basis of the open-source load testing tool Locust, can generate realistic traffic under the real load of the system under test, conform to the traffic characteristics in the real environment, and is easy to use and expand.

In this paper, we consider the traffic engineering problem in a large-scale hierarchical network arising in the next-generation cloud-based wireless networks. We propose a distributed semiasynchronous algorithm for this problem based on the so-called block successive upper bound minimization method of multipliers (BSUM-M). Theoretically, we show that the proposed algorithm converges to the global optimal solution under some assumptions on the degree of network asynchrony. We illustrate the effectiveness and efficiency of the proposed algorithm by comparing it with the state-of-the-art commercial solvers in a networked environment.

This paper presents the characterization of network delays in an IEC61850 process bus substation area network, both through theoretical analysis and simulations. Several design targets were defined considering the recommendations of standards and good design practices: number of network hops, total network delay, probability of the delay being exceeded, link load, network topology and availability. An analytical delay estimation methodology is proposed, considering both the steady-state traffic and traffic resulting from a breaker failure event. A complete substation is taken as an example for characterizing the network delays, considering a star network topology. Simulations allow to obtain the cumulative distribution functions and percentile values of network delays. Results show a good agreement between the simulation and the analytical analysis. While the delay is best characterized statistically through simulation, finding the maximum network delay through simulations can be very time consuming, making the analytical analysis more suitable.

The traditional fuzzy neural network often uses BP algorithm to optimize parameters when conducting parameter identification. However, BP algorithm tends to be trapped in local extremum. In view of the shortcomings of this method, this paper combines the differential evolution algorithm with the BP algorithm, and proposes an improved differential evolution BP algorithm to optimize the fuzzy neural network forecasting network traffic. In order to solve problems such as slow convergence speed and tendency of premature convergence existing in differential evolution algorithm, an improved differential evolution algorithm using the adaptive mutation operator and Gaussian disturbance crossover operator aims to improve the mutation of standard differential evolution algorithm and the design of crossover operators. To validate the effectiveness of it, this optimized fuzzy neural network forecasting algorithm is applied to four standard test functions and the actual network traffic. Simulation results show that the convergence speed and forecasting accuracy of the proposed algorithm are better than those of the traditional fuzzy neural network algorithm. It improves not only the generalization ability of the fuzzy neural network but also the forecasting accuracy of the network traffic.

For Internet service providers to efficiently use network resources, they need to conduct traffic engineering to dynamically control traffic routes to accommodate traffic with limited network resources. The performance of traffic engineering depends on the accuracy of traffic prediction. However, the volume of network traffic has been changing drastically in recent years due to the growth of various types of network services, making traffic prediction increasingly difficult. Our simple ideas to overcome this challenge are to separate traffic into predictable and unpredictable parts and to apply different control policies to predictable and unpredictable traffic. To promote these ideas, we use software-defined networking technology, particularly Open-Flow, that can control macroflows defined by any combination of L2-L4 packet header information such as 5-tuple. In this paper, we therefore propose the macroflow-generating method for separating traffic into predictable macroflows that have little traffic variation and unpredictable macroflows that have large traffic variation within a limited flow table size. We also propose a macroflow-based traffic engineering scheme that uses different routing policies in accordance with traffic predictability. Simulation evaluation results suggest that our proposed scheme can reduce the maximum link load in a network at the most congested time by 34% and the average link load in a network on average by 11% compared with the current traffic engineering schemes.

This paper presents a flexible macroscopic traffic model that is applicable for large scale urban traffic network simulations. The proposed approach combines several advantages. First, the discretized partial differential equation of the macroscopic model is solved numerically with low computational effort. The spatial and temporal resolution of the discretization are tuning parameters to balance computational effort with model accuracy. Second, the model allows arbitrary functional forms of the fundamental diagram defined by a small number of parameters. Thereby, moving density gradients (jam fronts) are represented accurately. The model parameters are physically meaningful and can readily be estimated from measurement data. Third, two general types of intersection handling are proposed and can be combined with different merge models. The first intersection approach is a binary traffic light, similar to real traffic lights. Detailed insights concerning queue length, flow across intersections and routing decisions can be investigated. The second approach is a continuous valve-like approach that allows investigation of averaged effects and large scale interaction and feedback effects. Fourth, the proposed model scales linearly with spatial and temporal resolution as well as network size and can be partially solved in parallel to increase computational efficiency. To demonstrate the above mentioned qualities of the presented model, two realistic example situations and a comprehensive study on the scaling properties are provided.

Road traffic simulation is a useful tool for studying road traffic and evaluating solutions to traffic problems. Large-scale agent-based road traffic simulation is computationally intensive, which triggers the need for conducting parallel simulation. This paper deals with the synchronization problem in parallel agent-based road traffic simulation to reduce the overall simulation execution time. We aim to reduce synchronization operations by introducing some redundant computation to the simulation. There is a trade-off between the benefit of reduced synchronization operations and the overhead of redundant computation. The challenge is to minimize the total overhead of redundant computation and synchronization. First, to determine the amount of redundant computation, we proposed a way to define extended layers of partitions in the road network. The sizes of extended layers are determined by the behavior of agents and the topology of road networks. Second, due to the dynamic nature of road traffic, a heuristic was proposed to adjust the amount of redundant computation according to traffic conditions during simulation run-time to minimize the overall simulation execution time. The efficiency of the proposed method was investigated in a parallel agent-based road traffic simulator using real-world network and trip data. Results have shown that the method can reduce synchronization overhead and improve the overall performance of the parallel simulation significantly.

In this paper, a model-based perimeter control policy for large-scale urban vehicular networks is proposed. Assuming a homogeneously loaded vehicle network and the existence of a well-posed Network Fundamental Diagram (NFD), we describe a protected network throughout its aggregated dynamics including nonlinear exit flow characteristics. Within this framework of constrained optimal boundary flow gating, two main performance metrics are considered: (a) first, connected to the NFD, the concept of average network travel time and delay as a performance metric is defined; (b) second, at boundaries, we take into account additional external network queue dynamics governed by uncontrolled inflow demands. External queue capacities in terms of finite-link lengths are used as the second performance metric. Hence, the corresponding performance requirement is an upper bound of external queues. While external queues represent vehicles waiting to enter the protected network, internal queue describes the protected network’s aggregated behavior. By controlling the number of vehicles joining the internal queue from the external ones, herewith a network traffic flow maximization solution subject to the internal and external dynamics and their performance constraints is developed. The originally non-convex optimization problem is transformed to a numerically efficiently convex one by relaxing the performance constraints into time-dependent state boundaries. The control solution can be interpreted as a mechanism which transforms the unknown arrival process governing the number of vehicles entering the network to a regulated process, such that prescribed performance requirements on travel time in the network and upper bound on the external queue are satisfied. Comparative numerical simulation studies on a microscopic traffic simulator are carried out to show the benefits of the proposed method.

Traffic matrices, abstract representations of demand, are essential for network operators endeavoring to model, measure, maintain, and improve the efficiency of their complex and heterogeneous architectures. Traffic matrix estimation consists in inferring a traffic matrix from link-level measurements. Provoked by the need to enable agile deployment of new services while, at the same time, slashing operating expenditure and energy consumption, the trend in telecommunications is to shift functionality from physical appliances to virtualized services. We analyze the effects of this landscape change on traffic matrices, their dynamics, and their estimation, indicating some new challenges and problems that will arise in all the associated modeling, analysis and evaluation activities.

We simulate model for evolution of local virtual time profile in conservative parallel discrete event the simulation (PDES) algorithm with long-range communication links. The main findings of simulation are that i) growth exponent depends logarithmically on the concentration p of long-range links; ii) utilisation of processing elements time decreases slowly with p. Thismeans that the conservative PDES with long-range communication links is fully scalable.

Hyper cellular network architecture is proposed to separate control coverage and traffic coverage, to realize flexible and energy-efficient network operations. Specifically, the traffic base stations (BSs), which only handle the data services, can be dynamically switched on/off and offload traffic to adjacent traffic base stations or control BSs for energy saving, according to the network load dynamics. With this new feature leveraged, it is crucial and challenging to revisit the problems of network planning, and dynamic BS sleeping and wireless resource allocation, based on the variation of the network traffic load. For the network planning, based on the stochastic geometry theory, the optimal densities of the control BSs and traffic BSs are derived, with respect to their different network functions and topology features. For energy efficient network control, dynamic BS sleeping and spectrum resource allocation mechanisms are proposed and optimized based on traffic offloading, which can substantially reduce the network energy consumption, with the guarantees on the network coverage and user quality of service.

Network traffic model and analysis provides the average load, bandwidth requirements and the different application of the available bandwidth for a particular network, in addition to several other details of the network. This paper presents mathematical model used for modelling real world problems using Botswana International University of Science and technology (BIUST) network traffic as a case. Sophisticated analysis of data is done to model the BIUST network with the succor of statistics, as it implies the collection and interpretation of data through mathematical processes called stochastic processes. From the attained results, the model and estimation of packet traffic distribution for BIUST Network based on Pareto distribution, it was perceived that about 20% of the users had about 80% of the bandwidth consumed.

The paper presents the modelling of a mixed traffic flow in which look-ahead controlled vehicles with a speed control are driven together with conventional vehicles. Since the speed profile of the lookahead control may differ from that of the conventional vehicle, the structure of the traffic flow changes. The paper analyses the impact of vehicles applying look-ahead control strategy on the traffic flow. In a simulation-based analysis it is shown that the fundamental diagram of the traffic network changes. A new mixed-traffic model incorporates the nonlinearity of the traffic dynamics using a polynomial formula, in which the look-ahead cruise control is considered with a parameter-dependent form. Using these nonlinear forms the impact of the look-ahead vehicles on the stability of the traffic is examined through controlled invariant sets. In this analysis the sum-of-squares programming method is applied. The results are demonstrated through simulation examples using the VISSIM simulation environment.

Intelligent Transportation Systems (ITS) focus on increasing the efficiency of existing surface transportation systems through the use of advanced computers, electronics, and communication technologies. In order to perform advanced traffic management and provide travel information, dynamic traffic assignment models need to be developed to provide time-dependent estimates of traffic flows on networks in order to efficiently utilize possible advanced traffic information as well as traffic control measures. Traffic assignment distributes Origin-Destination (OD) trips in a network and determines the flow patterns in a traffic network. This research aims at developing simulation-based algorithm for dynamic traffic assignment problems under mixed traffic flow considerations. Four different physical vehicle types are explicitly considered and modeled, including car, bus, motorcycle, and truck. Four different behavioral rules, pre-specified-path driver, user-equilibrium driver, system-optimization driver, and real-time information driver, are considered in the solution procedure. The DTA algorithm consists of an inner loop that incorporates a direction finding mechanism for the search process for System Optimization (SO) and User Equilibrium (UE) classes based on the simulation results of the current iteration, including experienced vehicular trip times and marginal trip times. In order to understand tripmaker acceptance toward route guidance, a survey is conducted to explore possible behavioral classifications and associated percentages. Numerical experiments are conducted in a test network and a real city network to illustrate the capabilities of the simulation-based DTA procedures, and to observe how system performs under multiple user class’s conditions, including multiple user behavior rules and multiple physical vehicle classes.

Intelligent traffic management can be considered one of the most promising solutions to contemporary traffic problems. The traffic in transportation associated with emergency conditions including psychiatric improvement in transport network, allocation of variable traffic flows, reducing the number of the crowded traffic roads and paths as well as its negative effects (such as delays, waiting time, stress of driver, noise and air pollution, and blocking the assistive devices). This research has been used by new multi-agent systems to manage traffic. On the one hand, the proposed algorithm includes traffic flow improvement in emergency conditions until it is considered as real-time traffic information and in other hand, by preventing the increase the volume of a paths have effects in the reduce of crowded traffic situations at a specific time (for example, providing the proposed paths in the shortest time). In this article, the integrated environment is including JACK software for having a virtual agent behavior simulation. In general, we can use the different simulation form in a distribution network to display the crowded and traffic. In this article, the JACK software is used for having the explicit capabilities and supporting the common of this software in modelling the multi-agent systems, such as agents, design, event and capabilities. In addition, designing and analyzing of this interaction is simplest than the existed designs in JACK software. As a result of the proposed model, it seems reasonable that the proposed approach is different than previous works in each case. On the one hand, there are modeling system in the different tasks as intelligent agents dependent to present the simple and effective road network. In this case, it may correct and changes the actions of driver in emergency conditions by the concept of agent cooperation for achieving the common target.

Transportation agencies often resort to the use of traffic simulation models to evaluate the impacts of changes in network design or network operations. They often have multiple traffic simulation tools that cover the network area where changes are to be made. These multiple simulators may differ in their modeling assumptions (e.g., macroscopic versus microscopic), in their reliability (e.g., quality of their calibration), as well as in their modeling scale (e.g., city-scale versus regional-scale). The choice of which simulation model to rely on, let alone of how to combine their use, is intricate. A larger-scale model may, for instance, capture more accurately the local-global interactions; yet may do so at a greater computational cost. This paper proposes an optimization framework that enables multiple simulation models to be jointly and efficiently used to address continuous urban transportation optimization problems. We propose a simulation-based optimization algorithm that embeds information from both a high-accuracy low-efficiency simulator and a low-accuracy high-efficiency simulator. At every iteration, the algorithm decides which simulator to evaluate. This decision is based on an analytical approximation of the accuracy loss as a result of running the lower-accuracy model. We formulate an analytical expression that is based on a differentiable and computationally efficient to evaluate traffic assignment model. We evaluate the performance of the algorithm with a traffic signal control problem on both a small network and a city network. We show that the proposed algorithm identifies signal plans with excellent performance, and can do so at a significantly lower computational cost than when systematically running the high-accuracy simulator. The proposed methodology contributes to enable large-scale high-resolution traffic simulation models to be used efficiently for simulation-based optimization. More broadly, it enables the use of multiple simulation models that may differ, for instance, in their scale, their resolution, or their computational costs, to be used jointly for optimization.

Mesoscopic traffic simulation is a promising technology for handling large-scale dynamic traffic assignment problems. Meanwhile, GPU has been a research hotspot in the last decade because of its massive parallel performance compared with CPU. Although several works deal with GPU-based parallel performance, few have addressed the issue of real-world network applications. Thus, the performance of GPU on realistic road networks is still not clear. In this paper, an ideal grid network and a realistic Singapore expressway network are implemented and compared. The former gets a speedup of 10.00 while the latter only get a speedup of 2.37. The two main reasons for this lower efficiency of a real network are the more complex data structure caused by network topology and fewer numbers of threads due to the scale of the expressway network. Future works need more effort to develop a system with better data structure and better designed concurrent threads.

Traffic matrix (TM) describes the volumes of traffic between a set of sources and destinations in a network. As an important parameter, TMs are used in a variety of network engineering tasks, such as traffic engineering, capacity planning and anomaly detection. However, it is a challenge to reliably measure TMs in practice. For example, due to flaws in the measurement systems and possible failure in data collection systems, missing values are unavoidable. It is important to recover the missing data from the partial direct measurements. Existing matrix completion methods do not fully consider network traffic behavior and traffic hidden characteristic. Their completion accuracy tends to be significantly worse when the data loss rate is high. In this paper, we perform a study on intrinsic characteristics of network traffic by analyzing real-world traffic trace data, which reveals that traffic has the features of temporal stability and spatial affinity. According to traffic spatial feature, we model TM as multi-Gaussian distributions, which describes the actual network traffic more accurately. Furthermore, we propose a novel matrix completion method based on multi-Gaussian models to estimate the missing traffic data. Finally, we utilize traffic temporal characteristic to further optimize traffic matrix completion for the missing data interpolation. Our proposed approach has been evaluated utilizing real-world traffic trace data. The extensive experiments demonstrate that our method achieves significantly better performance compared with the state-of-the-art interpolation methods.

This paper describes a computationally efficient parallel-computing framework for mesoscopic transportation simulation on large-scale networks. By introducing an overall data structure for mesoscopic dynamic transportation simulation, we discuss a set of implementation issues for enabling flexible parallel computing on a multi-core shared memory architecture. First, we embed an event-based simulation logic to implement a simplified kinematic wave model and reduce simulation overhead. Second, we present a space-time-event computing framework to decompose simulation steps to reduce communication overhead in parallel execution and an OpenMP-based space-time-processor implementation method that is used to automate task partition tasks. According to the spatial and temporal attributes, various types of simulation events are mapped to independent logical processes that can concurrently execute their procedures while maintaining good load balance. We propose a synchronous space-parallel simulation strategy to dynamically assign the logical processes to different threads. The proposed method is then applied to simulate large-scale, real-world networks to examine the computational efficiency under different numbers of CPU threads. Numerical experiments demonstrate that the implemented parallel computing algorithm can significantly improve the computational efficiency and it can reach up to a speedup of 10 on a workstation with 32 computing threads.

With the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks used in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations.

The properly adjust traffic lights have a significant impact on traffic flow dynamics. At the same time the traffic flows have a significant impact on the environment and especially significant is the noise. This article presents the different effect of traffic lights switching period to the results of traffic flow dynamics. Results were achieved by using discrete model of traffic flow. For the modelling a line of road marking its points with the traffic lights is created. For the traffic lights the appropriate switching period was determined. Results presents the simulated dynamic of traffic flows also determining the variation of the velocity, flow, density and the total number of vehicles. The results can help to determine the most appropriate traffic lights switching periods for the dynamics of traffic flows.

In order to ensure efficient functioning of multimodal transport hub it is necessary to provide proper traffic management both inside and outside of it. This article presents assessment of freight traffic flows management impact on the state of traffic flows in adjoining street-road network. This is one of actual tasks aiming to ensure traffic safety in proximity to large terminal complexes. The following results were obtained in the course of work: – limiting values of street-road network stable functioning parameters for infinite horizon were defined; – periods of hub steady functioning in condition of negative stability were defined with the use of traffic micro simulation programs – a non-conventional approach in solving the problems of assessment of functioning of terminal complexes and adjoining street-road networks. – conditions of implementation of adaptive simulation model enabling execution of transport flows within the terminal were defined.

Peer-to-Peer (P2P) service may damage the interests of Internet Service Provider (ISP) because P2P traffic usually takes a lot of network link bandwidth and even overwhelms some network links. Aimed at the problem, mainstream solutions are usually optimizing P2P traffic through the interaction between applications and underlying network. However, current solutions still have two aspects of defects: one is that the interacted underlying network status information is immutable and can’t reflect the real-time dynamic changes because it is usually configured by ISP. The other is that some solutions may cause excessive traffic localization, which may greatly influence other services in the local network. In order to improve the above two defects and provide P2P users with better service experience, we propose an enhanced application layer traffic optimization scheme, in which more valuable network status information of underlying network is dynamically calculated and provided to P2P application. Extensive simulations demonstrate that our P2P traffic optimization scheme is superior to other solutions in terms of available bandwidth, resource transmission delay and user service experience.

The paper reports the results of studies of changes in parameters of two-component model of the kinetic theory of transport flows. The paper states a ratio between cars stopping at the same time in the network, specific travel time and specific standing time under varying traffic conditions. The dependencies obtained allow forecasting changes in traffic conditions considering different levels of traffic organization. Also, the paper addresses the issue of application of information obtained from probing cars to assess traffic conditions on the road network and proposes an algorithm to simulate the required level of the road network saturation using probing cars.

With regard to cyber security, pervasive traffic visibility is one of the most essential functionalities for complex network systems. A traditional network system has limited access to core and edge switches on the network; on the other hand, SDN technology can provide flexible and programmable network management operations. In this article, we consider the practical problem concerning how to achieve scalable traffic measurement using SDN functionalities. Less intrusive traffic monitoring can be achieved by using a packet sampling technique that probabilistically captures data packets at switches, and the sampled traffic is steered toward a traffic analyzer such as an IDS on SDN. We propose the use of a centrality measure in graph theory for deciding the traffic sampling points among the switches. In addition, we discuss how to decide the traffic sampling rates at the selected switches. The results of the simulation and SDN testbed experiments indicate that the proposed sampling point and rate decision methods enhance the intrusion detection performance of an IDS in terms of malicious traffic flows in large-scale networks.

Software Defined Networking (SDN) with defining characteristics, such as “separation of data and control plane” and “centralizing network control with decision making”, has significantly simplified network management. However, active monitoring techniques used to dynamically measure network traffic introduce additional overheads in the network, while a passive approach lacks accuracy in terms of traffic measurement. As a result, various efforts have been devoted to designing per-flow based network measurement system to address both accuracy and overhead challenges. Existing measurement techniques lack a multi-objective network measurement mechanism to overcome various overheads, like communication cost, controller computation, and accuracy in a real-time environment. Therefore, this paper presents a novel and practical solution to enable accurate real-time traffic matrix for the traffic measurement system in SDN. The solution is proposed to measure fine-grained monitoring task with less controller communication and computational cost with high accuracy. The solution is based on two measurement designs, namely: fixed and elastic schemas. Our experiments demonstrate that both fixed and elastic schemas achieve significant overhead reduction without compromising on accuracy. Refereed/Peer-reviewed

This paper presents a Dynamic Traffic Assignment (DTA)-based microsimulation model that assesses traffic impacts on transport network during sudden closure of a critical infrastructure (CI) in Halifax, Canada. DTA models can replicate actual traffic conditions considering long queues and spillback. The model is calibrated and validated using field traffic data and used to predict traffic flows in the network. The model suggests an increment of 6 minutes in average traffic delay and 24.5% reduction in the number of arrived vehicles during the given incident. The model also reports spillover effects in the network.

A problem of traffic flows control in congestion conditions is considered. Control is performed on intersections using optimal traffic lights phases durations. The traffic lights phases durations are searched using previously proposed traffic flows mathematical model which is based on the controlled networks theory and evolutionary algorithms. To present a large scale road networks the model contains the connection matrices that describe interactions between input and output roads in subnetworks. Results obtained on traffic simulation software CTraf are given.

Traffic simulation models are capable of evaluating traffic management measures determining their impacts to mobility and environment. This paper presents a study that evaluates traffic management measures designed for the central area of the city of Thessaloniki, Greece, and estimates the environmental effects generated by the proposed measures. The analysis was performed with the use of the traffic simulation software Aimsun. Traffic assignment was conducted both statically and dynamically and all possible comparisons among the tested scenarios were performed. Travel time, delay time, queues and speed were the traffic performance measurements used in the evaluation process. Fuel consumption and carbon dioxide (CO2) emissions were estimated for the determination of the environmental impacts. A sensitivity analysis was also conducted in order to evaluate the effects caused by the variation in traffic demand. Different scenarios regarding particular traffic management schemes were tested and the outcomes indicated that mobility in the centre of the city can be improved in qualitative terms if more public space is given to the mass transport and other modes of mobility such as biking and walking.

This paper presents a real-time traffic network state estimation and prediction system with built-in decision support capabilities for traffic network management. The system provides traffic network managers with the capabilities to estimate the current network conditions, predict congestion dynamics, and generate efficient traffic management schemes for recurrent and non-recurrent congestion situations. The system adopts a closed-loop rolling horizon framework in which network state estimation and prediction modules are integrated with a traffic network manager module to generate efficient proactive traffic management schemes. The traffic network manger adopts a meta-heuristic search mechanism to construct the schemes by integrating a wide variety of control strategies. The system is applied in the context of Integrated Corridor Management (ICM), which is envisioned to provide a system approach for managing congested urban corridors. A simulation-based case study is presented for the US-75 corridor in Dallas, Texas. The results show the ability of the system to improve the overall network performance during hypothetical incident scenarios.

Network traffic analysis has been one of the most crucial techniques for preserving a large-scale IP backbone network. Despite its importance, large-scale network traffic monitoring techniques suffer from some technical and mercantile issues to obtain precise network traffic data. Though the network traffic estimation method has been the most prevalent technique for acquiring network traffic, it still has a great number of problems that need solving. With the development of the scale of our networks, the level of the ill-posed property of the network traffic estimation problem is more deteriorated. Besides, the statistical features of network traffic have changed greatly in terms of current network architectures and applications. Motivated by that, in this paper, we propose a network traffic prediction and estimation method respectively. We first use a deep learning architecture to explore the dynamic properties of network traffic, and then propose a novel network traffic prediction approach based on a deep belief network. We further propose a network traffic estimation method utilizing the deep belief network via link counts and routing information. We validate the effectiveness of our methodologies by real data sets from the Abilene and GÉANT backbone networks.

Road network traffic data has been widely studied by researchers and practitioners in different areas such as urban planning, traffic prediction and spatial-temporal databases. The existing urban traffic simulators suffer from two critical issues (1) scalability: most of them only offer single-machine solutions which are not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, and car following. In the paper, we propose GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize large-scale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. A full-fledged prototype of GeoSparkSim is implemented in Apache Spark. In this demonstration, we will show the attendees how to issue GeoSparkSim simulation tasks via the user interface, visualize simulated vehicle movements, and monitor the backend Spark cluster status.

Road network traffic data has been widely studied by researchers and practitioners in different areas such as urban planning, traffic prediction, and spatial-temporal databases. For instance, researchers use such data to evaluate the impact of road network changes. Unfortunately, collecting large-scale high-quality urban traffic data requires tremendous efforts because participating vehicles must install GPS receivers and administrators must continuously monitor these devices. There has been a number of urban traffic simulators trying to generate such data with different features. However, they suffer from two critical issues (1) scalability: most of them only offer single-machine solution which is not adequate to produce large-scale data. Some simulators can generate traffic in parallel but do not well balance the load among machines in a cluster. (2) granularity: many simulators do not consider microscopic traffic situations including traffic lights, lane changing, car following. In the paper, we propose GeoSparkSim, a scalable traffic simulator which extends Apache Spark to generate large-scale road network traffic datasets with microscopic traffic simulation. The proposed system seamlessly integrates with a Spark-based spatial data management system, GeoSpark, to deliver a holistic approach that allows data scientists to simulate, analyze and visualize largescale urban traffic data. To implement microscopic traffic models, GeoSparkSim employs a simulation-aware vehicle partitioning method to partition vehicles among different machines such that each machine has a balanced workload. The experimental analysis shows that GeoSparkSim can simulate the movements of 200 thousand vehicles over a very large road network (250 thousand road junctions and 300 thousand road segments).

A full methodology of short-term traffic prediction is proposed for urban road traffic network via Artificial Neural Network (ANN). The goal of the forecasting is to provide speed estimation forward by 5, 15 and 30 min. Unlike similar research results in this field, the investigated method aims to predict traffic speed for signalized urban road links and not for highway or arterial roads. The methodology contains an efficient feature selection algorithm in order to determine the appropriate input parameters required for neural network training. As another contribution of the paper, a built-in incomplete data handling is provided as input data (originating from traffic sensors or Floating Car Data (FCD)) might be absent or biased in practice. Therefore, input data handling can assure a robust operation of speed forecasting also in case of missing data. The proposed algorithm is trained, tested and analysed in a test network built-up in a microscopic traffic simulator by using daily course of real-world traffic. First Published Online:4 Sept 2017

In order to defend a cloud computing system from security attackers, an intrusion detection system (IDS) is widely used to inspect suspicious traffic on the network. However, the processing capacity of an IDS is much smaller than the amount of traffic to be inspected in a large-scaled network system. In this paper, we propose a traffic sampling strategy for software-defined networking (SDN) that fully utilizes the inspection capability of malicious traffic, while maintaining the total aggregate volume of the sampled traffic below the inspection processing capacity of the IDS. We formulate an optimization problem to find an appropriate sampling rate for each switch, and sample the traffic flows in the network according to the optimal sampling rates using the SDN functionalities. The simulation and experimental results indicate that the proposed approach significantly enhances the inspection performance of malicious traffic in large-sized networks.

With the rapid development of a cloud computing network, the network security has been a terrible problem when it provides much more services and applications. Network traffic modeling and analysis is significantly crucial to detect some lawless activities such as DDoS, virus and worms, and so on. Meanwhile, it is a common approach for acquiring a traffic matrix, which can be used by network operators to carry out network management and planning. Although a great number of methods have been proposed to model and analyze the network traffic, it is still a remarkable challenge since the network traffic characterization has been tremendously changed, in particular, for a cloud computing network. Motivated by that, we analyze and model the statistical features of network traffic based on the Bayesian network in this paper. Furthermore, we propose an accurate network traffic estimation approach and an efficient anomaly detection approach, respectively. In detail, we design a Bayesian network structure to model the causal relationships between network traffic entries. Based on this Bayesian network model, we obtain a joint probability distribution of network traffic by the maximum a posteriori approach. Then, we estimate the network traffic in terms of a regularized optimization model. Meanwhile, we also perform anomaly detection based on the proposed Bayesian network structure. We finally discuss the effectiveness of the proposed method for traffic matrix estimation and anomaly detection by applying it to the Abilene and GEANT networks.