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The cloud-assisted mobile edge computing system is a critical architecture to process computation-intensive and delay-sensitive mobile applications in close proximity to mobile users with high resource efficiency. Due to the heterogenous dynamics of task arrivals at edge nodes and the distributed nature of the system, the workloads of edge nodes are prone to be unbalanced, which can cause high task response time and resource cost. This paper solves the dynamic task scheduling problem in cloud-assisted mobile edge computing (including both peer task scheduling among edge nodes and cross-layer task scheduling from edge nodes to the cloud), aiming at minimizing average task response time within resource budget limit. To overcome the challenges of task arrival dynamics, edge node heterogeneity, and computation-communication delay tradeoff, we propose a Water-filling Based Dynamic Task Scheduling (WiDaS) algorithm. WiDaS dynamically tunes the usage of cloud resources based on the Lyapunov optimization method and efficiently schedules mobile tasks among edge nodes (and the cloud) by exploiting the idea of water filling. Extensive simulations are conducted to evaluate WiDaS under a trace-driven traffic pattern and two mathematic traffic patterns. The results demonstrate that WiDaS shows two-fold benefits of efficiency and effectiveness.

SUMMARYCohesin-mediated loop extrusion folds interphase chromosomes at the ten to hundreds kilobases scale. This process produces structural features such as loops and topologically associating domains. We identify three types of cis-elements that define the chromatin folding landscape generated by loop extrusion. First, CTCF sites form boundaries by stalling extruding cohesin, as shown before. Second, transcription termination sites form boundaries by acting as cohesin unloading sites. RNA polymerase II contributes to boundary formation at transcription termination sites. Third, transcription start sites form boundaries that are mostly independent of cohesin, but are sites where cohesin can pause. Together with cohesin loading at enhancers, and possibly other cis-elements, these loci create a dynamic pattern of cohesin traffic along the genome that guides enhancer-promoter interactions. Disturbing this traffic pattern, by removing CTCF barriers, renders cells sensitive to knock-out of genes involved in transcription initiation, such as the SAGA and TFIID complexes, and RNA processing such DEAD-Box RNA helicases. In the absence of CTCF, several of these factors fail to be efficiently recruited to active promoters. We propose that the complex pattern of cohesin movement along chromatin contributes to appropriate promoter-enhancer interactions and localization of transcription and RNA processing factors to active genes.HIGHLIGHTSAt least three types of chromatin boundaries regulate a cohesin traffic pattern.The cohesin traffic pattern guides enhancer-promoter interactions.Removing CTCF renders cells sensitive to deletion of RNA processing and gene regulation genes.Depleting CTCF affects localization of RNA processing and gene regulatory proteins.

Traffic signal control is a critical method that ensures the efficiency of traffic flow in cities across the world. There are massive studies that focus on generating optimal signal timing plans. Most of the these studies are model-based, where the signal plan is determined by optimization models with fixed parameters. Reinforcement learning (RL) is a model-free method that learns the optimal control policy over time which avoids limitations of traditional methods. In the past a few years, deep RL methods become particularly attractive since deep neural networks can provide scalable learning capability given the complex traffic condition in real life. In this manuscript, we develop a deep actor-critic method that can provide efficient traffic signal plans. First, a novel deep neural network model is applied to mine the recent traffic condition information as a sequence of temporally sequential image representations of the intersection, which improves from relying solely on limited traffic information such as queue length; then the deep neural network model is integrated with an actor-critic model which avoids drawbacks from the value-based or the policy-based methods. Simulation experiments illustrate that the deep actor-critic method outperforms the classic model-based method and several existing deep reinforcement learning methods according to different measures such as queue length, average delay time, and throughput.

The rapid progress of urbanization has expedited the process of urban planning, e.g., new residential, commercial areas, which in turn boosts the local travel demand. We propose a novel "off-deployment traffic estimation problem", namely, to foresee the traffic condition changes of a region prior to the deployment of a construction plan. This problem is important to city planners to evaluate and develop urban deployment plans. However, this task is challenging. Traditional traffic estimation approaches lack the ability to solve this problem, since no data about the impact can be collected before the deployment and old data fails to capture the traffic pattern changes. In this paper, we define the off-deployment traffic estimation problem as a traffic generation problem, and develop a novel deep generative model TrafficGAN that captures the shared patterns across spatial regions of how traffic conditions evolve according to travel demand changes and underlying road network structures. In particular, TrafficGAN captures the road network structures through a dynamic filter in the dynamic convolutional layer. We evaluate our TrafficGAN using a large-scale traffic data collected from Shenzhen, China. Results show that TrafficGAN can more accurately estimate the traffic conditions compared with all baselines.

Analyzing the traffic state of large citywide networks is an inherently difficult task. Various data issues, traffic signals, stops signs and other flow inhibitors of the network-level traffic state make the analysis more difficult than that under the small-scale local traffic state. To address this challenge, we propose a method based on spatio-temporal non-negative matrix factorization (ST-NMF), which is used for road network traffic pattern analysis. The method can be further extended to traffic data reconstruction and traffic prediction. In order to analyze traffic patterns, the proposed spatio-temporal non-negative matrix factorization model represents the network traffic as a linear combination of several basic patterns, which is also interpreted as the dynamics of spatial traffic characteristics over time in low-dimensional space. By the visual display of the spatial and temporal patterns and the assistance of clustering methods, the traffic pattern features are extracted. In the extended applications, data reconstruction relies on the sampling representation of missing data by ST-NMF, and data prediction is based on the prediction of the temporal patterns by ST-NMF. Through our method, we can not only obtain a high-quality data foundation, but also explore typical spatio-temporal patterns and general predictions of the future traffic state. The analysis results have important guiding significance on the management of intelligent transportation systems. Experiments on real-world traffic data are provided to verify the validity of our proposed approach.

The broad deployment of 802.11 (a.k.a., WiFi) access points and significant enhancement of the energy efficiency of these wireless transceivers has resulted in increasing interest in building 802.11-based IoT systems. Unfortunately, the main energy efficiency mechanisms of 802.11, namely PSM and APSD, fall short when used in IoT applications. PSM increases latency and intensifies channel access contention after each beacon instance, and APSD does not inform stations about when they need to wake up to receive their downlink packets. In this paper, we present a new mechanism---edge-assisted predictive sleep scheduling (EAPS)---to adjust the sleep duration of stations while they expect downlink packets. We first implement a Linux-based access point that enables us to collect parameters affecting communication latency. Using this access point, we build a testbed that, in addition to offering traffic pattern customization, replicates the characteristics of real-world environments. We then use multiple machine learning algorithms to predict downlink packet delivery. Our empirical evaluations confirm that when using EAPS the energy consumption of IoT stations is as low as PSM, whereas the delay of packet delivery is close to the case where the station is always awake.

Edge computing pushes computation and storage resources to the network edge which is close to end users, and thus is critical for latency-sensitive applications, e.g., intelligent Vehicular Ad Hoc Networks (VANETs). To enable these services, a set of edge servers need to be deployed to the roadsides. Such deployment should offer low-latency services to end users, while keeping a low deployment or maintenance cost, which is a non-trivial task. In this paper, we study the edge server placement problem in a metropolitan area. This problem is composed of two parts: to determine 1) the locations of the servers and 2) the coverage of each server, with multiple optimization objectives. First, we study the Shanghai Taxi Trace to gain insights into the traffic pattern of taxis, especially how vehicles move between different locations. Second, we build multi-objective optimization models to characterize the trade-off among three critical performance metrics, namely the initial deployment cost, the runtime cost (i.e., number of hand-offs between different servers), and the average delay of tasks. Due to the intractability of these NP-hard problems, we propose a heuristic multi-objective optimization method to decompose the global problem into a set of local problems with tractable scale. Numerical results verify that our heuristic strategy achieves a desirable balance among the three performance metrics, e.g., a 5% compromise of the delay can reduce up to 50% of the hand-offs for small local areas, and 10%+ for the entire global area, compared with the best existing algorithms.

Unmanned aerial vehicles (UAVs) have played an important role in wireless communications due to the advantages such as highly controllable mobility in three-dimensional (3D) space, swift deployment, line-of-sight (LoS) aerial-ground links, and so on. In this paper, we consider a UAV-enabled two-way relaying system where the UAV relay assists the information exchange between two ground users (GUs) under three different schemes, i.e., four-slot multi-hopping (FSMH) without network coding (NC), three-slot NC and two-slot physical NC (PNC). Firstly, the capacity region of each scheme in this relaying system is analyzed. Then, we maximize the system average sum rate by jointly optimizing the time resources allocation, transmission powers of the transceivers, and the UAV trajectory subject to the constraints on UAV mobility and information causality under each scheme. To solve those problems, we propose an iterative algorithm by applying the successive convex approximation and block coordinate descent techniques. Specifically, the time resources allocation, transmission powers and the UAV trajectory are alternatively optimized in each iteration. In addition, the non-convex trajectory optimization problem is solved by successively solving an approximate convex optimization problem. To gain more insights, we also investigate the performance of those three schemes with symmetric and asymmetric traffic respectively by introducing a new traffic pattern constraint. Numerical results show that the proposed relaying schemes with moving relay can achieve great throughput gains as compared to the conventional scheme with static relay. The three relay schemes also show great performance heterogeneity under different traffic patterns.

Emerging mobility technologies are changing the transportation system landscape. This is especially evident at airports, such as the Dallas-Fort Worth International Airport (DFW). Without careful analysis, these changes could lead to inefficient and costly airport operations. This paper presents a modeling framework that integrates travel mode encoding, demand projection, and microsimulation to enable airports to develop, simulate, and evaluate curbside traffic managements policies and measure their impact. The framework is utilized to analyze several traffic scenarios and policies for DFW: a baseline scenario which represents DFW traffic pattern as observed in 2018 and projected to 2045, a transit network company (TNC) electrification policy, a TNC queuing policy, a policy that increased transit ridership, a bus-only policy which considers the use of only buses inside DFW, an autonomous vehicle (AV) policy which investigates the impact of autonomous vehicle (AV) adoption on airport operations, and an example COVID-19 scenario which models the impact of the COVID19 pandemic. The simulations’ results demonstrate that: increasing the DFW transit ridership postpones the need for airport curbside expansion the most; encouraging shared-mobility with the bus-only policy produces the most savings in curbside congestion delays; automation and electrification for all passenger vehicle trips to/from DFW generates the most saving in fuel consumption and emissions; and uncontrolled AV adoption incurs the highest increase in fuel consumption, delay, and emissions and could require immediate airport capacity extension. Without policy intervention or investment in additional infrastructure capacity, these results predict the current operations would face significant congestion on high demand days starting as early as 2028. While derived in close partnership with DFW, the methodology presented here can be generalized to any airport.

High-radix hierarchical structures, such as the dragonfly, fat-tree, and torus, are cost-effective topologies for high-performance computer (HPC) networks. In these networks, dragonfly outperforms traditional topologies such as fat-tree and torus in cost and scalability. However, network congestion occurs due to the imbalanced traffic pattern, which can lead to degraded performance. The routing algorithm influences the performance of the dragonfly topology in many ways. Routing algorithm can be designed to avoid saturating global or local links, and to avoid deadlock in the network. In this letter, we introduce an adaptive multi-level routing (AMLR) for dragonfly networks. AMLR has three-level routes. By dividing these routes meticulously, all paths of the network can be used more effectively. Traffic between groups will be more balanced. In particular, we propose a congestion control scheme to cooperate with AMLR in the data transmission process. Furthermore, congestion detection and notification are leveraged to identify congested packet and inform the network. Evaluations show that the proposed adaptive multi-level routing and congestion control mechanism can relieve the imbalance between groups in the 100-node dragonfly topology. As a result, AMLR provides 26%, 98%, 78%, and 99% lower latencies, and 13%, 87%, 13%, and 128% higher throughputs compared to the shortest routing under uniform, adv+i, hotspot, and permutation traffic, respectively.

Most of the existing traffic signal control strategies are hard to satisfy the real-time requirements of traffic big data analysis, knowledge reasoning and decision making for sophisticated traffic dynamics and heterogeneous intersection structures in the context of Internet of Vehicles (IoV). In this paper, we attempt to propose a cooperative multi-agent actor–critic (CMAC) deep reinforcement learning (DRL) approach with value decomposition based on edge computing architecture. The intuition behind CMAC is to decompose the global actor–critic learning tasks into several local actor–critic​ sub-problems with respect to each intersection. Each agent searches the local optimal decision by actor–critic network that takes the discrete state encoding about several consecutive frames of image-like traffic states as the inputs of the network. Among them, the green ratio output strategy considering multiple constraints is formulated in the output layer of the actor network, so that the continuous control of traffic signals using multi-agent DRL (MADRL) can be realized. Furthermore, a cooperative mechanism that considers contribution weight distributions of local agents to the global traffic pattern is proposed to coordinate multiple local agents to evolve toward global optimization. Especially, some parallel training tasks of CMAC with a large number of computing loads are deployed on the cloud side in the edge computing architecture to accelerate learning and reconstructing knowledge. The well-trained multi-agent model is downloaded from the cloud side into the edge side for real-time decision making of traffic network flow adaptive control. Simulation results with regard to a realistic traffic network demonstrate that the proposed CMAC approach under edge computing architecture outperforms the value-decomposition based multi-agent actor–critic (VMAC), independent multi-agent actor–critic (IMAC), and the fixed timing control (FTC) in terms of alleviating traffic congestion.

Short-term traffic forecasting is important for the development of an intelligent traffic management system. Critical to the performance of the traffic prediction model utilized in such a system is accurate representation of the spatiotemporal traffic characteristics. This can be achieved by integrating spatiotemporal traffic information or the dynamic traffic characteristics in the modeling process. The currently employed spatiotemporal k-nearest neighbor (STKNN) model is based on the spatial heterogeneity and adaptive spatiotemporal parameters of the traffic to improve the prediction accuracy. However, the non-stationary characteristics of the traffic cannot be fully represented by simply modeling the entire time range or all the time partitions based on experience. We therefore developed a dynamic STKNN model (D-STKNN) for short-term traffic forecasting based on the non-stationary spatiotemporal pattern of the road traffic. The different traffic patterns along the road are first automatically determined using an affinity propagation clustering algorithm. The Warped K-Means algorithm is then used to automatically partition the time periods for each traffic pattern. Finally, the D-STKNN model is developed based on the three-dimensional spatiotemporal tensor data models for the different road segments with different traffic patterns during different time periods. The D-STKNN model was verified through extensive experiments performed using actual vehicular speed datasets collected from city roads in Beijing, China, and expressways in California, U.S.A. The proposed model outperforms existing seven baselines in different time periods under different traffic patterns. The results confirmed the imperative of considering the non-stationary spatiotemporal traffic pattern in developing a model for short-term traffic prediction.

Purpose This study aims to study the connected vehicle (CV) impact on highway operational performance under a mixed CV and regular vehicle (RV) environment. Design/methodology/approach The authors implemented a mixed traffic flow model, along with a CV speed control model, in the simulation environment. According to the different traffic characteristics between CVs and RVs, this research first analyzed how the operation of CVs can affect highway capacity under both one-lane and multi-lane cases. A hypothesis was then made that there shall exist a critical CV penetration rate that can significantly show the benefit of CV to the overall traffic. To prove this concept, this study simulated the mixed traffic pattern under various conditions. Findings The results of this research revealed that performing optimal speed control to CVs will concurrently benefit RVs by improving highway capacity. Furthermore, a critical CV penetration rate should exist at a specified traffic demand level, which can significantly reduce the speed difference between RVs and CVs. The results offer effective insight to understand the potential impacts of different CV penetration rates on highway operation performance. Originality/value This approach assumes that there shall exist a critical CV penetration rate that can maximize the benefits of CV implementations. CV penetration rate (the proportion of CVs in mixed traffic) is the key factor affecting the impacts of CV on freeway operational performance. The evaluation criteria for freeway operational performance are using average travel time under different given traffic demand patterns.

Software Defined Networking (SDN) is regarded as the next generation paradigm as it simplifies the structure of the data plane and improves the resource utilization. However, in current Software Defined Communication Systems (SDCSs), the maximum or minimum metric value based routing strategies come from traditional networks, which lack the ability of self-adaptation and do not efficiently utilize the computation resource in the controllers. To solve these problems, in this paper, we utilize the deep learning technique to conduct the routing computation for the SDCSs. Specifically, in our proposal, the considered Convolutional Neural Networks (CNNs) are adopted to intelligently compute the paths according to the input real-time traffic traces. To reduce the computation overhead of the central controller and improve the adaptation of CNNs to the changing traffic pattern, we consider an online training manner. Analysis shows that the computation complexity can be significantly reduced through the online training manner. Moreover, the simulation results demonstrate that our proposed CNNs are able to compute the appropriate paths combinations with high accuracy. Furthermore, the adopted periodical retraining enables the deep learning structures to adapt to the traffic changes.

There are significant spatiotemporal correlations among the traffic flows of neighboring road sections in the road network. Correctly identifying such correlations makes an essential contribution for improving the accuracy of traffic flow prediction. Many efforts have been made by several researchers to solve this issue, but they assume that the spatiotemporal correlations among traffic flows are stationary in both time and space, i.e., the degrees to which traffic flows affect each other are fixed. In this study, we propose a clustering based traffic flow prediction method that considers the dynamic nature of spatiotemporal correlations. In order to express the short-term dependence between the target road section and neighboring ones, the spatiotemporal correlation matrices are introduced. The historical traffic data are divided into several clusters according to the similarity between spatiotemporal correlation matrices. The spatiotemporal correlation analysis and the predictor selection based on the mutual information are performed in each cluster, and the multiple prediction models are trained separately. A prediction model corresponding to the cluster to which the current traffic pattern belongs is selected to output the prediction result. Experimental results on real traffic data show that the proposed method achieves good prediction accuracy by distinguishing the heterogeneity of spatiotemporal correlations among the traffic flows.

Predicting network traffic is crucial for preventing congestion and gaining superior quality of network services. This research aims to use backpropagation to predict the inbound level to understand and determine internet usage. The architecture consists of one input layer, two hidden layers, and one output layer. The study compares three activation functions: sigmoid, rectified linear unit (ReLU), and hyperbolic Tangent (tanh). Three learning rates: 0.1, 0.5, and 0.9 represent low, moderate, and high rates, respectively. Based on the result, in terms of a single form of activation function, although sigmoid provides the least RMSE and MSE values, the ReLu function is more superior in learning the high traffic pattern with a learning rate of 0.9. In addition, Re-LU is more powerful to be used in the first order in terms of combination. Hence, combining a high learning rate and pure ReLU, ReLu-sigmoid, or ReLu-Tanh is more suitable and recommended to predict upper traffic utilization

Traffic monitoring agencies collect traffic data samples to estimate annual average daily traffic (AADT) at short duration count sites. The steps to estimate AADT from sample data introduce error that manifests as uncertainty in the AADT statistic and its applications. Past research suggests that the assignment of a short duration count site to a traffic pattern group (TPG), characterized by known traffic periodicities, represents a significant but poorly quantified source of error. This paper presents an approach to quantify the range of errors arising from such assignments and to mitigate these errors using a novel data-driven assignment method. The approach uses simulated 48-hour short duration counts sampled from continuous count sites with known AADT to develop a benchmark of the total error expected when AADT is estimated from such samples. Likewise, the analysis produces a set of AADT estimates using temporal factors from pre-defined TPGs to quantify the range of assignment errors. The data-driven assignment method aims to mitigate these errors by minimizing the absolute mean deviation in AADT estimates produced from multiple short duration counts in a single year. The approach is applied to traffic data collected in Manitoba, Canada, as a case study. The results indicate that the mean absolute error from 48-hour short duration counts is 6.40% of the true AADT and that improper assignment can lead to a range in mean absolute errors of 9%. When applied to previously unassigned sites, the data-driven assignment method reduced mean absolute errors from 10.32%, using a conventional assignment method, to 7.86%.

A paradigm shift towards agile and adaptive traffic signal control empowered with the massive growth of Big Data and Internet of Things (IoT) technologies is emerging rapidly for Intelligent Transportation Systems. Generally, an adaptive signal control system fine-tunes signal timing parameters based on pre-defined control hyperparameters using instantaneous traffic detection information. Once traffic pattern changes, those hyperparameters (e.g., maximum and minimum green times) need to be adjusted according to the evolution of traffic dynamics over a very short-term period. Such adjustment processes are usually conducted by professional and experienced traffic engineers. Here we present a human-in-the-loop parallel learning framework and its utilization in an end-to-end recommendation system that mimics and enhances professional signal control engineers’ behaviors. The system has been deployed into a real-world application for an extended period in Hangzhou, China, where signal control hyperparameters are recommended based on large-scale multidimensional traffic datasets. Experimental evaluations demonstrate significant improvements in traffic efficiency through the use of our signal recommendation system.

The various demands from cellular users are increasing day by day. A tower can be selected if the traffic through it at a specific time is predictable. Then a chosen set of issues can also be addressed. In the future, such prediction of traffic will help to identify networks that have better conditions with desired performance. To be specific, in this paper, the Long-Short Term Memory (LSTM) model has been proposed to predict the network traffic at a given 4G LTE cell tower. The LSTM model uses traffic data records of a network for one full year to predict its traffic pattern in a particular week. In addition to this, the 4G LTE access networks are operated via Blockchain. Each LTE tower acts as a separate node like a Peer-to-Peer network. The processes and conditions for accessing mobile networks are coded as smart contracts. Blockchain - Radio Access Network (B-RAN) establishes a secure connection between Access Point (AP) and User Equipment (UE) using smart contracts. If all the conditions of the contract code are satisfied, then data transmission takes place between the user and the AP. The proposed LSTM model on blockchain-enabled network APs to predict LTE data traffic gets validated. It is found that it makes the network encrypted, secure and improves its functioning. The superior performance of the proposed LSTM model justifies that the proposed multilayer LSTM improves the performance from 8.2% to 17.7% as compared to the Autoregressive Integrated Moving Average (ARIMA). The latter is the baseline prediction model.

The explosion of traffic brings the challenges for Internet Service Providers (ISPs) to make a profit with the high cost of infrastructure and increased competition. This calls for economic mechanisms that can enable providers to allocate on-demand resources through the prediction of traffic volumes and adjust the price. In this paper, we analyze the network traffic pattern of mobile data and make an accurate prediction of traffic volumes through ARIMA and LSTM. Based on the analysis, we then suggest a scalable price strategy for ISPs to satisfy the various requirements of customers.

In complex networks systems like mobile edge infrastructures, real-time traffic classification according to application types is an enabling technique for network resource optimization and advanced security management. State-of-the-art schemes take advantage of machine learning techniques to train classification models based on behavioral characteristics of network traffic flows. Nonetheless, most existing studies assume complete a priori information of the application classes and formulate the task as a standalone multi-class classification problem. Such classification models cannot properly handle the unknown applications that are absent from the training set during the time of training. In this work, we propose a practical mobile network traffic classification scheme that builds robust classifiers based on incomplete a priori information. Specifically, the core idea is to extract the unknown patterns emerging in the network periodically to complement the initial labeled data set that only consists of a limited number of known applications. We propose two algorithms for the unknown pattern extraction step. One is based on iterative asymmetric binary classification and the other is based on constrained clustering. Empirical results based on a public data set show that the proposed scheme can effectively detect both known and unknown applications.

Geocast routing aims the transmission of a message to a community of vehicles in the same geographical area. The GPS information of vehicles, especially taxis, in a city gives reliable information about the traffic pattern of a city. With the help of this information smart models can be created to use when making routing decisions. In this paper, a smart geocasting protocol, GeoAKOM, is introduced and implemented by using the GPS data of taxis in Bursa city, Turkey. In GeoAKOM, the mobility pattern of city is analized in two levels as in GeoMobCon introduced earlier. In the macroscopic level analysis, the city is segmented into clusters using k-means clustering and optimal paths for a message is generated using these clusters. In the microscopic level, mobility based likelihood value is used to make routing decision. The message is transmitted if the encountered vehicle has a larger mobility based likelihood or contact history based likelihood value than the carrying vehicle as in GeoMobCon. Moreover, in GeoAKOM, the next cluster of a vehicle is predicted with the help of all k-order Markov model (AKOM) by using GPS records of all vehicles. Mobility based likelihood calculations are performed by using this prediction. With these features, GeoAKOM provides a robust solution for the vehicles which do not have large GPS record history and which are relatively new in the system. Simulations on real world taxi GPS data are compared for various number of clusters in terms of delivery ratio, average delay, and hop count by using the ONE simulator. Simulation results show that, GeoAKOM provides promising results when compared with GeoMobCon and the First Contact routing algorithms.

We experimentally present a wavelength routing approach for optical networks with dynamic traffic pattern. All-optical wavelength conversion, energy efficient VCSELs and precise wavelength selectiv...

Many studies have been carried out regarding the navigation algorithms and scenarios of other and own ship related to autonomous ships. However, there was a lack of research on the selection of saf...

Realising the spatio-temporal evolutionary pattern of urban traffic can give advice about making personal trip route planning and improving road construction. A novel pattern-discovering model is presented to identify the traffic regularity and characteristics from spatial and temporal dimensions. To unveil this new method, there are two main parts as follows: first, by employing the constrained projected gradient of the non-negative matrix factorisation algorithm, the original traffic data matrix is decomposed into the feature matrix and the weight matrix. Necessary constraints are newly added so that the resulting matrices are ensured to make practical sense for reflecting the traffic spatio-temporal regular pattern. Then, the self-organising maps network is further used to cluster the factorisation error into several classes representing the disparate traffic pattern of each time. In addition, the experiment is conducted on real historical data to verify the performance of the algorithm. The global urban traffic flow for a week is summarised through a set of basic patterns with related weight distribution. The well-visualised result demonstrates that the authors method can achieve significant improvement in terms of computational efficiency and accuracy when compared with other widely-used methods.

Coded caching has been shown to result in significant throughput gains, but its gains were proved only by assuming a placement phase with no transmission cost. A free placement phase is, however, an unrealistic assumption that could stand as an obstacle towards delivering the promise of coded caching. In this letter, we focus on analyzing the effect of the placement communication cost on the overall throughput and the structure of the optimal caching scheme, under the assumptions of the worst case traffic pattern and unlimited memory at the end users. Interestingly, we find relevant network configurations where uncoded caching is the optimal solution.

Granblue Fantasy is one of Role Playing Games (RPG). It’s a video role-playing game developed by Cygames. This research to observes the Granblue Fantasy Game. The purpose is to analyze the traffic data of the Granblue Fantasy to find the pattern using Deep Packet Inspection (DPI), Capturing the Data Traffic, Feature Extraction Process and Visualize the Pattern. The Pattern are Gacha, Solo Raid, Casino and Multiraid. This research demonstrate that Multiraid battle has more data than other pattern with TTL 237.

Traffic count stations play a key role in measuring roadway characteristics and traffic performance by collecting and monitoring travel behavior and vehicle data. Continuous counting stations (CCSs), which count traffic volumes continuously throughout the year, are used to develop seasonal adjustment factors to convert short-term traffic counts (average daily traffic) to annual average daily traffic (AADT). As data collection is conducted at limited locations, many state Departments of Transportation (DOTs) group the CCSs based on different traffic patterns and estimate the AADT at specific locations by considering seasonal adjustment factors. Computer-based clustering approaches are widely used in grouping continuous traffic data for their accuracy in traffic pattern recognition. However, most of the clustering techniques do not consider the spatial constraints and therefore overlooked the locational proximity and inference from nearby traffic data. In this study, a GIS-based multivariate spatial clustering approach was developed to recognize statewide traffic patterns based on temporal and spatial variables. A total of 12 optimal clusters were automatically computed and labeled based on the proposed clustering algorithm. The proposed clustering approach was compared and validated based on machine learning classifiers. The results showed that it outperformed the traditional Michigan DOT clustering approach and was consistent in nature across different years. The model was applied to estimate the AADT, and good accuracy was detected relative to other approaches. The proposed clustering method offers a new approach to group traffic patterns by simultaneously incorporating proximity and similarity of traffic data.

Maritime traffic service networks and information systems play a vital role in maritime traffic safety management. The data collected from the maritime traffic networks are essential for the perception of traffic dynamics and predictive traffic regulation. This paper is devoted to surveying the key processing components in maritime traffic networks. Specifically, the latest progress on maritime traffic data mining technologies for maritime traffic pattern extraction and the recent effort on vessels’ motion forecasting for better situation awareness are reviewed. Through the review, we highlight that the traffic pattern knowledge presents valued insights for wide-spectrum domain application purposes, and serves as a prerequisite for the knowledge based forecasting techniques that are growing in popularity. The development of maritime traffic research in pattern mining and traffic forecasting reviewed in this paper affirms the importance of advanced maritime traffic studies and the great potential in maritime traffic safety and intelligence enhancement to accommodate the implementation of the Internet of Things, artificial intelligence technologies, and knowledge engineering and big data computing solution.

Since Photonic Networks-on-Chip (PNoCs) were proposed, there was an unanimity about the benefits that photonic links could bring to the on-chip interconnection. However, a debate always takes place regarding the suitable architecture and routing scheme to be used. This debate concerns the use of fully photonic PNoC or an Electro-assisted one. Both schemes have their pros and cons, but the main drawback in both architectures is their scalability. We propose in this paper an alternative to these two conventional PNoC architectures. Our proposed system is based on a novel Wavelength-Shifting mechanism, which combines the benefits of the previously mentioned schemes while limiting their drawbacks. The proposed system was validated by an analytical model, in addition to a set of simulations using synthetic and realistic traffic patterns. Evaluation results show that compared to the electro-assisted architectures, we could enhance the latency, power, and bandwidth by an order of magnitude, reaching a performance similar to the fully photonic architecture. In addition, the number of used photonic devices still much lower than the one used in conventional fully photonic architectures by an average of 60 percent. Furthermore, the new wavelength-shifting mechanism is highly scalable, and it is not affected anymore by the communication's distance, nor the traffic pattern, which make it a promising solution to replace existing conventional architectures.

Wireless cameras are widely deployed in surveillance systems for security guarding. However, the privacy concerns associated with unauthorized videotaping, are drawing increasing attention recently. Existing detection methods for unauthorized wireless cameras are either limited by their detection accuracy or requiring dedicated devices. In this paper, we propose DeWiCam , a lightweight and effective detection mechanism using smartphones. The basic idea of DeWiCam is to utilize the intrinsic traffic patterns of flows from wireless cameras. Compared with traditional traffic pattern analysis, DeWiCam is more challenging because it cannot access the encrypted information in the data packets. Yet, DeWiCam overcomes the difficulty and can detect nearby wireless cameras reliably. To further identify whether a camera is in an interested room, we propose a human-assisted identification model. Extension functions of DeWiCam further enable the video resolution and audio channel inference to provide extra protection. We implemented DeWiCam on the Android platform and evaluated it with extensive experiments on 20 cameras. The evaluation results show that DeWiCam can detect cameras with an accuracy of 99 percent within $2.7\;\mathrm {s}$ 2 . 7 s .

Aiming at objectively measuring the realism of virtual traffic flows and evaluating the effectiveness of different traffic simulation techniques, this paper introduces a general, dictionary-based learning method to evaluate the fidelity of any traffic trajectory data. First, a traffic pattern dictionary that characterizes common patterns of real-world traffic behavior is built offline from pre-collected ground truth traffic data. The corresponding learning error is set as the benchmark of the dictionary-based traffic representation. With the aid of the constructed dictionary, the realism of input simulated traffic flow data can be evaluated by comparing its dictionary-based reconstruction error with the dictionary error benchmark. This evaluation metric can be robustly applied to any simulated traffic flow data; in other words, it is independent of how the traffic data are generated. We demonstrated the effectiveness and robustness of this metric through many experiments on real-world traffic data and various simulated traffic data, comparisons with the state-of-the-art entropy-based similarity metric for aggregate crowd motions, and perceptual evaluation studies.

Congestion control is always an active research field for guaranteeing the Quality of Services (QoS) of datacenter networks (DCNs). However, the current end-to-end TCP design enables both senders and receivers not to directly and precisely obtain the congestion information, incurring inaccurate or non-real-time adjustments. Moreover, switches that act as fundamental primitives in DCN are usually used for reactive congestion feedbacks (e.g., Ack and ECN), leading to underused for proactive congestion control. In this article, we propose a novel active queue management (AQM) scheme called Active Buffer Queueing (ABQ) which leverages active buffer queueing of DCN switches to achieve both high-throughput and loss-free transmissions. When the traffic pattern in DCN is changed intensely in flow size, number and interval, the feedback information to TCP end hosts are imprecise. Unlike other AQMs, the key idea of ABQ is to adjust the flow rate (or packet pace) directly in the switch according to the real-time congestion state. We explain the design rationale behind ABQ and present simulation results of its performance. Finally, we discuss the implementation on the NetFPGA.

The urban road traffic network evolution is complex and varies depend on road type, zoning types and social activities. Typical traffic pattern variation of road network could be examined by considering the daily human travel activities. Thus, factor and cluster analysis is carried out. This paper is a comparative analysis of various Data Mining clustering methods for the grouping of roads based on traffic profile. The analysis was carried out using data available from 45 Automatic Traffic Recorder (ATR) sites in Newcastle, UK. Factor and cluster analysis were applied on the road traffic data so that roads could be classified, allowing diurnal traffic profiles to be assigned a group to roads with similar attributes. These groups could be classify based on road traffic characteristics. Five road classifications were found.

Real-time prediction of congestion propagation is essential for urban traffic control and guidance. In this paper, a traffic pattern knowledge graph and its associated reasoning framework are proposed to approach this issue. By training a reinforcement learning-based agent to learn relation reasoning paths, it is possible to predict congestion propagation on real-time traffic data in an efficient manner.

Prediction is an important part of the traffic management system (TMS) which supports route planning, dynamic traffic control, and information provision. We developed a multi-dimensional learning machine for predicting the traffic speed. Proposed methodology considered both historical experience and near past observation of traffic data by combining a convolutional neural network (CNN) with tensor decomposition (TD) predictor. TD based method is treated as an effective traffic predictor considering temporal-spatial neighbourhood data. However, limited by learning mechanism, such a method cannot extract historical traffic pattern from large datasets. Our main contribution is converting the traffic prediction into a tensor imputation problem, which considers the historical pattern information from CNN by constructing input tensor. Besides, multiple low-rank choosing and weighted optimization are introduced to improve the accuracy of TD-based prediction. We validated our methodology using empirical detector data of urban expressway in Shanghai. Compared with single algorithms, our method has smaller absolute and relative error.

With the increasing bandwidth requirements for people and the development of urbanization, the movement of the population in the city (especially the supercity) has an increasing influence on the traffic distribution in both space and time dimensions. The imbalanced distribution results in the regional blocking of different areas in different time periods and reduces the spectrum resource utilization in elastic optical networks. To resolve this problem, this paper proposes a tidal traffic model to formulate a kind of tidal traffic phenomenon firstly. Based on the analysis for this model, the area-aware routing and spectrum allocation algorithm that focuses on the traffic adjustment in specific functional areas is proposed. And two benchmark algorithms named min-hop k-shortest path routing algorithm and occupied-slots-as-weight k-shortest path routing algorithm are introduced. The evaluation results show that, compared to the benchmark algorithms, the proposed area-aware algorithm could reduce the blocking probability efficiently from 2% to 47% with low time complexity.

There has been an increasing interest in recent years in using clustering analysis for the identification of traffic patterns that are representative of traffic conditions in support of transportation system operations and management (TSMO); integrated corridor management; and analysis, modeling, and simulation (AMS). However, there has been limited information to support agencies in their selection of the most appropriate clustering technique(s), associated parameters, the optimal number of clusters, clustering result analysis, and selecting observations that are representative of each cluster. This paper investigates and compares the use of a number of existing clustering methods for traffic pattern identifications, considering the above. These methods include the K-means, K-prototypes, K-medoids, four variations of the Hierarchical method, and the combination of Principal Component Analysis for mixed data (PCAmix) with K-means. Among these methods, the K-prototypes and K-means with PCs produced the best results. The paper then provides recommendations regarding conducting and utilizing the results of clustering analysis.

There are increasing number of works addressing the design challenges of fast, scalable solutions for the growing number of new type of applications. Recently, many of the solutions aimed at improving processing element capabilities to speed up the execution of machine learning application domain. However, only a few works focused on the interconnection subsystem as a potential source of performance improvement. Wrapping many cores together offer excellent parallelism, but it brings other challenges (e.g., adequate interconnections). Scalable, power-aware interconnects are required to support such a growing number of processing elements, as well as modern applications. In this paper, we propose a scalable and energy efficient Network-on-Chip architecture fusing the advantages of rings as well as the 2D-mesh without using any bridge router to provide high-performance. A dynamic adaptation mechanism allows to better adapt to the application requirements. Simulation results show efficient power consumption (up to 141.3% saving for connecting 1024 cores), 2x (on average) throughput growth with better scalability (up to 1024 processing elements) compared to popular 2D-mesh while tested in multiple statistical traffic pattern scenarios., 35 pages, Accepted to Journal of Supercomputing

This study aimed to determine the applicability of using selected libraries of computing environment R to establish the coefficient of self-similarity. R environment is an analytical environment with rich functionality that is used in many research and practical works concerning data analysis and knowledge discovery. Such an issue is significant in the context of contemporary wide area computer networks and the emerging type of network infrastructure IoT. This originates directly from the new nature of IoT traffic, which also has a substantial impact on Industry 4.0. It provides built-in operations facilitating data processing. The Hurst coefficient is used to evaluate traffic behavior and analyze its character. The study analyzed two cases of IoT network traffic: high and low intensity. For different sizes of time windows, we dermined the statistical Hurst exponent and compared it with standard, smoothed, and Robinson methods. The accuracy of the methods used was evaluated. Spectral regression graphs were additionally generated for selected motion variants. The obtained results were verified by Higuchi and Aggvar methods.

Recent developments in the Internet infrastructure have enabled new classes of real-time applications that strictly require low end-to-end data delivery latency. While many radio technologies offer millisecond-grade transmission latency, guaranteeing sub-second application-level delays is still a challenging goal. Conventional delay-sensitive applications do not use reliable network transport protocols to avoid packet loss recovery delay, especially in loss-prone environments such as wireless networks. QUIC is a UDP-based transport protocol with many TCP-like features, designed to reduce connection and transport latency. With the additional support of multipath transmission and packet-level Forward Erasure Correction (FEC), QUIC has the potential to provide reliable connections for latency-sensitive applications. In this work, we introduce DIOD (\underlineD elay-based \underlineI n-\underlineO rder \underlineD ecode) --- a multipath scheduler designed to work alongside FEC to minimize MPQUIC's application-level latency. An FEC adaptation scheme is also proposed to support the schedulers in dynamic environments. Our solution is evaluated with a synthetic traffic generator and a live video streaming application in emulated networks with correlated and uncorrelated loss models. Our experimental results show its significant latency reduction, especially when the traffic pattern is similar to that of real-time applications.

Dynamic Streaming over HTTP is the main standard used for online video streaming, service that has about 1.1 billion subscribers around the world. That implies billions of streaming connections between video servers and client displays. These devices involved in the streaming connection use an Ethernet Interface Card that consumes energy. In order to reduce the energy consumption, the IEEE 802.3az Energy Efficient Ethernet has been proposed. In this study, the ethernet traffic pattern when transmitting online video content is characterized in order to analyze the efficiency of the IEEE 802.3az standard under video streaming scenarios, and to verify the convenience of activating this energy saving alternative at the network interface of billions of client devices.

Massive Machine-Type Communications (mMTC) are one of the service types supported by 5G. These are characterized by the need to serve a large number of devices that have only sporadic traffic and which have low-energy consumption. While the non-intensive nature of the traffic does not pose much strain on efficient network planning, the requirement to serve a large number of devices certainly does. With the network resources being limited and the competition between users in the cell, an important question that arises is that of admission control, so that the admitted users can be successfully served by the network. To this end, in this paper, we provide the analysis that leads to an admission control policy. We do this for two types of users in terms of their traffic intensities and channel conditions: 1) homogeneous users, and 2) heterogeneous users. We validate our results on a trace. Results show that the number of admitted users depends on the traffic pattern but not on the channel conditions. An increase in traffic intensity by 15 times can decrease the number of admitted users by 15$ times.

Large cloud service providers have built an increasing number of geo-distributed data centers (DCs) connected by WAN to host their diverse services. While we have seen a large body of work on traffic engineering of WAN, the WAN traffic characteristics of production DC networks remain not well understood. In this paper, we report on the network traffic observed in Baidu's DC network (DCN) that consists of tens of geo-distributed DCs. Baidu hosts both traditional services like Web and Computing, as well as emerging services, such as Analytics, AI, and Map. We analyze WAN traffic characteristics in Baidu's DCN from the perspectives of traffic demands, traffic communication among DCs, and traffic characteristics of diverse services. Specifically, we focus on the disparity that might exist among different types of services. We also discuss the implications of our findings for WAN traffic engineering, fabric design, and service deployment.

Road traffic prediction plays a remarkable role in intelligent transportation system managements. Most current research usually considers the two dimensions of time and space dependencies. However, merely considering the upstream and downstream information in the spatial dimension is incapable to capture sufficient spatial features. In this paper, a traffic prediction model using Spatial-Temporal information and Traffic Pattern Similarity information (STTPS) is proposed. It selects observation points with similar traffic pattern to supplement spatial information through similarity analysis and attention mechanism. Simultaneously, many influences of seasonality and super-recent factors are fully considered in the time dimension. Furthermore, a time convolution network and a gated recurrent unit are used for feature extraction. Finally, the time characteristics of variant scales is fused to obtain the prediction result. The proposed prediction is compared with the state-of-art models to demonstrate its capability in both single-step (5 min) and multi-step (1 hour) traffic forecasting.

In this study, we develop a data pipeline incorporating automated data quality checking, data imputation, and spatiotemporal visualization of large-scale traffic data to understand the changes in traffic patterns during hurricane evacuation as it unfolds. We collect large-scale Microwave Vehicle Detection System (MVDS) data during Hurricane Irma from four highways in Florida: I–75, I–95, I–4, and Florida Turnpike that served majority of the evacuation traffic. Based on an extensive analysis, we provide insights on network wide spatiotemporal evacuation traffic patterns of Hurricane Irma. Such insights will help transportation agencies recognize the utility of large-scale real-time data, previously unused, to better understand the extent and spatiotemporal distribution of evacuation traffic. To demonstrate this, we analyze the processed data to understand the influence of different spatiotemporal factors on the changes in evacuation traffic pattern. Our results show at least an 18-hour (approximately) time lag between the time of issuing an evacuation order and the time when people first started to evacuate in large numbers. Such findings have potential implications to deal with the challenges of mass evacuation in real time and allows us to develop large-scale network level evacuation traffic prediction model.

4G and 5G cellular traffic pattern recognition and prediction are key objectives for network optimization. They also are becoming of fundamental importance for the next-generation cellular network. Recognizing mobile traffic patterns and proactively knowing the user behaviors allow the operator to optimize the resource allocation. On the other hand, it is a complex problem due to the diverse set of applications behind the traffic. Most traffic prediction problems focus on capturing the dynamic of traffic and enhancing the performance. In this paper, we design a deep learning model for traffic pattern recognition and prediction of the type of arrival packet using Long Short-Term Memory (LSTM) neural networks. The mobile traffic information is collected from the Downlink Control Information (DCI) using the Amarisoft software. The learning phase of the model relies on a well-known traffic pattern simulated on Amarisoft 4G and 5G testbed.

This paper presents a self-supervised machine learning approach for cognitive reconfiguration in a Hyper-X-like flexible-bandwidth optical interconnect architecture. The proposed approach makes use of a clustering algorithm to learn the traffic patterns from historical traces. A heuristic algorithm is developed for optimizing the connectivity graph for each identified traffic pattern. Further, to mitigate the scalability issue induced by frequent clustering operations, we parameterize the learned traffic patterns by a deep neural network classifier. The classifier is trained offline by supervised learning to enable classification of traffic matrices during online operations, thereby facilitating cognitive reconfiguration decision making. Simulation results show that compared with a static all-to-all interconnection, the proposed approach can improve throughput by up to 1.76× while reducing end-to-end packet latency and flow completion time by up to 2.8× and 25×, respectively.