Effects of real-time traffic information systems on traffic performance under different network structures

The effects of real-time traffic information system(RTTIS)on traffic performance under parallel,grid and ring networks were investigated.The simulation results show that the effects of the proportion of RTTIS usage depend on the road network structures.For traffic on a parallel network,the performance of groups with and without RTTIS level is improved when the proportion of vehicles using RTTIS is greater than 0 and less than 30%,and a proportion of RTTIS usage higher than 90%would actually deteriorate the performance.For both grid and ring networks,a higher proportion of RTTIS usage always improves the performance of groups with and without RTTIS.For all three network structures,vehicles without RTTIS benefit from some proportion of RTTIS usage in a system.

Data network traffic analysis and optimization strategy of real-time power grid dynamic monitoring system for wide-frequency measurements

The application and development of a wide-area measurement system(WAMS)has enabled many applications and led to several requirements based on dynamic measurement data.Such data are transmitted as big data information flow.To ensure effective transmission of wide-frequency electrical information by the communication protocol of a WAMS,this study performs real-time traffic monitoring and analysis of the data network of a power information system,and establishes corresponding network optimization strategies to solve existing transmission problems.This study utilizes the traffic analysis results obtained using the current real-time dynamic monitoring system to design an optimization strategy,covering the optimization in three progressive levels:the underlying communication protocol,source data,and transmission process.Optimization of the system structure and scheduling optimization of data information are validated to be feasible and practical via tests.

Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.

Traffic Flow Statistics Method Based on Deep Learning and Multi-Feature Fusion

Traffic flow statistics have become a particularly important part of intelligent transportation.To solve the problems of low real-time robustness and accuracy in traffic flow statistics.In the DeepSort tracking algorithm,the Kalman filter(KF),which is only suitable for linear problems,is replaced by the extended Kalman filter(EKF),which can effectively solve nonlinear problems and integrate the Histogram of Oriented Gradient(HOG)of the target.The multi-target tracking framework was constructed with YOLO V5 target detection algorithm.An efficient and longrunning Traffic Flow Statistical framework(TFSF)is established based on the tracking framework.Virtual lines are set up to record the movement direction of vehicles to more accurate and detailed statistics of traffic flow.In order to verify the robustness and accuracy of the traffic flow statistical framework,the traffic flow in different scenes of actual road conditions was collected for verification.The experimental validation shows that the accuracy of the traffic statistics framework reaches more than 93%,and the running speed under the detection data set in this paper is 32.7FPS,which can meet the real-time requirements and has a particular significance for the development of intelligent transportation.

Real-Time Traffic Signal Timing for Urban Road Multi-Intersection

this paper develops a real-time traffic signal timing model which is to be integrated into a single intersection for urban road, thereby solving the problem of traffic congestion. We analyze the current situation of the traffic flow with release matrix firstly, and then put forward the basic models to minimize total delay time of vehicles at the intersection. The optimal real-time signal timing model (non-fixed cycle and non-fixed split) is built with the Webster split optimal model. At last, the simulated results, which are compared with conventional model, manifest the promising properties of proposed model.

Resource Efficient Hardware Implementation for Real-Time Traffic Sign Recognition

Traffic sign recognition (TSR, or Road Sign Recognition, RSR) is one of the Advanced Driver Assistance System (ADAS) devices in modern cars. To concern the most important issues, which are real-time and resource efficiency, we propose a high efficiency hardware implementation for TSR. We divide the TSR procedure into two stages, detection and recognition. In the detection stage, under the assumption that most German traffic signs have red or blue colors with circle, triangle or rectangle shapes, we use Normalized RGB color transform and Single-Pass Connected Component Labeling (CCL) to find the potential traffic signs efficiently. For Single-Pass CCL, our contribution is to eliminate the “merge-stack” operations by recording connected relations of region in the scan phase and updating the labels in the iterating phase. In the recognition stage, the Histogram of Oriented Gradient (HOG) is used to generate the descriptor of the signs, and we classify the signs with Support Vector Machine (SVM). In the HOG module, we analyze the required minimum bits under different recognition rate. The proposed method achieves 96.61% detection rate and 90.85% recognition rate while testing with the GTSDB dataset. Our hardware implementation reduces the storage of CCL and simplifies the HOG computation. Main CCL storage size is reduced by 20% comparing to the most advanced design under typical condition. By using TSMC 90 nm technology, the proposed design operates at 105 MHz clock rate and processes in 135 fps with the image size of 1360 × 800. The chip size is about 1 mm2 and the power consumption is close to 8 mW. Therefore, this work is resource efficient and achieves real-time requirement.

Statistical analysis of aggregation in freeway traffic

We restudy the master-equation approach to aggregation in freeway traffic based on the theory of birth-death process, in which the clustering behaviour in one-lane freeway traffic model is investigated. The transition probabilities for the jump processes are reconstructed by using Greenshields＇ model, and the equation of the mean size of the cluster at any time t is derived from the birth^death equation. Numerical experiments show the clustering behaviours varying with time very well.

A Statistical Analysis of China＇s Traffic Tunnel Development Data

Traffic tunnels include tunnel works for traffic and transport in the areas of railway, highway, and rail transit. With many mountains and nearly one fifth of the global population, China possesses numerous large cities and megapolises with rapidly growing economies and huge traffic demands. As a result, a great deal of railway, highway, and rail transit facilities are required in this country. In the past, the construction of these facilities mainly involved subgrade and bridge works; in recent years.

AN INTELLIGENT METHOD FOR REAL-TIME DETECTION OF DDOS ATTACK BASED ON FUZZY LOGIC

The paper puts forward a variance-time plots method based on slide-window mechanism tocalculate the Hurst parameter to detect Distribute Denial of Service(DDoS)attack in real time.Basedon fuzzy logic technology that can adjust itself dynamically under the fuzzy rules,an intelligent DDoSjudgment mechanism is designed.This new method calculates the Hurst parameter quickly and detectsDDoS attack in real time.Through comparing the detecting technologies based on statistics andfeature-packet respectively under different experiments,it is found that the new method can identifythe change of the Hurst parameter resulting from DDoS attack traffic with different intensities,andintelligently judge DDoS attack self-adaptively in real time.

Multi-objective Optimization of Multi-Agent Elevator Group Control System Based on Real-time Particle Swarm Optimization Algorithm

In order to get a globally optimized solution for the Elevator Group Control System (EGCS) scheduling problem, an algorithm with an overall optimization function is needed. In this study, Real-time Particle Swarm Optimization (RPSO) is proposed to find an optimal solution to the EGCS scheduling problem. Different traffic patterns and controller mechanisms for EGCS are analyzed. This study focuses on up-peak traffic because of its critical importance to modern office buildings. Simulation results show that EGCS based on Multi-Agent Systems (MAS) using RPSO gives good results for up-peak EGCS scheduling problem. Besides, the elevator real-time scheduling and reallocation functions are realized based on RPSO in case new information is available or the elevator becomes busy because it is unavailable or full. This study contributes a new scheduling algorithm for EGCS, and expands the application of PSO.

Optimal Deep Learning Enabled Statistical Analysis Model for Traffic Prediction

Due to the advances of intelligent transportation system(ITSs),traffic forecasting has gained significant interest as robust traffic prediction acts as an important part in different ITSs namely traffic signal control,navigation,route mapping,etc.The traffic prediction model aims to predict the traffic conditions based on the past traffic data.For more accurate traffic prediction,this study proposes an optimal deep learning-enabled statistical analysis model.This study offers the design of optimal convolutional neural network with attention long short term memory(OCNN-ALSTM)model for traffic prediction.The proposed OCNN-ALSTM technique primarily preprocesses the traffic data by the use of min-max normalization technique.Besides,OCNN-ALSTM technique was executed for classifying and predicting the traffic data in real time cases.For enhancing the predictive outcomes of the OCNN-ALSTM technique,the bird swarm algorithm(BSA)is employed to it and thereby overall efficacy of the network gets improved.The design of BSA for optimal hyperparameter tuning of the CNN-ALSTM model shows the novelty of the work.The experimental validation of the OCNNALSTM technique is performed using benchmark datasets and the results are examined under several aspects.The simulation results reported the enhanced outcomes of the OCNN-ALSTM model over the recent methods under several dimensions.

A Statistical Comparison of Traffic Measurements from the Moving versus Stationary Observer Methods

Data of traffic flow, speed and density are required for planning, designing, and modelling of traffic stream for all parts of the road system. Specialized equipments such as stationary counts are used to record volume and speed;but they are expensive, difficult to set up, and require periodic maintenance. The moving observer method was proposed in 1954 by Wardrop and Charlesworth to estimate these variables inexpensively. Basically, the observer counts the number of vehicles overtaken, the number of vehicles passed, and the number of vehicles encountered while traveling in the opposite direction. The trip time is reported for both travel directions. Additionally, the length of road segment is measured. These variables are then used in estimating speeds and volumes. In a westbound direction from Interstate Highway 30 (I-30) in the DFW area, this study examined the accuracy and feasibility of this method by comparing it with stationary observer method as the standard method for such counts. The statistical tests were used to test the accuracy. Results show that this method provides accurate volume and speed estimates when compared to the stationary method for the road segment with three lanes per direction, especially when several runs are taken.

Real-time monitoring of weld penetration quality in robotic arc welding process

It is of great significance to develop an intelligent monitoring system for weld penetration defects such as incomplete penetration and burn-through in real-time during robotic arc welding process. In this paper, robotic gas metal arc welding experiments are carried out on the mild steel test pieces with Vee-type groove. Through-the-arc sensing method is used to capture the transient values of the welding voltage and current. The raw data of the captured welding current and voltage are processed statistically, and the feature vector SIO is extracted to correlate the welding conditions to the weld penetration information. It lays foundation for intelligent monitoring of weld quality in robotic arc welding.

Statistical Multiplexing of Video Traffic and Data Traffic

This paper studies statistical multiplexing performance by input of video traffic and data traffic. The inputs have different Qos requirements such as loss and delay jitter. By applying a modified FBM model, we present methods to estimate effective bandwidth of the aggregated traffic. Simulations were performed to evaluate effective bandwidth. The comparison between the estimation and the simulation shows that the estimations can give correct data for the effective bandwidths in terms of our interests. The analysis of gain by using priority multiplexing also addresses proper Qos configuration for the inputs in order to achieve positive gains.

A YOLOv8-CE-based real-time traffic sign detection and identification method for autonomous vehicles

Traffic sign detection in real scenarios is challenging due to their complexity and small size,often preventing existing deep learning models from achieving both high accuracy and real-time performance.An improved YOLOv8 model for traffic sign detection is proposed.Firstly,by adding Coordinate Attention(CA)to the Backbone,the model gains location information,improving detection accuracy.Secondly,we also introduce EIoU to the localization function to address the ambiguity in aspect ratio descriptions by calculating the width-height difference based on CIoU.Additionally,Focal Loss is incorporated to balance sample difficulty,enhancing regression accuracy.Finally,the model,YOLOv8-CE(YOLOv8-Coordinate Attention-EIoU),is tested on the Jetson Nano,achieving real-time street scene detection and outperforming the Raspberry Pi 4B.Experimental results show that YOLOv8-CE excels in various complex scenarios,improving mAP by 2.8%over the original YOLOv8.The model size and computational effort remain similar,with the Jetson Nano achieving an inference time of 96 ms,significantly faster than the Raspberry Pi 4B.

A novel internet traffic identification approach using wavelet packet decomposition and neural network

Internet traffic classification plays an important role in network management, and many approaches have been proposed to classify different kinds of internet traffics. A novel approach was proposed to classify network applications by optimized back-propagation (BP) neural network. Particle swarm optimization (PSO) algorithm was used to optimize the BP neural network. And in order to increase the identification performance, wavelet packet decomposition (WPD) was used to extract several hidden features from the time-frequency information of network traffic. The experimental results show that the average classification accuracy of various network applications can reach 97%. Moreover, this approach optimized by BP neural network takes 50% of the training time compared with the traditional neural network.

Online Internet Traffic Identification Algorithm Based on Multistage Classifier

Internet traffic classification plays an important role in network management. Many approaches have been proposed to clas-sify different categories of Internet traffic. However, these approaches have specific us-age contexts that restrict their ability when they are applied in the current network envi-ronment. For example, the port based ap-proach cannot identify network applications with dynamic ports; the deep packet inspec-tion approach is invalid for encrypted network applications; and the statistical based approach is time-onsuming. In this paper, a novel tech-nique is proposed to classify different catego-ries of network applications. The port based, deep packet inspection based and statistical based approaches are integrated as a multi-stage classifier. The experimental results demonstrate that this approach has high rec-ognition rate which is up to 98% and good performance of real-time for traffic identifica-tion.

Modeling and Characterizing Internet Backbone Traffic

With enormous growth of the number of Internet users and appearance of new applications, characterization of Internet traffic has attracted more and more attention and has become one of the major challenging issues in telecommunication network over the past few years. In this paper, we study the network traffic pattern of the aggregate traffic and of specific application traffic, especially the popular applications such as P2P, VoIP that contribute most network traffic. Our study verified that majority Internet backbone traffic is contributed by a small portion of users and a power function can be used to approximate the contribution of each user to the overall traffic. We show that P2P applications are the dominant traffic contributor in current Internet Backbone of China. In addition, we selectively present the traffic pattern of different applications in detail.

CHRT:Clustering-Based Hybrid Re-Routing System for Traffic Congestion Avoidance

Re-routing system has become an important technology to improve traffic efficiency.The traditional re-routing schemes do not consider the dynamic characteristics of urban traffic,making the planned routes unable to cope with the changing traf-fic conditions.Based on real-time traffic information,it is challenging to dynamically re-route connected vehicles to alleviate traffic congestion.Moreover,how to obtain global traffic information while reducing communication costs and improving travel efficiency poses a challenge to the re-routing system.To deal with these challenges,this paper proposes CHRT,a clustering-based hybrid re-routing system for traffic congestion avoidance.CHRT develops a multi-layer hybrid architecture.The central server accesses the global view of traffic,and the distributed part is composed of vehicles divided into clusters to reduce latency and communication overhead.Then,a clustering-based priority mechanism is proposed,which sets priorities for clusters based on realtime traffic information to avoid secondary congestion.Furthermore,to plan the optimal routes for vehicles while alleviating global traffic congestion,this paper presents a multi-metric re-routing algorithm.Through extensive simulations based on the SUMO traffic simulator,CHRT reduces vehicle traveling time,fuel consumption,and CO2 emissions compared to other systems.In addition,CHRT globally alleviates traffic congestion and improves traffic efficiency.

Accurate P2P traffic identification based on data transfer behavior

Peer-to-Peer (P2P) technology is one of the most popular techniques nowadays, and accurate identification of P2P traffic is important for many network activities. The classification of network traffic by using port-based or payload-based analysis is becoming increasingly difficult when many applications use dynamic port numbers, masquerading techniques, and encryption to avoid detection. A novel method for P2P traffic identification is proposed in this work, and the methodology relies only on the statistics of end-point, which is a pair of destination IP address and destination port. Features of end-point behaviors are extracted and with which the Support Vector Machine classification model is built. The experimental results demonstrate that this method can classify network applications by using TCP or UDP protocol effectively. A large set of experiments has been carried over to assess the performance of this approach, and the results prove that the proposed approach has good performance both at accuracy and robustness.