A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.

Screening and reconstruction of real-time traffic data

The quality of real time traffic information is of the great importance, therefore the factors having effect on traffic characteristics are analyzed in general, and the necessities of real time data processing are summarized. The identification and reconstruction of real time traffic data are analyzed using Kalman filter equation and statistical approach. Four methods for ITS (Intelligent transportation system) detector data screening in traffic management systems are discussed in detail. Meanwhile traffic data examinations are discussed with solutions formulated through analysis, and recommendations are made for information collection and data management in future.

Traffic Data： Bluetooth Sensors vs. Crowdsourcing——A Comparative Study to Calculate Travel Time Reliability in Calgary, Alberta, Canada

The City of Calgary did a comparative study between two techniques of traffic data collection： Bluetooth sensors and crowdsourcing, for measuring travel time reliability on two goods movement corridors in Calgary, Alberta. To estimate travel time and speed, we used the output of BIuFAX sensors, which were operated by monitoring Bluetooth signals at several points along a roadway. On the other hand, TomTom historical traffic data were extracted from the TomTom Traffic Stats portal. To calculate travel time reliability, we applied the buffer index, and the planning time index recommended by FHWA （Federal Highway Administration）. The Bluetooth traffic data were presumed as the benchmark in this study. Unlike the TomTom traffic data, the data provided by the Bluetooth technology met the minimum recommended sample size requirement, although data processing was time consuming and impractical for long study periods. Our study results showed that crowdsourcing technique can be a viable alternative and provide travel time reliability estimates with a reasonable accuracy, when there are adequate numbers of records registered. However, the TomTom sample sizes in Calgary were not large enough to provide a statistically reliable method of providing travel time indices. Further researches may verify the accuracy of crowdsourcing technologies for travel time studies.

An Effective Network Traffic Data Control Using Improved Apriori Rule Mining

The increasing usage of internet requires a significant system for effective communication. To pro- vide an effective communication for the internet users, based on nature of their queries, shortest routing path is usually preferred for data forwarding. But when more number of data chooses the same path, in that case, bottleneck occurs in the traffic this leads to data loss or provides irrelevant data to the users. In this paper, a Rule Based System using Improved Apriori (RBS-IA) rule mining framework is proposed for effective monitoring of traffic occurrence over the network and control the network traffic. RBS-IA framework integrates both the traffic control and decision making system to enhance the usage of internet trendier. At first, the network traffic data are ana- lyzed and the incoming and outgoing data information is processed using apriori rule mining algorithm. After generating the set of rules, the network traffic condition is analyzed. Based on the traffic conditions, the decision rule framework is introduced which derives and assigns the set of suitable rules to the appropriate states of the network. The decision rule framework improves the effectiveness of network traffic control by updating the traffic condition states for identifying the relevant route path for packet data transmission. Experimental evaluation is conducted by extrac- ting the Dodgers loop sensor data set from UCI repository to detect the effectiveness of theproposed Rule Based System using Improved Apriori (RBS-IA) rule mining framework. Performance evaluation shows that the proposed RBS-IA rule mining framework provides significant improvement in managing the network traffic control scheme. RBS-IA rule mining framework is evaluated over the factors such as accuracy of the decision being obtained, interestingness measure and execution time.

Long-range correlation analysis of urban traffic data

This paper investigates urban traffic data by analysing the long-range correlation with detrended fluctuation analysis. Through a large number of real data collected by the travel time detection system in Beijing, the variation of flow in different time periods and intersections is studied. According to the long-range correlation in different time scales, it mainly discuss the effect of intersection location in road net, people activity customs and special traffic controls on urban traffic flow. As demonstrated by obtained results, the urban traffic flow represents three-phase characters similar to highway traffic. Moreover, compared by the two groups of data obtained before and after the special traffic restrictions （vehicles with special numbered plates only run in a special workday） enforcement, it indicates that the rules not only reduce the flow but also avoid irregular fluctuation.

Development of a tracking-based system for automated traffic data collection for roundabouts

Traffic data collection is essential for performance assessment, safety improvement and road planning. While automated traffic data collection for highways is relatively mature, that for roundabouts is more challenging due to more complex traffic scenes, data specifications and vehicle behavior. In this paper, the authors propose an automated traffic data collection system dedicated to roundabout scenes. The proposed system has mainly four steps of processing. First, camera calibration is performed for roundabout traffic scenes with a novel circle-based calibration algorithm. Second, the system uses enhanced Mixture of Gaussian algorithm with shaking removal for video segmentation, which can tolerate repeated camera displacements and background movements. Then, Kalman filtering, Kemel-based tracking and overlap-based opti- mization are employed to track vehicles while they are occluded and to derive the complete vehicle trajectories. The resulting vehicle trajectory of each individual vehicle gives the position, size, shape and speed of the vehicle at each time moment. Finally, a data mining algorithm is used to automatically extract the interested traffic data from the vehicle trajectories. The overall traffic data collection system has been implemented in software and runs on regular PC. The total processing time for a 3-hour video is currently 6 h. The automated traffic data collection system can significantly reduce cost and improve efficiency compared to manual data collection. The extracted traffic data have been compared to accurate manual measurements for 29 videos recorded on 29 different days, and an accuracy of more than 90% has been achieved.

Traffic-Aware Fuzzy Classification Model to Perform IoT Data Traffic Sourcing with the Edge Computing

The Internet of Things(IoT)has revolutionized how we interact with and gather data from our surrounding environment.IoT devices with various sensors and actuators generate vast amounts of data that can be harnessed to derive valuable insights.The rapid proliferation of Internet of Things(IoT)devices has ushered in an era of unprecedented data generation and connectivity.These IoT devices,equipped with many sensors and actuators,continuously produce vast volumes of data.However,the conventional approach of transmitting all this data to centralized cloud infrastructures for processing and analysis poses significant challenges.However,transmitting all this data to a centralized cloud infrastructure for processing and analysis can be inefficient and impractical due to bandwidth limitations,network latency,and scalability issues.This paper proposed a Self-Learning Internet Traffic Fuzzy Classifier(SLItFC)for traffic data analysis.The proposed techniques effectively utilize clustering and classification procedures to improve classification accuracy in analyzing network traffic data.SLItFC addresses the intricate task of efficiently managing and analyzing IoT data traffic at the edge.It employs a sophisticated combination of fuzzy clustering and self-learning techniques,allowing it to adapt and improve its classification accuracy over time.This adaptability is a crucial feature,given the dynamic nature of IoT environments where data patterns and traffic characteristics can evolve rapidly.With the implementation of the fuzzy classifier,the accuracy of the clustering process is improvised with the reduction of the computational time.SLItFC can reduce computational time while maintaining high classification accuracy.This efficiency is paramount in edge computing,where resource constraints demand streamlined data processing.Additionally,SLItFC’s performance advantages make it a compelling choice for organizations seeking to harness the potential of IoT data for real-time insights and decision-making.With the Self-Learning process,the SLItFC model monitors the network traffic data acquired from the IoT Devices.The Sugeno fuzzy model is implemented within the edge computing environment for improved classification accuracy.Simulation analysis stated that the proposed SLItFC achieves 94.5%classification accuracy with reduced classification time.

GraphCWGAN-GP:A Novel Data Augmenting Approach for Imbalanced Encrypted Traffic Classification

Encrypted traffic classification has become a hot issue in network security research.The class imbalance problem of traffic samples often causes the deterioration of Machine Learning based classifier performance.Although the Generative Adversarial Network(GAN)method can generate new samples by learning the feature distribution of the original samples,it is confronted with the problems of unstable training andmode collapse.To this end,a novel data augmenting approach called Graph CWGAN-GP is proposed in this paper.The traffic data is first converted into grayscale images as the input for the proposed model.Then,the minority class data is augmented with our proposed model,which is built by introducing conditional constraints and a new distance metric in typical GAN.Finally,the classical deep learning model is adopted as a classifier to classify datasets augmented by the Condition GAN(CGAN),Wasserstein GAN-Gradient Penalty(WGAN-GP)and Graph CWGAN-GP,respectively.Compared with the state-of-the-art GAN methods,the Graph CWGAN-GP cannot only control the modes of the data to be generated,but also overcome the problem of unstable training and generate more realistic and diverse samples.The experimental results show that the classification precision,recall and F1-Score of theminority class in the balanced dataset augmented in this paper have improved by more than 2.37%,3.39% and 4.57%,respectively.

Trip Purposes of Automobile Users Inference Using Multi-day Traffic Monitoring Data

Determining trip purpose is an important link to explore travel rules. In this paper,we takea utomobile users in urban areas as the research object,combine unsupervised learning and supervised learningm ethods to analyze their travel characteristics,and focus on the classification and prediction of automobileu sers’trip purposes. However,previous studies on trip purposes mainly focused on questionnaires and GPSd ata,which cannot well reflect the characteristics of automobile travel. In order to avoid the multi-dayb ehavior variability and unobservable heterogeneity of individual characteristics ignored in traditional traffic questionnaires,traffic monitoring data from the Northern District of Qingdao are used,and the K-meansc lustering method is applied to estimate the trip purposes of automobile users. Then,Adaptive Boosting(AdaBoost)and Random Forest(RF)methods are used to classify and predict trip purposes. Finally,ther esult shows:(1)the purpose of automobile users can be mainly divided into four clusters,which includeC ommuting trips,Flexible life demand travel in daytime,Evening entertainment and leisure shopping,andT axi-based trips for the first three types of purposes,respectively;(2)the Random Forest method performss ignificantly better than AdaBoost in trip purpose prediction for higher accuracy;(3)the average predictiona ccuracy of Random Forest under hyper-parameters optimization reaches96.25%,which proves the feasibilitya nd rationality of the above clustering results.

Evaluation of Arterial Signal Coordination with Commercial Connected Vehicle Data: Empirical Traffic Flow Visualization and Performance Measurement

Emerging connected vehicle (CV) data sets have recently become commercially available, enabling analysts to develop a variety of powerful performance measures without deploying any field infrastructure. This paper presents several tools using CV data to evaluate traffic progression quality along a signalized corridor. These include both performance measures for high-level analysis as well as visualizations to examine details of the coordinated operation. With the use of CV data, it is possible to assess not only the movement of traffic on the corridor but also to consider its origin-destination (O-D) path through the corridor. Results for the real-world operation of an eight-intersection signalized arterial are presented. A series of high-level performance measures are used to evaluate overall performance by time of day, with differing results by metric. Next, the details of the operation are examined with the use of two visualization tools: a cyclic time-space diagram (TSD) and an empirical platoon progression diagram (PPD). Comparing flow visualizations developed with different included O-D paths reveals several features, such as the presence of secondary and tertiary platoons on certain sections that cannot be seen when only end-to-end journeys are included. In addition, speed heat maps are generated, providing both speed performance along the corridor and locations and the extent of the queue. The proposed visualization tools portray the corridor’s performance holistically instead of combining individual signal performance metrics. The techniques exhibited in this study are compelling for identifying locations where engineering solutions such as access management or timing plan change are required. The recent progress in infrastructure-free sensing technology has significantly increased the scope of CV data-based traffic management systems, enhancing the significance of this study. The study demonstrates the utility of CV trajectory data for obtaining high-level details of the corridor performance as well as drilling down into the minute specifics.

Review of Load Balancing Mechanisms in SDN-Based Data Centers

With the continuous expansion of the data center network scale, changing network requirements, and increasing pressure on network bandwidth, the traditional network architecture can no longer meet people’s needs. The development of software defined networks has brought new opportunities and challenges to future networks. The data and control separation characteristics of SDN improve the performance of the entire network. Researchers have integrated SDN architecture into data centers to improve network resource utilization and performance. This paper first introduces the basic concepts of SDN and data center networks. Then it discusses SDN-based load balancing mechanisms for data centers from different perspectives. Finally, it summarizes and looks forward to the study on SDN-based load balancing mechanisms and its development trend.

Measuring accessibility of bus system based on multi-source traffic data

Accessibility is a representative indicator for evaluating the supply of bus system.Traditional studies have evaluated the accessibility from different aspects.Considering the interaction among land use,bus timetable arrangement and individual factors,a more holistic accessibility measurement is proposed to combine static and dynamic characteristics from multisource traffic data.The rationale of the proposed model is verified by a case study of bus system in Shenzhen,China,which is carried out to find the spatial and temporal discrepancy of service of bus system.It is found that the adjustment of bus schedule to time-varying travel demand can affect accessibility of bus system and that Land-use development,average bus speed and bus facilities all have positive effects on accessibility of bus system.These findings provide sig-nificant reference for transport planning and policy-making.The proposed model is not limited to accessibility measuring of bus system,but also applicable to other travel modes.

Real traffic-data based evaluation of vehicular traffic environment and state- of-the-art with future issues in location-centric data dissemination for VANETs

Extensive investigation has been performed in location-centric or geocast routing protocols for reliable and efficient dissemination of information in Vehicular Adhoc Networks （VANETs）. Various location-centric routing protocols have been suggested in literature for road safety ITS applications considering urban and highway traffic environment. This paper characterizes vehicular environments based on real traffic data and investigates the evolution of location-centric data dissemination. The current study is carded out with three main objectives： （i） to analyze the impact of dynamic traffic environment on the design of data dissemination techniques, （ii） to characterize location-centric data dissemination in terms of functional and qualitative behavior of protocols, properties, and strengths and weaknesses, and （iii） to find some future research directions in information dissemination based on location. Vehicular traffic environments have been classified into three categories based on physical characteristics such as speed, inter-vehicular distance, neighborhood stability, traffic volume, etc. Real traffic data is considered to analyze on-road traffic environments based on the measurement of physical parameters and weather conditions. Design issues are identified in incorporating physical parameters and weather conditions into data dissemination. Functional and qualitative characteristics of location-centric techniques are explored considering urban and highway environments. Comparative analysis of location-centric techniques is carded out for both urban and highway environments individually based on some unique and common characteristics of the environments. Finally, some future research directions are identified in the area based on the detailed investigation of traffic environments and location-centric data dissemination techniques.

Accuracy Assessment and Guidelines for Manual Traffic Counts from Pre-Recorded Video Data

Traffic count is the fundamental data source for transportation planning, management, design, and effectiveness evaluation. Recording traffic flow and counting from the recorded videos are increasingly used due to convenience, high accuracy, and cost-effectiveness. Manual counting from pre-recorded video footage can be prone to inconsistencies and errors, leading to inaccurate counts. Besides, there are no standard guidelines for collecting video data and conducting manual counts from the recorded videos. This paper aims to comprehensively assess the accuracy of manual counts from pre-recorded videos and introduces guidelines for efficiently collecting video data and conducting manual counts by trained individuals. The accuracy assessment of the manual counts was conducted based on repeated counts, and the guidelines were provided from the experience of conducting a traffic survey on forty strip mall access points in Baton Rouge, Louisiana, USA. The percentage of total error, classification error, and interval error were found to be 1.05 percent, 1.08 percent, and 1.29 percent, respectively. Besides, the percent root mean square errors (RMSE) were found to be 1.13 percent, 1.21 percent, and 1.48 percent, respectively. Guidelines were provided for selecting survey sites, instruments and timeframe, fieldwork, and manual counts for an efficient traffic data collection survey.

Energy consumption and emission mitigation prediction based on data center traffic and PUE for global data centers

With the rapid development of technologies such as big data and cloud computing,data communication and data computing in the form of exponential growth have led to a large amount of energy consumption in data centers.Globally,data centers will become the world’s largest users of energy consumption,with the ratio rising from 3%in 2017 to 4.5%in 2025.Due to its unique climate and energy-saving advantages,the high-latitude area in the Pan-Arctic region has gradually become a hotspot for data center site selection in recent years.In order to predict and analyze the future energy consumption and carbon emissions of global data centers,this paper presents a new method based on global data center traffic and power usage effectiveness(PUE)for energy consumption prediction.Firstly,global data center traffic growth is predicted based on the Cisco’s research.Secondly,the dynamic global average PUE and the high latitude PUE based on Romonet simulation model are obtained,and then global data center energy consumption with two different scenarios,the decentralized scenario and the centralized scenario,is analyzed quantitatively via the polynomial fitting method.The simulation results show that,in 2030,the global data center energy consumption and carbon emissions are reduced by about 301 billion kWh and 720 million tons CO2 in the centralized scenario compared with that of the decentralized scenario,which confirms that the establishment of data centers in the Pan-Arctic region in the future can effectively relief the climate change and energy problems.This study provides support for global energy consumption prediction,and guidance for the layout of future global data centers from the perspective of energy consumption.Moreover,it provides support of the feasibility of the integration of energy and information networks under the Global Energy Interconnection conception.

Data Traffic Reduction with Compressed Sensing in an AIoT System

To provide Artificial Intelligence(AI)services such as object detection,Internet of Things(IoT)sensor devices should be able to send a large amount of data such as images and videos.However,this inevitably causes IoT networks to be severely overloaded.In this paper,therefore,we propose a novel oneM2M-compliant Artificial Intelligence of Things(AIoT)system for reducing overall data traffic and offering object detection.It consists of some IoT sensor devices with random sampling functions controlled by a compressed sensing(CS)rate,an IoT edge gateway with CS recovery and domain transform functions related to compressed sensing,and a YOLOv5 deep learning function for object detection,and an IoT server.By analyzing the effects of compressed sensing on data traffic reduction in terms of data rate per IoT sensor device,we showed that the proposed AIoT system can reduce the overall data traffic by changing compressed sensing rates of random sampling functions in IoT sensor devices.In addition,we analyzed the effects of the compressed sensing on YOLOv5 object detection in terms of performance metrics such as recall,precision,mAP50,and mAP,and found that recall slightly decreases but precision remains almost constant even though the compressed sensing rate decreases and that mAP50 and mAP are gradually degraded according to the decreased compressed sensing rate.Consequently,if proper compressed sensing rates are chosen,the proposed AIoT system will reduce the overall data traffic without significant performance degradation of YOLOv5.

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.

Longitudinal Performance Assessment of Traffic Signal System Impacted by Long-Term Interstate Construction Diversion Using Connected Vehicle Data

Local arterials can be significantly impacted by diversions from adjacent work zones. These diversions often occur on unofficial detour routes due to guidance received on personal navigation devices. Often, these routes do not have sufficient sensing or communication equipment to obtain infrastructure-based traffic signal performance measures, so other data sources are required to identify locations being significantly affected by diversions. This paper examines the network impact caused by the start of an 18-month closure of the I-65/70 interchange (North Split), which usually serves approximately 214,000 vehicles per day in Indianapolis, IN. In anticipation of some proportion of the public diverting from official detour routes to local streets, a connected vehicle monitoring program was established to provide daily performances measures for over 100 intersections in the area without the need for vehicle sensing equipment. This study reports on 13 of the most impacted signals on an alternative arterial to identify locations and time of day where operations are most degraded, so that decision makers have quantitative information to make informed adjustments to the system. Individual vehicle movements at the studied locations are analyzed to estimate changes in volume, split failures, downstream blockage, arrivals on green, and travel times. Over 130,000 trajectories were analyzed in an 11-week period. Weekly afternoon peak period volumes increased by approximately 455%, split failures increased 3%, downstream blockage increased 10%, arrivals on green decreased 16%, and travel time increase 74%. The analysis performed in this paper will serve as a framework for any agency that wants to assess traffic signal performance at hundreds of locations with little or no existing sensing or communication infrastructure to prioritize tactical retiming and/or longer-term infrastructure investments.

船舶交通数据实时传输可靠性评估系统

为保障船舶交通数据实时传输的可靠性,设计船舶交通数据实时传输可靠性评估系统。从网络条件、节点设备、环境因素与网络管理4个主要方面出发选取评估指标,构建船舶交通数据实时传输可靠性评估指标体系;采用基于三角模糊数的模糊层次分析法计算评估指标的权重值;将各评估指标与对应的权重值形成并进行汇总,由此得到最终船舶交通数据实时传输可靠性评估结果。实验结果显示,该系统能够获取准确的评估结果,且由于不同因素造成的船舶交通数据实时传输故障发生概率下降幅度均达到50%以上,由此说明基于该系统的评估结果能够有效保障船舶交通数据实时传输的可靠性。