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.

Research on P2P Traffic Monitoring System based on DPI Technology

This paper focuses on the key technologies of P2P technology and network traffic monitoring, which focuses on AC automaton and bypass interference control technology, and on based of it, we design a new P2P traffic monitoring system. The system uses DPI and DFI recognition technology, as well as straight loss and bypass interference control technology, basically meet the recognition and control of P2P traffic. Finally, the test results show that this system recognition accuracy of P2P traffic is high, good control effect, function and performance meet the design requirements.

Two States CBR Modeling of Data Source in Dynamic Traffic Monitoring Sensor Networks

Real traffic information was analyzed in the statistical characteristics and approximated as a Gaussian time series. A data source model, called two states constant bit rate (TSCBR), was proposed in dynamic traffic monitoring sensor networks. Analysis of autocorrelation of the models shows that the proposed TSCBR model matches with the statistical characteristics of real data source closely. To further verify the validity of the TSCBR data source model, the performance metrics of power consumption and network lifetime was studied in the evaluation of sensor media access control (SMAC) algorithm. The simulation results show that compared with traditional data source models, TSCBR model can significantly improve accuracy of the algorithm evaluation.

Real-time vehicle tracking for traffic monitoring systems

A real-time vehicle tracking method is proposed for trattlC monitoring system at roau mte^cc- tions, and the vehicle tracking module consists of an initialization stage and a tracking stage. Li- cense plate location based on edge density and color analysis is used to detect the license plate re- gion for tracking initialization. In the tracking stage, covariance matching is employed to track the license plate. Genetic algorithm is used to reduce the computational cost. Real-time image tracking of multi-lane vehicles is achieved. In the experiment, test videos are recorded in advance by record- ers of actual E-police systems erage false detection rate and at several different city intersections. In the tracking module, the av- missed plates rate are 1.19%, and 1.72%, respectively.

ICT Devices: Vital Tools for Enhancing Road Traffic Monitoring

Road Traffic monitoring involves the collection of data describing the characteristic of vehicles and their movement through road networks. Such data may be used for one of these purposes such as law enforcement, congestion and incident detection and increasing road capacity. Transportation is a requirement for every nation regardless of its economy, political stability, population size and technological development. Movement of goods and people from one place to another is crucial to maintain strong economic and political ties between the various components of any given nation among nations. However, there are different modes of transportation and the most paramount one to human beings is road transportation. Due to increase in the modes of transportation, road users encounter different problems such as road blockage and incidents. Therefore there is need to monitor users incidents and to know the causes. Road traffic monitoring can be done manually or using ICT devices. This paper focuses on how the use of ICT devices can enhance road traffic monitoring. It traces the brief history of transportation;it equally discussed road traffic and safety, tools for monitoring road traffic, Intelligent Transportation Systems (ITS) use for traffic monitoring and their benefits. The result shows that the use of ICT devices in road traffic monitoring should be a Millennium Goal for all developed and developing countries because of its numerous advantages in the reduction of the intensity of traffic and other road incidents.

An Improved Histogram Equalization Method in the Traffic Monitoring Image Processing Field

To process the traffic monitoring image, a local Histogram Equalization method based on fuzzy mathematics was proposed in this paper. In this paper, firstly, we define a function to measure the similarity degree of two images. Then, a suitable Gaussian fuzzy distribution function was chose to generate a 3 × 3 matrix of influential factors. In order to reduce the artificial boundaries, we combined the 3 × 3 influential matrix with a 3 × 3 smooth filter matrix to get the final smooth-influ- ence matrix. Finally, the smooth-influence matrix was used to process the center block image. The simulation results demonstrated that the proposed method can reduce time consumption while improving the image contrast and can get satisfactory results.

A Scalable Architecture for Network Traffic Monitoring and Analysis Using Free Open Source Software

The lack of current network dynamics studies that evaluate the effects of new application and protocol deployment or long-term studies that observe the effect of incremental changes on the Internet, and the change in the overall stability of the Internet under various conditions and threats has made network monitoring challenging. A good understanding of the nature and type of network traffic is the key to solving congestion problems. In this paper we describe the architecture and implementation of a scalable network traffic moni-toring and analysis system. The gigabit interface on the monitoring system was configured to capture network traffic and the Multi Router Traffic Grapher (MRTG) and Webalizer produces graphical and detailed traffic analysis. This system is in use at the Obafemi Awolowo University, IleIfe, Nigeria;we describe how this system can be replicated in another environment.

Network traffic classification:Techniques,datasets,and challenges

In network traffic classification,it is important to understand the correlation between network traffic and its causal application,protocol,or service group,for example,in facilitating lawful interception,ensuring the quality of service,preventing application choke points,and facilitating malicious behavior identification.In this paper,we review existing network classification techniques,such as port-based identification and those based on deep packet inspection,statistical features in conjunction with machine learning,and deep learning algorithms.We also explain the implementations,advantages,and limitations associated with these techniques.Our review also extends to publicly available datasets used in the literature.Finally,we discuss existing and emerging challenges,as well as future research directions.

Waterbomb-origami inspired triboelectric nanogenerator for smart pavement-integrated traffic monitoring

With a rapid development of intelligent transportation systems(ITSs),traffic monitoring has gained increasing attention.Here,we present a new kind of waterbomb-origami-inspired triboelectric nanogenerator(WO-TENG)as a traffic monitoring system to integrate smart pavement with lightweight,cost-effective,excellent deformability,flexibility,and self-rebounding properties.The electrical performance is significantly improved by more than 67%compared with current origami-based TENG,and multi tribo-pairs have great synchronicity.The fully-packaged self-driven WO-TENG is further developed to integrate smart pavement,which can successfully decouple the influence of vehicle speed and weight on the sensing accuracy.This phenomenon demonstrates the feasibility and stability of the WO-TENG for traffic monitoring.Independently of the voltage amplitude and time interval electrical wave,vehicle speed,number of vehicles,and types of vehicles can be further evaluated accurately.This work can not only address the challenge of traditional traffic monitoring system,but also promote the development of TENG based self-powered sensors in ITSs.

Study on Monitoring and Analysis of TSP Caused by Road Traffic in Binzhou City

Wind speed,temperature,relative humidity and TSP concentration at three intersections in Binzhou City were monitored,and the relationships of TSP concentration with wind speed,temperature,relative humidity and traffic flow at the three intersections in Binzhou City were analyzed by using SPSS.The results show that traffic flow was the main factor affecting TSP concentration of road traffic in Binzhou City.

On-line Popularity Monitoring Method Based on Bloom Filters and Hash tables for Differentiated Traffic

Towards line speed and accurateness on-line content popularity monitoring on Content Centric Networking(CCN) routers, we propose a three-stage scheme based on Bloom filters and hash tables for differentiated traffic. At the first stage, we decide whether to deliver the content to the next stage depending on traffic types. The second stage consisting of Standard Bloom filters(SBF) and Counting Bloom filters(CBF) identifies the popular content. Meanwhile, a scalable sliding time window based monitoring scheme for different traffic types is proposed to implement frequent and real-time updates by the change of popularities. Hash tables according with sliding window are used to record the popularity at the third stage. Simulation results reveal that this method reaches a 40 Gbps processing speed at lower error probability with less memory, and it is more sensitive to the change of popularity. Additionally, the architecture which can be implemented in CCN router is flexible and scalable.

基于改进DV-HOP的道路交通拥堵传感节点快速监测

由于现有交通拥堵监控设备在海量三维交通信息中处理数据的能力较差,导致监测目标延迟较高,故设计一种基于改进DV-HOP的道路交通拥堵传感节点快速监测方法;在待监测区域安置无线视觉传感器,划分子节点与Sink节点,采集车辆通行状况和整体长度;把异常道路数据作为小概率事件,确立速率采集周期及交通状态采集周期,推算历史车辆速率均值和交通数据方差,设定拥堵临界值,分析路段是否产生拥堵;对道路交通拥堵节点进行初始化,确定全部道路交通拥堵节点,并将其转换到二维坐标中;利用改进DV-HOP算法获取道路交通拥堵节点位置信息,得出道路交通拥堵节点监测结果;实验分析表明:设计方法的均等系数值可达0.998,数据传输延时仅为3.5 s,表明交通拥堵监测精度较高。

基于生成对抗网络的路况监控视频能见度检测方法

传统路况监控视频能见度检测方法直接对人眼可分辨最远点进行检测,未进行车道线检测及兴趣域提取,造成传统方法检测效果较差。对此,提出基于生成对抗网络的路况监控视频能见度检测方法。在车道线检测及兴趣域提取的基础上,基于生成对抗网络检测人眼可分辨最远点,进一步与计算机视觉和图形领域相结合,实现路况监控视频能见度检测。实验结果表明,该方法具有更好的检测效果,可推广应用。

Analysis of the Application of Artificial Intelligence in Transportation

With the advancement of the information age,the transportation industry has experienced rapid growth,leading to an expansion in the scale and number of highway constructions.However,this development has also given rise to numerous traffic issues,including frequent vehicle congestion and traffic accidents.To address these problems,it is essential to leverage modern technology for real-time information collection and analysis,providing robust technical support for intelligent transportation systems.This paper focuses on artificial intelligence(AI)technology,explaining its concept and its role in intelligent transportation.It reviews the various application areas and analyzes the use of AI in intelligent transportation.Finally,it proposes strategies for applying AI to promote the healthy development of intelligent transportation systems.

流量在线监测综合测试管理平台应用研究

为提高河道流量测验精度,结合水文水情现状及发展需求,对河道流量在线监测综合测试的3方面关键技术进行了研究。一是探索符合水文特点的河道流量测验方法和测流传感器;二是制定ADCP和雷达波流量在线监测数据传输协议及数据库表结构;三是开发流量在线监测综合测试管理平台。研究结果表明,平台实现了对“分散”、“偏远”的无人值守ADCP、雷达波流量在线监测站进行统一管理与远程控制,实现设备厂家在流量在线监测领域互相兼容,便于系统建设及运行管理,有利于统一和规范流量在线监测市场,优化水文水资源监测方案,助力水文对传统水文站进行自动化、信息化、智能化改造。

基于改进YOLOX的轻量化交通监控目标检测算法

交通目标检测技术是道路交通管理部门进行交通流量监测、安全管控等核心工作的重要技术之一。面对大量的交通监控视频数据,需要使用检测速度更快、精度更高、占用计算资源更少的交通目标检测技术。为了满足这一需求,根据YOLOX算法和PP-LCNet网络,提出了一种面向交通监控场景的轻量型交通目标检测算法PL-YOLO。使用基于PP-LCNet改进的网络作为目标检测器的主干特征网络,使用深度可分离卷积代替YOLOX中的普通卷积,降低模型运算过程中的复杂度;根据交通监控场景下的车辆分布密集且尺寸小的特点,添加SimAM注意力机制模块,聚焦于更有意义的特征图像。实验结果表明,相对于YOLOX-s模型,改进后的PL-YOLO检测精度提升1.89个百分点,模型大小降低了54%,FPS从20.88帧/s提升到26.68帧/s。

IPv6中一种基于卷积的DDoS攻击两阶段防御机制

针对IPv6快速普及背景下分布式拒绝服务(DDoS)攻击威胁不断增长的现状,提出一种两阶段的DDoS攻击防御机制,包括初期实时监控DDoS攻击发生的预检测阶段,以及告警后精准过滤DDoS攻击流量的深度检测阶段.首先,分析IPv6报文格式并解析PCAP流量捕获文件中的16进制头部字段作为样本元素.其次,在预检测阶段,引入轻量化二值卷积神经网络(BCNN),设计一种二维流量矩阵作为模型输入,整体感知网络在混杂DDoS流量后出现的恶意态势作为告警DDoS发生的证据.告警后,深度检测阶段介入,引入一维卷积神经网络(1DCNN)具体区分混杂的DDoS报文,从而下发阻断策略.在实验中,自建IPv6-LAN拓扑并基于NAT 4to6技术重放CIC-DDoS2019公开集生成纯IPv6-DDoS流量源测试.结果证明,所提机制提升针对DDoS攻击的响应速度、准确度和攻击流量过滤效率,当DDoS流量出现仅占总网络6%和10%时,BCNN就能以90.9%和96.4%的准确度感知到DDoS攻击的发生,同时1DCNN能够以99.4%准确率区分DDoS报文并过滤.

Characterizing Internet Backbone Traffic Based on Deep Packets Inpection and Deep Flows Inspection

Based on the massive data collected with a passive network monitoring equipment placed in China＇s backbone, we present a deep insight into the network backbone traffic and evaluate various ways for inproving traffic classifying efficiency in this pa- per. In particular, the study has scrutinized the net- work traffic in terms of protocol types and signatures, flow length, and port distffoution, from which mean- ingful and interesting insights on the current Intemet of China from the perspective of both the packet and flow levels are derived. We show that the classifica- tion efficiency can be greatly irrproved by using the information of preferred ports of the network applica- tions. Quantitatively, we find two traffic duration thresholds, with which 40% of TCP flows and 70% of UDP flows can be excluded from classification pro- cessing while the in^act on classification accuracy is trivial, i.e., the classification accuracy can still reach a high level by saving 85% of the resources.

增强的Zeek网络流量采集与监控分析系统设计

随着计算机技术和网络攻击手段的不断发展,网络监控需求不断强烈。针对企事业单位网络监控效果与实际需求不匹配、缺少可复用的流量采集与监控分析一体化系统的现状,该文设计了增强的Zeek的网络流量采集与监控分析系统,用于企事业单位的流量管理。系统利用Zeek的可扩展性,设计了多端口识别与自定义采集时间间隔的功能,实现了对网络汇聚流量的更精准和灵活的采集。接着,将采集数据的本地存储与持久化存储相结合,在Web端提供对网络安全数据的全面分析。系统实现了流量数据的定制化采集、持久化存储与Web交互展示和控制功能,在保证现有应用系统平稳运行的前提下,降低了信息时延,满足了真实大规模网络环境流量数据的个性化采集和实时监测与溯源分析需求,同时为进一步扩展为其他应用模式提供了可用的架构基础。

城市道路交通运行态势监控诱导系统设计研究

城市道路交通运行态势监控诱导系统,是基于对交通运行状况的实时监测,在交通诱导屏向广大交通参与者精准发布路况信息,诱导出行者选择适宜出行路线的智能交通系统。该系统还可为交通规划部门提供准确和实时的交通数据,帮助优化道路网络布局和交通设施建设,提高道路资源的利用效率,从根源上提升公安交通管理的科技信息化应用水平。