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.

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.

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.

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.

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。

面向LoRa自组网的交通监控系统设计

为将LoRa无线通信技术应用在交通监控系统中,以提高交通监控系的灵活性和智能化,设计一种面向中心节点的自组网系统。该组网协议主要包括网络管理和数据上报2部分。网络管理实现节点入网、节点被动退网主动退网。为保证通信的时效性,数据上报环节设置多个通信信道。同时根据入网顺序,配置每个节点的数据上报窗口,保证数据的有序传输。对于硬件系统,采用STM32F103系列单片机作为主控,并利用SX1278芯片设计其对应的LoRa通信电路。最后通过实验对该系统进行测试,测试数据表明该系统可以灵活地对车辆检测节点进行自适应组网,而且通信时延满足通信模型要求,系统稳定性较高。

公路交通信息采集及监测系统的设计与实现

近些年,随着我国城市化进程的不断加速,我国公共交通道路事业得到了快速发展。在公共交通道路的发展过程中,信息采集和监测系统是整体公路交通体系的重要组成部分之一。根据相关工作经验对我国公路交通信息采集和监测系统的设计工作提出了相关的设计意见,通过分析公共交通采集的重要性,希望能够对我国公路交通的全面监测工作进行有效管理,从而为该领域的研究提供一定的帮助和借鉴。

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.

基于矩阵分解的电力监控系统局域网异常流量检测方法

当前的电力监控系统在各安全区的横向和纵向边界上部署了严密的安全防护策略。然而,在边界内部,局域网内各设备间的网络流量限制较少,局域网内部存在设备间的安全威胁。为了实现电力监控系统中网络内部安全事件的检测,本文提出了一种基于矩阵分解的电力监控系统内部恶意流量检测方案,通过对网络内部历史流量的采集和训练,得到正常流量模型,并以白名单的形式对网络内部流量实时检测。实验证明本文提出的检测方案具有对未知异常流量的检测能力和模型自适应迭代特点。

空管系统气象设备集中监控系统设计和实施方案

管系统气象设备作为保障航空安全的重要基础设施,其集中监控系统的设计和实施成为当务之急,已经成为当前我国航空气象领域的研究热点。基于此,首先对空管系统气象设备集中监控系统的存在的重要性进行了分析,之后分别从设计目标、框架、功能、硬件设备、软件系统以及数据传输和存储设计等方面对空管系统气象设备集中监控系统的设计进行了概述,最后基于“总体规划、分步实施、逐步推广”的原则对空管系统气象设备集中监控系统的实施进行了介绍,旨在实现空管系统气象设备集中监控系统的稳定、可靠、高效运行,为我国空管系统的气象监测和预警提供有力的支持。

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.

Large-Scale Traffic Characterization of Chinese Multimedia Messaging Service Using Hadoop

Recently, there have been many mo- bile value-added services in the Chinese mo- bile telecommunication market nowadays. Am- ong them, the characteristics of Multimedia Mes- saging Service （MMS） have not yet been fully understood. In this paper, with the help of a cloud computing platform, we investigated the flow-level charactefistcs of Chinese MMS. All of the experimental data were collected by the TMS equipment deployed in a major node in Sou- them China. The collection time spanned six mo- nths. We performed high-level analysis to show the basic distributions of MMS characteristics. Then, by analysing the detailed MMS features, we determined the distribution of personal MMS, and made a comprehensive comparison between 2G and 3G MMS. Finally, we tried to build a model on the personal MMS inter-arrival time, and we found that the Weibull distribution was optimum.

Design of a d_(33)-mode piezocomposite electricity generating element and its application to bridge monitoring

The piezoelectric effect is used in sensing applications such as in force and displacement sensors.However,the brittleness and low performance of piezoceramic lead zirconate titanate(PZT) often impede its applicability in civil structures which are subjected to large loads.The concept of a piezocomposite electricity generating element(PCGE) has been proposed for improving the electricity generation performance and overcoming the brittleness of piezoceramic wafers.The post-curing residual stress in the PZT layer constitutes a main reason for the PCGE's enhanced performance,and the outer epoxy-based composites protect the brittle PZT layer.A d33-mode PCGE designed for bridge monitoring application was inserted in a bridge bearing to provide a permanent and simple weigh-in-motion system.The designed PCGEs were tested through a series of tests including fatigue and dynamic tests to verify their applicability for monitoring purposes in a bridge structure.A simple beam example was presented to show the applicability of the proposed bridge bearing equipped with the PCGE for adequately measuring the traffic loads.

基于SNMP的高铁CTC设备远近端通道监控单元设计

针对目前调度集中系统车务终端远近端通道缺少有效的监测手段问题,为进一步降低调度集中系统故障对行车安全的影响程度,深入研究并提出远近端通道监控单元设计方案。通过分析简单网络管理协议通信机制,明确在电务维护终端增加监控单元,主要包括代理进程、管理信息库、配置工具、告警通知、状态监视等模块。电务维护终端以用户数据报文协议方式实现交换机的数据配置、信息交互及处理、异常报警等功能,并能实时显示设备状态拓扑图。目前,该方案已在合肥段管内试用,应用效果良好,满足监控界面直观化和报警信息可视化等现场运维需求,可为调度集中系统故障处理提供参考。

高速公路中的智慧监控技术应用

阐述高速公路信息化中的智慧监控系统的应用,对视频格式、视频分类方法、视频传输参数进行标准化管理,结合大数据技术实现全方位的流量分析、提供智慧出行服务。

一种容器和Zabbix的空管设备监控系统设计

随着中国民航事业的不断发展,空管设备不断更新,各系统硬件架构、网络结构也日益复杂,现有监控系统缺乏对系统底层硬件和网络的监控能力,无法有效跟踪分析各系统整体状态。因此,本文基于容器技术和Zabbix开源平台构建了集中监控系统,实现对多个运行系统的统一实时监控和故障报警,能够有效提高运维效率,降低运维人员的工作复杂度。

交通标识牌智能安全监测系统设计

为了监测不同工况下交通标识牌安全状态信息,实时掌握标识牌受载和安全性能等详细情况,提出设计一套智能安全监测系统,用于实时监测标识牌安全状态和相关数值。利用编程控制单片机结合红外变形监测技术对交通标识牌进行实时监测并预警,通过无线网络或专网远程传输实时动态数据;通过人工智能技术的深度学习相关算法处理数据,提取特征数据与安全阈进行校核输出决策结果,决策结果控制预警机构是否启动;数据融合评判标识牌安全状态,数据可视化直观反映标识牌安全状态和参数信息。该系统初步实现了对交通标识牌安全状态动态监测。

高速公路收费站拥堵预警诱导平台

交通拥堵预警和疏导管控是高速公路日常管理的重要组成部分。借助多维数据融合技术,构建以主动识别和实时监控为基础,以预警提醒和实时发布为主体的高速公路拥堵预警诱导平台,大幅提升管理的时效性与道路的通畅性。

基于物联网与SDN的智能交通管理系统设计

文章提出了一种基于物联网和软件定义网络(Software Defined Network,SDN)的智能交通管理系统的设计方案。该系统的目标是通过监控视频分析道路交通流量,动态调整交通信号灯的倒计时,以提高道路利用率。首先,提出智能交通管理系统的整体架构,包括交通信号灯、监控视频、光纤收发器、传输网络以及中心平台;其次,研究SDN在系统中的关键技术;最后,研究系统的实施方案,并讨论和分析了系统的特点和优势。分析表明,该系统架构具有一定的实用性,能为智能交通管理提供一种新思路。