Real-time energy management strategy based on predictive cruise control for hybrid electric vehicles

With the help of traffic information of the connected environment, an energy management strategy (EMS) is proposed based on preceding vehicle speed prediction, host vehicle speed planning, and dynamic programming (DP) with PI correction to improve the fuel economy of connected hybrid electric vehicles (HEVs). A conditional linear Gaussian (CLG) model for estimating the future speed of the preceding vehicle is established and trained by utilizing historical data. Based on the predicted information of the preceding vehicle and traffic light status, the speed curve of the host vehicle can ensure that the vehicle follows safety and complies with traffic rules simultaneously as planned. The real-time power allocation is composed of offline optimization results of DP and the real-time PI correction items according to the actual operation of the engine. The effectiveness of the control strategy is verified by the simulation system of HEVs in the interconnected environment established by E-COSM 2021 on the MATLAB/Simulink and CarMaker platforms.     

Applied Research of IoV in Insurance Industry

A vehicle intelligent terminal is designed to collect vehicle state information and driver behavior information based on the application of Internet of Vehicle in the insurance industry, and data of 12 risk factors affecting the traffic safety is extracted from the information. The Principal Component Analysis is improved and PCA based on the index significant priority is proposed, using which the data of risk factors is processed and 3 principal components are obtained as the pricing factor of the insurance industry.     

Anomaly detection of network traffic based on autocorrelation principle

Network anomalies caused by network attacks can significantly degrade or even terminate network services. A Real-time and reliable detection of anomalies is essential to rapid anomaly diagnosis, anomaly mitigation, and malfunction recovering. Unlike most detection methods based on the statistical analysis of the packet headers （Such as IP addresses and ports）, a new approach only using network traffic volumes is proposed to detect anomalies reliably. Our method is based on autocorrelation function to judge whether anomalies have happened. In details, the correlation coefficients of normal and anomaly data fluctuate slightly respectively, while those of the overlapped data composed of them fluctuate greatly. Experimental results on network traffic volumes transformed from 1999 DARPA intrusion evaluation data set show that this method can effectively detect network anomalies, while avoiding the high false alarms rate.     

Traffic chaos and its p rediction based on a nonlinear car-f ollowing model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car’s movement in a single lane. Traffic chaos is a promising field ,and chaos theory has been applied to identify and predict its chaotic movement . A simulated traffic flow is generated using a car-following model (GM model) , and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN (radial basis function neural network) is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent . The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short- time traffic flow series.     

Traffm chaos and its prediction based on a nonlinear car-following model

This paper discusses the dynamic behavior and its predictions for a simulated traffic flow based on the nonlinear response of a vehicle to the leading car＇s movement in a single lane. Traffic chaos is a promising field, and chaos theory has been applied to identify and predict its chaotic movement. A simulated traffic flow is generated using a car-following model（ GM model）, and the distance between two cars is investigated for its dynamic properties. A positive Lyapunov exponent confirms the existence of chaotic behavior in the GM model. A new algorithm using a RBF NN （radial basis function neural network） is proposed to predict this traffic chaos. The experiment shows that the chaotic degree and predictable degree are determined by the first Lyapunov exponent. The algorithm proposed in this paper can be generalized to recognize and predict the chaos of short-time traffic flow series     

A Framework for Agent-Based Traffic Light Control

As traffic congestion rises within large cities, intelligent traffic control is becoming increasingly important. In this filed, intelligent traffic light control has received much attention. Considering the distributed characteristic of traffic light, multi-agent system can simulate the traffic light control easily. Based on analyzing the traffic light pattern at junctions, an agent-based framework is proposed in this paper, which consists of two kinds of agents： system agent and junction agent. System agent is responsible for gathering the history information of different junction agents to preset the time for each traffic light pattern. While, junction agent controls the traffic light at a road junction by preset-observe-ponder-act cycle. The detailed procedure of pondering is also given. In the authors method, not only the history information but also the dynamic data and communication between agents are considered. People expect the proposed framework can provide a more balanced, coordinated and optimal method for the traffic light control.     

Object－Oriented Modeling of Virtual Assembly Process

Virtual assembly is a Virtual Reality (VR) based engineering application which allows engineers to evaluate, analyze, and plan the assembly of mechanical systems. To model the virtual assembly process, new methodology must be applied. Based on the idea that the virtual assembly system is an event driven system, the interactive behavior and information model is proposed to describe the dynamic process of virtual assembly. Definition of the object-oriented model of virtual assembly is put forward.     

Information service model with mobile agent supported

Mobile Agent is a kind of novel agent technology characterized by mobile,intelligent,parallel and asynchronous computing.In this paper,a new information service model that adopts mobile agent technology is introduced first.and then an experimental system DOLTRIA system that is implemented based on the model is described.The DOLTRIA system implemented by WWW framework and Java can search for relevant HTML documents on a set of Web servers,The result of experiments shows that performance improvement can be achieved by this model,and both the elapsed time and network traffic are reduced significantly.     

Optimal decision rules acquisition in incomplete and Fuzzy Decision Information System

The Incomplete and Fuzzy Decision Information System （IFDIS） with both missing data and fuzzy decisions is rather extensively applications, which is the generalizations of complete information system, incomplete information system and fuzzy decision information system. In this paper, on the base of notion of the tolerance relation and the concept of IFDIS, the rough set model of IFDIS based on tolerance relation is proposed and the properties of the models are discussed, and then the definitions of（upper- or lower-） consistent reduction are suggested and knowledge reduction based on discernibility matrix is presented. The optimal fuzzy decision rules and its acquisition algorithm are proposed. Finally we provide an example to illustrate the validity of the algorithm.     

实时交通信息服务数据组织与基于JAVA的系统实现

相对于单纯道路网络信息和历史经验交通信息,实时交通信息对于出行者出行决策具有更为重要的意义。随着实时交通信息获取手段的多样化和普及化,更富生命力的实时交通服务系统已经纳入应用日程。首先分析总结了公众对于实时动态交通信息的需求状况,将与导航和出行密切相关的实时道路交通信息分为交通事件和交通流信息两大类,对交通事件,根据其几何特征,进一步分为点事件、线事件、面事件和关系事件。基于J2ME/J2EEE软件开发架构,Post SQL与Post GIS的时空交通数据管理模式,研发了中心式的实时交通信息服务系统原型,并对典型的交通事件和交通流信息在移动终端模拟器进行了时空表达示例。     

基于交通网格的车载导航系统在eBoxII上的实现

对eBoxII用做车载导航系统的可行性进行了研究,实现了基于嵌入式开发设备eBoxII的车载导航系统.完成了系统中语音数据和海量地图数据的存储、地图显示的优化,语音同步播报、后台通信服务等关键技术.该车载终端为上海交通信息网格3.0版的移动导航终端,由后台交通网格系统提供支撑服务.大量实验数据表明,该车载终端具有较高的运行效率.     

基于手机RFSIM与北斗GIS城市实时交通动态导航平台

目前,从智能交通系统的发展（ITS）来看,国内大多数导航系统为静态导航,缺乏根据实时交通信息进行动态导航的功能.从而提出一种基于手机RFSIM与GIS技术结合的城市实时交通动态导航平台,为出行者提供实时动态的交通信息和导航服务.该系统主要包括实时交通信息采集系统,短时交通预测系统,北斗或GPS定位以及GIS实时导航系统.在已获得的相关技术的支撑下,着力解决在此导航系统中涉及到的交通信息的获取、北斗定位和实时交通动态导航中的关键技术问题,并进行可行性分析.     

基于手机定位的动态行程时间探测

动态交通信息的发布与预测是ITS的关键所在。然而由于在各种外部因素下,道路交通状态呈现出随机变化的特性使得动态交通信息的发布与预测成为一大难点。行程时间和车速的发布与预测是实时动态交通发布与预测的重要指标,也是交通流诱导系统理论研究项目的一个重要环节。所以,这就使动态行程时间的探测这一课题显得尤为重要。结合某城市的实际情况,文中探讨并实践了一种全新的基于手机定位的动态行程时间探测方法,并对其技术路线、关键问题以及难点问题进行一一论述。该方法的最大优势:城市范围内除需要安装几块探测卡以外,几乎无需任何其他投入,从而省去了利用其他技术所需要的巨额安装与维护费用,这一点对像我国这样的发展中国家具有重大的意义。另外,该技术还有安装快、报告的信息遍及整个路网,而不是仅限于预定地点的特点。     

基于时空依赖性和注意力机制的交通速度预测

交通流精准预测对保障公共安全和解决交通拥堵具有重要的意义,在城市交通规划、交通管理、交通控制等起着重要的作用.交通预测由于其受限制于城市路网并且随着时间动态变化,其中存在着空间依赖与时间依赖,是近些年来具有挑战性的课题之一.为了同时捕获到空间和时间上的依赖,提出了一个新的神经网络:基于注意力机制的时空图卷积网络(A-TGCN).TGCN网络模型用于捕获交通数据中的动态时空特性与相关性,采用注意力机制来增强每个A-TGCN层中关键节点的信息.通过在两组数据上的实验结果表明,A-TGCN在精度以及可解释性方面都有很好的表现.     

Dynamic Traffic Assignment under Uncertainty: A Distributional Robust Chance-Constrained Approach

This paper provides a chance-constrained programming approach for transportation planning and operations under uncertainty. The major contribution of this paper is to approximate a joint chance-constrained Cell Transmission Model based System Optimum Dynamic Traffic Assignment with only partial distributional information about uncertainty as a linear program which is computationally efficient. Numerical experiments have been conducted to show the performance of the proposed approach compared with other two workable approaches based on a cumulative distribution function and a sampling method. This new approach can be used as a pragmatic tool for system optimum dynamic traffic control and management.     

ATIS环境下动态多用户路径选择行为仿真研究

交通仿真模型在先进的交通系统管理、控制和优化中得到了广泛的应用,研究提出了先进的出行者信息系统ATIS的多用户路径选择仿真系统框架,根据ATIS环境下的不同出行者行为和信息属性,把出行者分为传统型TR、固定路径型PS、理性型BR、用户均衡型UE、系统最优型SO以及VMS型,运用基于仿真的DTA给出了各类出行者的路径诱导算法,开发了ATIS环境下的微观仿真系统TESS,并以杭州市中心区路网为例进行了仿真分析。精度分析表明,基于模拟的方法可以详细刻画ATIS环境下不同类型的出行者的路径选择行为;敏感性分析表明,当受导用户市场占有率为0.2左右时,整个交通系统的总效益最高。     

基于Linux的流量控制机制研究

为实现QoS,网络节点必须具有流量控制的能力。Linux从内核2.2版起就增加了对网络QoS的支持,并提供了一套强大的流量控制功能。本文首先分析了Linux的协议体系结构、数据传递机制和数据转发过程,再介绍了流量分类、队列管理、流量整形和流量监测等技术,得到了Linux下流量控制机制的原理。     

Building Quality-of-Service Monitoring Systems for Traffic Engineering and Service Management

Deployment of quality-of-service (QoS) based value-added services in IP networks necessitates the use of traffic engineering. Traffic engineering allows service providers to use the network resources efficiently, according to the different quality levels associated with the range of services they offer. Traffic engineering relies typically on monitoring data for both offline proactive and dynamic reactive approaches. Monitoring data may be used for network provisioning, dynamic resource allocation, route management, and in-service performance verification for value-added IP services. A monitoring system should scale with the network size, the network speed, and the number of customers subscribed to use value-added IP services. This paper investigates the requirements of scalable monitoring system architectures, proposes principles for designing such systems and validates these principles through the design and implementation of a scalable monitoring system for traffic engineering and QoS delivery in IP Differentiated Services networks. Methods for assessing the relative merits of such monitoring systems are proposed. Experimental assessment results prove the scalability, accuracy, and also demonstrate the benefits of the proposed monitoring system.     

基于雷视一体机的交通流数据采集系统设计

交通流数据是进行交通管理宏观决策的基础数据,交通流数据采集系统是交通管理信息化智能化的重要组成部分。随着我国交通领域的蓬勃发展,交通流量激增、高速交通拥堵、交通事故等突发事件频发,为此,设计一套实时性好、准确度高的交通流数据采集系统是十分必要的。论文基于雷视一体机开发了一套高速公路交通流数据采集系统,采用端-边-云分级传输的物联网架构,并结合了自主研发的雷视一体机,采用CNN神经网络技术提取图像信息后,在边缘计算机中通过一维数据最优估计、多传感器数据匹配、多传感器双向最优估计、多传感器目标特征融合的软件工作流程,将雷达与监控相机提取到的信息进行最优化估计,准确提取道路目标交通信息,仅将处理后的特征信息上传至云端服务器,实现交通流数据的精确、实时采集。系统试运行结果表明,该基于雷视一体机的交通流数据采集系统能够有效提高检测准确性,加强检测结果的实时性。